[Federal Register Volume 70, Number 200 (Tuesday, October 18, 2005)]
[Notices]
[Pages 60565-60575]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 05-20785]
-----------------------------------------------------------------------
NUCLEAR REGULATORY COMMISSION
Notice of Availability of Interim Staff Guidance Document for
Fuel Cycle Facilities
AGENCY: Nuclear Regulatory Commission.
ACTION: Notice of availability.
-----------------------------------------------------------------------
FOR FURTHER INFORMATION CONTACT: James Smith, Project manager,
Technical Support Group, Division of Fuel Cycle Safety and Safeguards,
Office of Nuclear Material Safety and Safeguards, U.S. Nuclear
Regulatory Commission, Washington, DC 20005-0001. Telephone: (301) 415-
6459; fax number: (301) 415-5370; e-mail: [email protected].
SUPPLEMENTARY INFORMATION:
I. Introduction
The Nuclear Regulatory Commission (NRC) continues to issue Interim
Staff Guidance (ISG) documents for fuel cycle facilities. These ISG
documents provide clarifying guidance to the NRC staff when reviewing
either a license application or a license amendment request for a fuel
cycle facility under 10 CFR Part 70. The NRC is soliciting public
comments on the attached draft ISG document, which will be considered
in the final version or subsequent revisions.
II. Summary
The purpose of this notice is to provide the public an opportunity
to review and comment on a revised draft Interim Staff Guidance
document for fuel cycle facilities. A previous version of this draft
received substantive comments; therefore, to provide the public an
opportunity to review and comment on the revised version, the document
is being re-issued in draft. FCSS-Interim Staff Guidance-10 provides
guidance to NRC staff relative to determining whether the minimum
margin of subcriticality (MoS) is sufficient to provide an adequate
assurance of subcriticality for safety to demonstrate compliance with
the performance requirements of 10 CFR 70.61(d).
[[Page 60566]]
III. Further Information
The document related to this action is available electronically at
the NRC's Electronic Reading Room at http://www.nrc.gov/reading-rm/adams.html. From this site, you can access the NRC's Agencywide
Documents Access and Management System (ADAMS), which provides text and
image files of NRC's public documents. The ADAMS ascension number for
the document related to this notice is ML052770515. If you do not have
access to ADAMS or if there are problems in accessing the document
located in ADAMS, contact the NRC Public Document Room (PDR) Reference
staff at 1-800-397-4209, 301-415-4737, or by e-mail to [email protected].
This document may also be viewed electronically on the public
computers located at the NRC's PDR, O 1 F21, One White Flint North,
11555 Rockville Pike, Rockville, MD 20852. The PDR reproduction
contractor will copy documents for a fee. Comments and questions should
be directed to the NRC contact listed above by November 17, 2005.
Comments received after this date will be considered if it is practical
to do so, but assurance of consideration cannot be given to comments
received after this date.
Dated at Rockville, Maryland this 6th day of October 2005.
For the Nuclear Regulatory Commission.
Melanie A. Galloway,
Chief, Technical Support Group, Division of Fuel Cycle Safety and
Safeguards, Office of Nuclear Material Safety and Safeguards.
Attachment--Draft FCSS Interim Staff Guidance-10, Revision 1,
``Justification for Minimum Margin of Subcriticality for Safety''
Prepared by Division of Fuel Cycle Safety and Safeguards, Office of
Nuclear Material Safety and Safeguards
Issue
Technical justification for the selection of the minimum margin of
subcriticality for safety for fuel cycle facilities, as required by 10
CFR 70.61(d)
Introduction
10 CFR 70.61(d) requires, in part, that licensees or applicants
(henceforth to be referred to as ``licensees'') demonstrate that
``under normal and credible abnormal conditions, all nuclear processes
are subcritical, including use of an approved margin of subcriticality
for safety.'' There are a variety of methods that may be used to
demonstrate subcriticality, including use of industry standards,
handbooks, hand calculations, and computer methods. Subcriticality is
assured, in part, by providing margin between actual and expected
critical conditions. This interim staff guidance (ISG), however, only
applies to margin used in those methods that rely on calculation of
keff, including deterministic and probabilistic computer
methods. The use of other methods (e.g., use of endorsed industry
standards, widely accepted handbooks, certain hand calculations),
containing varying amounts of margin, is outside the scope of this ISG.
For methods relying on calculation of Keff, margin may
be provided either in terms of limits on physical parameters of the
system (of which Keff is a function), or in terms of limits
on Keff directly, or both. For the purposes of this ISG, the
term margin of safety will be used to refer to the margin to
criticality in terms of system parameters, and the term margin of
subcriticality (MoS) will refer to the margin to criticality in terms
of Keff. A common approach to ensuring subcriticality is to
determine a maximum Keff limit below which the licensee's
calculations must fall. This limit will be referred to in this ISG as
the Upper Subcritical Limit (USL). Licensees using calculational
methods perform validation studies, in which critical experiments
similar to actual or anticipated facility calculations are chosen and
then analyzed to determine the bias and uncertainty in the bias. The
bias is a measure of the systematic differences between calculational
method results and experimental data. The uncertainty in the bias is a
measure of both the accuracy and precision of the calculations and the
uncertainty in the experimental data. A USL is then established that
includes allowances for bias and bias uncertainty as well as an
additional margin, to be referred to in this ISG as the minimum margin
of subcriticality (MMS). The MMS is variously referred to in the
nuclear industry as minimum subcritical margin, administrative margin,
and arbitrary margin, and the term MMS should be regarded as synonymous
with those terms. The term MMS will be used throughout this ISG, and
has been chosen for consistency with the rule. The MMS is an allowance
for any unknown errors or uncertainties in the method of calculating
Keff that may exist beyond those which have been accounted
for explicitly in calculating the bias and its uncertainty.
There is little guidance in the fuel facility Standard Review Plans
(SRPs) as to what constitutes sufficient technical justification for
the MMS. NUREG-1520, ``Standard Review Plan for the Review of a License
Application for a Fuel Cycle Facility,'' Section 5.4.3.4.4, states that
this margin must include, among other uncertainties, ``adequate
allowance for uncertainty in the methodology, data, and bias to assure
subcriticality.'' However, there has been almost no guidance on how to
determine an appropriate MMS. Partly due to the lack of historical
guidance, and partly due to differences between facilities' processes
and methods of calculation, there have been significantly different MMS
values approved for the various fuel cycle facilities over time. In
addition, the different ways licensees have of defining margins and
calculating Keff limits have made a consistent approach to
reviewing Keff limits difficult. Recent licensing experience
has highlighted the need for further guidance to clarify what
constitutes an acceptable justification for the MMS.
The MMS can have a substantial effect on facility operations (e.g.,
storage capacity, throughput) and there has, therefore, been
considerable recent interest in decreasing margin in Keff
below what has been licensed previously. In addition, the increasing
sophistication of computer codes and the ready availability of
computing resources means that there has been a gradual move towards
more realistic (often resulting in less conservative) modeling of
process systems. These two factors--the increasing interest in reducing
the MMS and the reduction in modeling conservatism--make technical
justification of the MMS more risk-significant than it has been in the
past. In general, consistent with a risk-informed approach to
regulation, a smaller MMS requires a more substantial technical
justification.
This ISG is only applicable to fuel enrichment and fabrication
facilities licensed under 10 CFR part 70.
Discussion
This guidance is applicable to evaluating the MMS in methods of
evaluation that rely on calculation of Keff. The
Keff value of a fissionable system depends, in general, on a
large number of physical variables. The factors that can affect the
calculated value of Keff may be broadly divided into the
following categories: (1) The geometric configuration; (2) the material
composition; and (3) the neutron distribution. The geometric form and
material composition of the system determine--together with the
underlying nuclear data (e.g., v,X(E), cross-section data)--
the spatial and energy distribution of neutrons in the system (flux and
energy spectrum). An error in the nuclear data or the
[[Page 60567]]
geometric or material modeling of these systems can produce an error in
the neutron flux and energy spectrum, and thus in the calculated value
of Keff. The bias associated with a single system is defined
as the difference between the calculated and physical values of
Keff, by the following equation:
[bgr] = kcalc - kphysical
Thus, determining the bias requires knowing both the calculated and
physical Keff values of the system. The bias associated with
a single critical experiment can be known with a high degree of
confidence, because the physical (experimental) value is known a priori
(kphysical [ap] 1). However, for calculations performed to
demonstrate subcriticality of facility processes (to be referred to as
``applications''), this is not generally the case. The bias associated
with such an application (i.e., not a known critical configuration) is
not typically known with this same high degree of confidence, because
the actual physical Keff of the system is usually not known.
In practice, the bias is determined as the average calculated
Keff for a set of experiments that cover different aspects
of the licensee's applications. The bias and its uncertainty must be
estimated by calculating the bias associated with a set of critical
experiments having geometric forms, material compositions, and neutron
spectra similar to those of the application. Because of the large
number of factors that can affect the bias, and the finite number of
critical experiments available, staff should recognize that this is
only an estimate of the true bias of the system. The experiments
analyzed cannot cover all possible combinations of conditions or
sources of error that may be present in the applications to be
evaluated. The effect on Keff of geometric, material, or
spectral differences between critical experiments and applications
cannot be known with precision. Therefore, an additional margin (MMS)
must be applied to allow for the effects of any unknown uncertainties
that may exist in the calculated value of Keff beyond those
accounted for in the calculation of the bias and its uncertainty. As
the MMS decreases, there needs to be a greater level of assurance that
the various sources of bias and uncertainty have been taken into
account, and that the bias and uncertainty are known with a high degree
of accuracy. In general, the more similar the critical experiments are
to the applications, the more confidence there is in the estimate of
the bias and the less MMS is needed.
In determining an appropriate MMS, the reviewer should consider the
specific conditions and process characteristics present at the facility
in question. However, the MMS should not be reduced below 0.02. The
nuclear cross sections are not generally known to better than ~1-2%,
and thus it is not possible to have a greater level of assurance in the
calculated results than this. Moreover, errors in the criticality codes
have been discovered over time that have produced Keff
differences of roughly this same magnitude of 1-2% (e.g., Information
Notice 2005-13, ``Potential Non-Conservative Error in Modeling
Geometric Regions in the KENO-V.a Criticality Code'').
Staff should recognize the important distinction between ensuring
that processes are safe and ensuring that they are adequately
subcritical. The value of Keff is a direct indication of the
degree of subcriticality of the system, but is not fully indicative of
the degree of safety. A system that is very subcritical (i.e., with
Keff [Lt]1) may have a small margin of safety if a small
change in a process parameter can result in criticality. An example of
this would be a UO2 powder storage vessel, which is subcritical when
dry, but may require only the addition of water for criticality.
Similarly, a system with a small MoS (i.e., with Keff [Lt]1)
may have a very large margin of safety if it cannot credibly become
critical. An example of this would be a natural uranium system in light
water, which may have a Keff value close to 1 but will never
exceed 1.
Because of this, a distinction should be made between the margin of
subcriticality and the margin of safety. Although a variety of terms
are in use in the nuclear industry, the term margin of subcriticality
will be taken to mean the difference between the actual (physical)
value of keff and the value of keff at which the
system is expected to be critical. The term margin of safety will be
taken to mean the difference between the actual value of a parameter
and the value of the parameter at which the system is expected to be
critical. The appropriate MMS depends only on the confidence that
applications calculated to be subcritical will be subcritical. It does
not depend on other aspects of the process (e.g., safety of the process
or the ability to control parameters within certain bounds) that may
need to be reviewed as part of an overall licensing review.
There are a variety of different approaches that a licensee could
choose in justifying the MMS. Some of these approaches and means of
reviewing them are described in the following sections, in no
particular preferential order. Many of these approaches consist of
qualitative arguments, and therefore there will be some degree of
subjectivity in determining the adequacy of the MMS. Because the MMS is
an allowance for unknown (or difficult to identify or quantify) errors,
the reviewer must ultimately exercise his or her best judgement in
determining whether a specific MMS is justified. Thus, the topics
listed below should be regarded as factors the reviewer should take
into consideration in exercising that judgement, rather than any kind
of prescriptive checklist.
The reviewer should also bear in mind that the licensee is not
required to use any or all of these approaches, but may choose an
approach that is applicable to its facility or a particular process
within its facility. While it may be desirable and convenient to have a
single keff limit or MMS value (and single corresponding
justification) across an entire facility, it is not necessary for this
to be the case. The MMS may be easier to justify for one process than
for another, or for a limited application versus generically for the
entire facility. The reviewer should expect to see various combinations
of these approaches, or entirely different approaches, used, depending
on the nature of the licensee's processes and methods of calculation.
Any approach used must ultimately lead to a determination that there is
adequate assurance of subcriticality.
(1) Conservatism in the Calculational Models
The margin in keff produced by the licensee's modeling
practices, together with the MMS, provide the margin between actual
conditions and expected critical conditions. In terms of the
subcriticality criterion taken from ANSI/ANS-8.17-1984 (R1997),
``Criticality Safety Criteria for the Handling, Storage, and
Transportation of LWR Fuel Outside Reactors'' (as explained in Appendix
A): MoS >= [Delta]Km + [Delta]Ksa
where [Delta]km is the MMS and [Delta]ksa is the
margin in keff due to conservative modeling of the system
(i.e., conservative values of system parameters).
Two different applications for which the sums on the right hand
side of the equation above are equal to each other are equally
subcritical. Assurance of subcriticality may thus be provided by
specifying a margin in keff ([Delta]km), or
specifying conservative modeling practices ([Delta]ksa), or
some combination thereof. This principle will be particularly useful to
the reviewer evaluating a proposed reduction in the currently approved
MMS; the review of such a reduction should prove straightforward in
cases in which the overall combination of modeling
[[Page 60568]]
conservatism and MMS has not changed. Because of this straightforward
quantitative relationship, any modeling conservatism that has not been
previously credited should be considered before examining other
factors. Cases in which the overall MoS has decreased may still be
acceptable, but would have to be justified by other means.
In evaluating justification for the MMS relying on conservatism in
the model, the reviewer should consider only that conservatism in
excess of any manufacturing tolerances, uncertainties in system
parameters, or credible process variations. That is, the conservatism
should consist of conservatism beyond the worst-case normal or abnormal
conditions, as appropriate, including allowance for any tolerances.
Examples of this added conservatism may include assuming optimum
concentration in solution processes, neglecting neutron absorbers in
structural materials, or requiring minimum reflector conditions (e.g.,
at least a 1-inch, tight-fitting reflector around process equipment).
These technical practices used to perform criticality calculations
generally result in conservatism of at least several percent in
keff. To credit this as part of the justification for the
MMS, the reviewer should have assurance that the modeling practices
described will result in a predictable and dependable amount of
conservatism in keff. In some cases, the conservatism may be process-
dependent, in which case it may be relied on as justification for the
MMS for a particular process. However, only modeling practices that
result in a global conservatism across the entire facility should be
relied on as justification for a site-wide MMS. Ensuring predictable
and dependable conservatism includes verifying that this conservatism
will be maintained over the facility lifetime, such as through the use
of license commitments or conditions.
If the licensee has a program that establishes operating limits (to
ensure that subcritical limits are not exceeded) below subcritical
limits determined in nuclear criticality safety evaluations, the margin
provided by this (optional) practice may be credited as part of the
conservatism. In such cases, the reviewer should credit only the
difference between operating and subcritical limits that exceeds any
tolerances or process variation, and should ensure that operating
limits will be maintained over the facility lifetime, through the use
of license commitments or conditions.
Some questions that the reviewer may ask in evaluating the use of
modeling conservatism as justification for the MMS include:
How much margin in keff is provided due to
conservatism in modeling practices?
How much of this margin exceeds allowance for tolerances
and process variations?
Is this margin specific to a particular process or does it
apply to all facility processes?
What provides assurance that this margin will be
maintained over the facility lifetime?
(2) Validation Methodology and Results
Assurance of subcriticality for methods that rely on the
calculation of keff requires that those methods be
appropriately validated. One of the goals of validation is to determine
the method's bias and the uncertainty in the bias. After this has been
done, an additional margin (MMS) is specified to account for any
additional uncertainties that may exist. The appropriate MMS depends,
in part, on the degree of confidence in the validation results. Having
a high degree of confidence in the bias and bias uncertainty requires
both that there be sufficient (for the statistical method used)
applicable benchmark-quality experiments and that there be a rigorous
validation methodology. If either the data or the methodology is
deficient, a high degree of confidence in the results cannot be
attained, and a larger MMS may need to be employed than would otherwise
be acceptable. Therefore, although validation and determining the MMS
are separate exercises, they are related. The more confidence one has
in the validation results, the less additional margin (MMS) is needed.
The less confidence one has in the validation results, the more MMS is
needed.
Any review of a licensing action involving the MMS should involve
examination of the licensee's validation methodology and results. While
there is no clear quantifiable relationship between the validation and
MMS (as exists with modeling conservatism), several aspects of
validation should be considered before making a qualitative
determination of the adequacy of the MMS.
There are four factors that the reviewer should consider in
evaluating the validation: (1) The similarity of benchmark experiments
to actual applications; (2) sufficiency of the data (including the
number and quality of experiments); (3) adequacy of the validation
methodology; and (4) conservatism in the calculation of the bias and
its uncertainty. These factors are discussed in more detail below.
Similarity of Benchmark Experiments
Because the bias and its uncertainty must be estimated based on
critical experiments having similar geometric form, material
composition, and neutronic behavior to specific applications, the
degree of similarity between the critical experiments and applications
is a key consideration in determining the appropriateness of the MMS.
The more closely critical experiments represent the characteristics of
applications being validated, the more confidence the reviewer has in
the estimate of the bias and the bias uncertainty for those
applications.
The reviewer must understand both the critical experiments and
applications in sufficient detail to ascertain the degree of similarity
between them. Validation reports generally contain a description of
critical experiments (including source references). The reviewer may
need to consult these references to understand the physical
characteristics of the experiments. In addition, the reviewer may need
to consult process descriptions, nuclear criticality safety
evaluations, drawings, tables, input files, or other information to
understand the physical characteristics of applications. The reviewer
must consider the full spectrum of normal and abnormal conditions that
may have to be modeled when evaluating the similarity of the benchmarks
to applications.
In evaluating the similarity of experiments to applications, the
reviewer must recognize that some parameters are more significant than
others to accurately calculate keff. The parameters that
have the greatest effect on the calculated keff of the
system are those that are most important to match when choosing
critical experiments. Because of this, there is a close relationship
between similarity of benchmarks to applications and system
sensitivity. Historically, certain parameters have been used to trend
the bias because these are the parameters that have been found to have
the greatest effect on the bias. These parameters include the
moderator-to-fuel ratio (e.g., H/U, H/X, vm/vf),
isotopic abundance (e.g., uranium-235 (235U), plutonium-239
(239Pu), or overall Pu-to-uranium ratio), and parameters
that characterize the neutron energy spectrum (e.g., energy of average
lethargy causing fission (EALF), average energy group (AEG)). Other
parameters, such as material density or overall
[[Page 60569]]
geometric shape, are generally considered to be of less importance. The
reviewer should consider all important system characteristics that can
reasonably be supposed to affect the bias. For example, the critical
experiments should include any materials that can have an appreciable
effect on the calculated keff, so that the effect due to the
cross sections of those materials is included in the bias. Furthermore,
these materials should have at least the same reactivity worth in the
experiments (which may be evidenced by having similar number densities)
as in the applications. Otherwise, the effect of any bias from the
underlying cross sections or the assumed material composition may be
masked in the applications. It is also important that the materials be
present in a statistically significant number of experiments having
similar neutron spectra to the application. Conversely, materials that
do not have an appreciable effect on the bias may be neglected and
would not have to be represented in the critical experiments.
Merely having critical experiments that are representative of
applications is the minimum acceptance criterion, and does not alone
justify having any particular value of the MMS. There are some
situations, however, in which there is an unusually high degree of
similarity between the critical experiments and applications, and in
these cases, this fact may be credited as justification for having a
smaller MMS than would otherwise be acceptable. If the critical
experiments have geometric forms, material compositions, and neutron
spectra that are nearly indistinguishable from those of the
applications, this may be justification for reducing the MMS. For
example, justification for having a small MMS for finished fuel
assemblies could include selecting critical experiments consisting of
fuel assemblies in water, where the fuel has nearly the same pellet
diameter, pellet density, cladding materials, pitch, absorber content,
enrichment, and neutron energy spectrum as the licensee's fuel. In this
case, the validation should be very specific to this type of system,
because including other types of benchmark experiments could mask
variations in the bias. Therefore, this type of justification is
generally easiest when the area of applicability (AOA) is very narrowly
defined. The reviewer should pay particular attention to abnormal
conditions. In this example, damage to the fuel or partial flooding may
significantly affect the applicability of the critical experiments.
There are several tools available to the reviewer to ascertain the
degree of similarity between critical experiments and applications.
Some of these are listed below:
1. NUREG/CR-6698, ``Guide to Validation of Nuclear Criticality
Safety Calculational Method,'' Table 2.3, contains a set of
screening criteria for determining benchmark applicability. As is
stated in the NUREG, these criteria were arrived at by consensus
among experienced nuclear criticality safety specialists and may be
considered to be conservative. The reviewer should consider
agreement on all screening criteria to be justification for
demonstrating a very high degree of benchmark similarity. (Agreement
on the most significant screening criteria for a particular system
should be considered as demonstration of an acceptable degree of
benchmark similarity.) Less conservative (i.e., broader) screening
criteria may also be acceptable, if appropriately justified.
2. Analytical methods that systematically quantify the degree of
similarity between a set of critical experiments and applications in
pair-wise fashion may be used. One example of this is the TSUNAMI
code in the SCALE 5 code package. One strength of TSUNAMI is that it
calculates an overall correlation that is a quantitative measure of
the degree of similarity between an experiment and an application.
Another strength is that this code considers all the nuclear
phenomena and underlying cross sections and weighs them by their
importance to the calculated keff (i.e., sensitivity of
keff to the data). The NRC staff currently considers a
correlation coefficient of ck >=0.95 to be indicative of
a very high degree of similarity. This is based on the staff's
experience comparing the results from TSUNAMI to those from a more
traditional screening criterion approach. Conversely, a correlation
coefficient less than 0.90 should not be used as a demonstration of
a high degree of benchmark similarity. Because of limited use of the
code to date, these observations should be considered tentative and
thus the reviewer should not use TSUNAMI as a ``black box,'' or base
conclusions of adequacy solely on its use. However, it may be used
to test a licensee's statement that there is a high degree of
similarity between experiments and applications.
3. Traditional parametric sensitivity studies may be employed to
demonstrate that keff is highly sensitive or insensitive
to a particular parameter. For example, if a 50% reduction in the
10B cross section is needed to produce a 1% change in the
system keff, then it can be concluded that the system is
highly insensitive to the boron content, in the amount present. This
is because a credible error in the 10B cross section of a
few percent will have a statistically insignificant effect on the
bias. Therefore, in the amount present, the boron content is not a
parameter that is important to match in order to conclude that there
is a high degree of similarity between benchmarks and applications.
4. Physical arguments may demonstrate that keff is
highly sensitive or insensitive to a particular parameter. For
example, the fact that oxygen and fluorine are almost transparent to
thermal neutrons (i.e., cross sections are very low) may justify why
experiments consisting of UO2F2 may be
considered similar to UO2 or UF4 applications,
provided that both experiments and applications occur in the thermal
energy range.
The reviewer should ensure that all parameters which can measurably
affect the bias are considered when assessing benchmark similarity. For
example, comparison should not be based solely on agreement in the
235U fission spectrum for systems in which the system
keff is highly sensitive to 238U fission,
10B absorption, or 1H scattering. A method such
as TSUNAMI that considers the complete set of reactions and nuclides
present can be used to rank the various system sensitivities, and to
thus determine whether it is reasonable to rely on the fission spectrum
alone in assessing the similarity of benchmarks to applications.
Some questions that the reviewer may ask in evaluating reliance on
benchmark similarity as justification for the MMS include:
Do the benchmarks cover all geometric forms, material
compositions, and neutron energy spectra expected in applications?
Are the materials present with at least the same
reactivity worth as in applications?
Do the licensee's criteria for determining whether
experiments are sufficiently similar to applications consider all
nuclear reactions and nuclides that could affect the bias?
Sufficiency of the Data
Another aspect of evaluating the selected benchmarks for a specific
MMS is ensuring that there is a sufficient number of benchmark quality
experiments to determine the bias across the entire AOA. Having a
sufficient number of benchmark-quality experiments means that: (1)
There are enough (applicable) experiments to make a statistically
meaningful calculation of the bias and its uncertainty; (2) the
experiments somewhat evenly span the entire range of all the important
parameters, without gaps requiring extrapolation or wide interpolation;
and (3) the experiments are all benchmark-quality experiments. The
number of benchmarks needed is dependent on the statistical method used
to analyze the data. For example, some methods require a minimum number
of data points to reliably determine whether the data are normally
distributed. Merely having a large number of experiments is not
[[Page 60570]]
sufficient to provide confidence in the validation result, if the
experiments are not applicable to the application. The reviewer should
particularly examine whether consideration of only the most applicable
experiments would result in a larger negative bias (and thus a lower
USL) than that determined based on the full set of experiments. The
experiments should also be sufficiently well-characterized (including
experimental parameters and their uncertainties) to be considered
benchmark experiments. They should be drawn from established sources
(such as from the International Handbook of Evaluated Criticality
Safety Benchmark Experiments (IHECSBE), laboratory reports, or peer-
reviewed journals).
Some questions that the reviewer may ask in evaluating the number
and quality of benchmark experiments as justification for the MMS
include:
Are the critical experiments chosen all high-quality
benchmarks from reliable (e.g., peer-reviewed and widely-accepted)
sources?
Are the critical experiments chosen taken from multiple
independent sources, to minimize the possibility of systematic errors?
Have the experimental uncertainties associated with the
critical experiments been provided and used in calculating the bias and
bias uncertainty?
Is the number and distribution of critical experiments
sufficient to establish trends in the bias across the entire range of
parameters?
Is the number of critical experiments commensurate with
the statistical methodology being used?
Validation Methodological Rigor
Having a sufficiently rigorous validation methodology means having
a methodology that is appropriate for the number and distribution of
critical experiments, that calculates the bias and its uncertainty
using an established statistical methodology, that accounts for any
trends in the bias, and that accounts for all apparent sources of
uncertainty in the bias (e.g., the increase in uncertainty due to
extrapolating the bias beyond the range covered by the benchmark data.)
Examples of deficiencies in the validation methodology may include: (1)
Using a statistical methodology relying on the data being normally
distributed about the mean keff to analyze data that are not
normally distributed; (2) using a linear regression fit on data that
has a non-linear dependence on a trending parameter; (3) use of a
single pooled bias when very different types of critical experiments
are being evaluated in the same validation. These deficiencies serve to
decrease confidence in the validation results and may warrant
additional margin (i.e., a larger MMS). Additional guidance on some of
the more commonly observed deficiencies is provided below.
The assumption that data is normally distributed is generally
valid, unless there is a strong trend in the data or different types of
critical experiments with different mean calculated keff
values are being combined. Tests for normality require a minimum number
of critical experiments to attain a specified confidence level
(generally 95%). If there is insufficient data to verify that the data
are normally distributed, or the data are shown to be not normally
distributed, a non-parametric technique should be used to analyze the
data.
The critical experiments chosen should provide a continuum of data
across the entire validated range, so that any variation in the bias as
a function of important system parameters may be observed. The presence
of discrete clusters of experiments having a lower calculated
keff than the set of critical experiments as a whole should
be examined closely, to determine if there is some systematic effect
common to a particular type of calculation that makes use of the
overall bias non-conservative. Because the bias can vary with system
parameters, if the licensee has combined different subsets of data
(e.g., solutions and powders, low- and high-enriched, homogeneous and
heterogeneous), the bias for the different subsets should be analyzed.
In addition, the goodness-of-fit for any function used to trend the
bias should be examined to ensure it is appropriate to the data being
analyzed.
If critical experiments do not cover the entire range of parameters
needed to cover anticipated applications, it may be necessary to extend
the AOA by making use of trends in the bias. Any extrapolation (or wide
interpolation) of the data should be done by means of an established
mathematical methodology that takes into account the functional form of
both the bias and its uncertainty. The extrapolation should not be
based on judgement alone, such as by observing that the bias is
increasing in the extrapolated range, because this may not account for
the increase in the bias uncertainty that will occur with increasing
extrapolation. The reviewer should independently confirm that the
derived bias is still valid in the extrapolated range and should ensure
that the extrapolation is not large. NUREG/CR-6698 states that critical
experiments should be added if the data must be extrapolated more than
10%. If the extrapolation is too large, new factors that could affect
the bias may be introduced as the physical phenomena in the system
change. The reviewer should not view validation as a purely
mathematical exercise, but should bear in mind the neutron physics and
underlying physical phenomena when interpreting the results.
Discarding an unusually large number of critical experiments as
outliers (i.e., more than 1-2%) should also be viewed with some
concern. Apparent outliers should not be discarded based purely upon
judgement or statistical grounds (such as causing the data to fail
tests for normality), because they could be providing valuable
information on the method's validity for a particular application. The
reviewer should verify that there are specific defensible reasons, such
as reported inconsistencies in the experimental data, for discarding
any outliers. If any of the critical experiments from a particular data
set are discarded, the reviewer should examine other experiments
included to determine whether they may be subject to the same
systematic errors. Outliers should be examined carefully especially
when they have a lower calculated keff than the other
experiments included.
NUREG-1520 states that the MoS should be large compared to the
uncertainty in the bias. The observed spread of the data about the mean
keff should be examined as an indicator of the overall
precision of the calculational method. The reviewer should ascertain
whether the statistical method of validation considers both the
observed spread in the data and the experimental and calculational
uncertainty in determining the USL. The reviewer should also evaluate
whether the observed spread in the data is consistent with the reported
uncertainty (e.g., whether X2/N [ap] 1). If the
spread in the data is larger than or comparable to the MMS, then the
reviewer should consider whether additional margin (i.e., a larger MMS)
is needed.
As a final test of the code's accuracy, the bias should be
relatively small (i.e., bias [ap] 2 percent), or else the reason for
the bias should be determined. No credit should be taken for positive
bias, because this would result in making changes in a non-conservative
direction without having a clear understanding of those changes. If the
absolute value of the bias is very large--and especially if the reason
for the large bias cannot be determined--this may indicate that the
calculational method is not very accurate, and a larger MMS may be
appropriate.
Some questions that the reviewer may ask in evaluating the rigor of
the
[[Page 60571]]
validation methodology as justification for the MMS include:
Are the results from use of the methodology consistent
with the data (e.g., normally distributed)?
Is the normality of the data confirmed prior to performing
statistical calculations? If the data does not pass the tests for
normality, is a non-parametric method used?
Does the assumed functional form of the bias represent a
good fit to the critical experiments? Is a goodness-of-fit test
performed?
Does the method determine a pooled bias across disparate
types of critical experiments, or does it consider variations in the
bias for different types of experiments? Are there discrete clusters of
experiments for which the bias appears to be non-conservative?
Has additional margin been applied to account for
extrapolation or wide interpolation? Is this done based on an
established mathematical methodology?
Have critical experiments been discarded as apparent
outliers? Is there a valid reason for doing so?
Performing an adequate code validation is not by itself sufficient
justification for any specific MMS. The reason for this is that the
validation analysis determines the bias and its uncertainty, but not
the MMS. The MMS is added after the validation has been performed to
provide added assurance of subcriticality. However, having a validation
methodology that either exceeds or falls short of accepted standards
for validation may be a basis for either reducing or increasing the
MMS.
Statistical Conservatism
In addition to having conservatism in keff due to
modeling practices, licensees may also provide conservatism in the
statistical methods used to calculate the USL. For example, NUREG/CR-
6698 states that an acceptable method for calculating the bias is to
use the single-sided tolerance limit approach with a 95/95 confidence
(i.e., 95% confidence that 95% of all future critical calculations will
lie above the USL). If the licensee decides to use the single-sided
tolerance limit approach with a 95/99.9 confidence, this would result
in a more conservative USL than with a 95/95 confidence. This would be
true of other methods for which the licensee's confidence criteria
exceeds the minimum accepted criteria. Generally, the NRC has accepted
95% confidence levels for validation results, so using more stringent
confidence levels may provide conservatism. In addition, there may be
other reasons a larger bias and/or bias uncertainty than necessary has
been used (e.g., because of the inclusion of inapplicable benchmark
experiments that have a lower calculated keff).
The reviewer may credit this conservatism towards having an
adequate MoS if: (1) The licensee demonstrates that this translates
into a specific [Delta]keff; and (2) the licensee
demonstrates that the margin will be dependably present, based on
license or other commitments.
(3) Additional Risk-Informed Considerations
Besides modeling conservatism and the validation results, other
factors may provide added assurance of subcriticality. These factors
should be considered in evaluating whether there is adequate MoS and
are discussed below.
System Sensitivity and Uncertainty
The sensitivity of keff to changes in system parameters
can be used to assess the potential effect of errors on the calculation
of keff. If the calculated keff is especially
sensitive to a given parameter, an error in that parameter could have a
correspondingly large contribution to the bias. Conversely, if
keff is very insensitive to a given parameter, then an error
may have a negligible effect on the bias. This is of particular
importance when assessing whether the chosen critical experiments are
sufficiently similar to applications to justify a small MMS.
The reviewer should not consider the sensitivity in isolation, but
should also consider the magnitude of uncertainties in the parameters.
If keff is very sensitive to a given parameter, but the
value of that parameter is known with very high accuracy (and its
variations are well-controlled), the potential contribution to the bias
may still be very small. Thus, the contribution to the bias is a
function of the product of the keff sensitivity with the
uncertainty. To illustrate this, suppose that keff is a
function of a large number of variables, x1,
x2,..., xN. Then the uncertainty in
keff may be expressed as follows, if all the individual
terms are independent:
[GRAPHIC] [TIFF OMITTED] TN18OC05.014
where the partial derivatives [part]k/[part]xi are
proportional to the sensitivity and the terms [delta]i
represent the uncertainties, or likely variations, in the parameters.
Each term in this equation then represents the contribution to the
overall uncertainty in keff.
There are several tools available to the reviewer to ascertain the
sensitivity of keff to changes in the underlying parameters.
Some of these are listed below:
1. Analytical tools that calculate the sensitivity for each
nuclide-reaction pair present in the problem may be used. One example
of this is the TSUNAMI code in the SCALE 5 code package. TSUNAMI
calculates both an integral sensitivity coefficient (i.e., summed over
all energy groups) and a sensitivity profile as a function of energy
group. The reviewer should recognize that TSUNAMI only calculates the
keff sensitivity to changes in the underlying nuclear data,
and not to other parameters that could affect the bias and should be
considered. (See section on Benchmark Similarity for caveats about
using TSUNAMI.)
2. Direct sensitivity calculations may be used, in which system
parameters are perturbed and the resulting impact on keff
determined. Perturbation of atomic number densities can also be used to
confirm the sensitivity calculated by other methods (e.g., TSUNAMI).
Such techniques are not limited to considering the effect of the
nuclear data.
There are also several sources available to the reviewer to
ascertain the uncertainty associated with the underlying parameters.
For process parameters, these sources of uncertainty may include
manufacturing tolerances, quality assurance records, and experimental
and/or measurement results. For nuclear data parameters, these sources
of uncertainty may include published data, uncertainty data distributed
with the cross section libraries, or the covariance data used in
methods such as TSUNAMI.
Some systems are inherently more sensitive to changes in the
underlying parameters than others. For example, high-enriched uranium
systems typically exhibit a greater sensitivity to changes in system
parameters (e.g., mass, moderation) than low-enriched systems. This has
been the reason that HEU (i.e., >20wt% 235U) facilities have
been licensed with larger MMS values than LEU (<=10wt% 235U)
facilities. This greater sensitivity would also be true of weapons-
grade Pu compared to low-assay mixed oxides (i.e., with a few percent
Pu/U). However, it is also true that the uncertainties associated with
measurement of the 235U cross sections are much smaller than
those associated with measurement of the 238U cross
sections. Both the greater sensitivity and smaller uncertainty would
need to be considered in evaluating whether a larger MMS is needed for
high-enriched systems.
[[Page 60572]]
Frequently, operating limits that are more conservative than safety
limits determined using keff calculations are established to
prevent those safety limits from being exceeded. For systems in which
keff is very sensitive to the system parameters, more margin
between the operating and safety limits may be needed. Systems in which
keff is very sensitive to the process parameters may need
both a larger margin between operating and safety limits and a larger
MMS. This is because the system is sensitive to any change, whether it
be caused by normal process variations or caused by unknown errors.
Because of this, the assumption is often made that the MMS is meant to
account for variations in the process or the ability to control the
process parameters. However, the MMS is meant only to allow for unknown
(or difficult to quantify) uncertainties in the calculation of
keff. The reviewer should recognize that determination of an
appropriate MMS is not dependent on the ability to control process
parameters within safety limits (although both may depend on the system
sensitivity).
Some questions that the reviewer may ask in evaluating the system
sensitivity as justification for the MMS include:
How sensitive is keff to changes in the
underlying nuclear data (e.g., cross sections)?
How sensitive is keff to changes in the
geometric form and material composition?
Are the uncertainties associated with these underlying
parameters well-known?
How does the MMS compare to the expected magnitude of
changes in keff resulting from uncertainties in these
underlying parameters?
Knowledge of the Neutron Physics
Another important consideration that may affect the appropriate MMS
is the extent to which the physical behavior of the system is known.
Fissile systems which are known to be subcritical with a high degree of
confidence do not require as much MMS as systems where subcriticality
is less certain. An example of a system known to be subcritical with
high confidence is a light-water reactor fuel assembly. The design of
these systems is such that they can only be made critical when highly
thermalized. Due to extensive analysis and reactor experience, the
flooded isolated assembly is known to be subcritical. In addition, the
thermal neutron cross sections for materials in finished reactor fuel
have been measured with a very high degree of accuracy (as opposed to
cross sections in the resonance region). Other examples of systems in
which there is independent corroborating evidence of subcriticality may
include systems consisting of very simple geometric shapes, or other
idealized situations, in which there is strong evidence that the system
is subcritical based on comparison with highly similar systems in
published sources (e.g., standards and handbooks). In these cases, the
MMS may be significantly reduced due to the fact that the calculation
of keff is not relied on alone to provide assurance of
subcriticality.
Reliance on independent knowledge that a given system is
subcritical necessarily requires that the configuration of the system
be fixed. If the configuration can change from the reference case,
there will be less knowledge about the behavior of the changed system.
For example, a finished fuel assembly is subject to strict quality
assurance checks and would not reach final processing if it were
outside of specifications. In addition, it has a form that has both
been extensively studied and is highly stable. For these reasons, there
is a great deal of certainty that this system is well-characterized and
is not subject to change. A typical solution or powder system (other
than one with a simple geometric arrangement) would not have been
studied with the same level of rigor as a finished fuel assembly. Even
if they were studied with the same level of rigor, these systems have
forms that are subject to change into forms whose neutron physics has
not been as extensively studied.
Some questions that the reviewer may ask in evaluating the
knowledge of the neutron physics as justification for the MMS include:
Is the geometric form and material composition of the
system rigid and unchanging?
Is the geometric form and material composition of the
system subject to strict quality assurance, such that tolerances have
been bounded?
Has the system been extensively studied in the nuclear
industry and shown to be subcritical (e.g., in reactor fuel studies)?
Are there other reasons besides criticality calculations
to conclude that the system will be subcritical (e.g., handbooks,
standards, published data)?
How well-known is the nuclear data (e.g., cross sections)
in the energy range of interest?
Likelihood of the Abnormal Condition
Some facilities been licensed with different sets of
keff limits for normal and abnormal conditions. Separate
keff limits for normal and abnormal conditions are
permissible but are not required. There is some likelihood that
processes calculated to be subcritical will in fact be critical, and
this likelihood increases as the MMS is reduced (though it cannot in
general be quantified). NUREG-1718, ``Standard Review Plan for the
Review of an Application for a Mixed Oxide (MOX) Fuel Fabrication
Facility,'' states that abnormal conditions should be at least unlikely
from the standpoint of the double contingency principle. Then, a
somewhat higher likelihood that a system calculated to be subcritical
is in fact critical is more permissible for abnormal conditions than
for normal conditions, because of the low likelihood of the abnormal
condition being realized. The reviewer should verify that the licensee
has defined abnormal conditions such that achieving the abnormal
condition requires at least one contingency to have occurred, that the
system will be closely monitored so that it is promptly detected, and
that it will be promptly corrected upon detection. It is also true that
there is generally more conservatism present in the abnormal case,
because the parameters that are assumed to have failed are analyzed at
their worst-case credible condition.
The increased risk associated with having a smaller MMS for
abnormal conditions should be commensurate with and offset by the low
likelihood of achieving the abnormal condition. That is, if the normal
case keff limit is judged to be acceptable, then the
abnormal case limit will also be acceptable, provided the increased
likelihood (that a system calculated to be subcritical will be
critical) is offset by the reduced likelihood of realizing the abnormal
condition because of the controls that have been established. Note that
if two or more contingencies must occur to reach a given condition,
there is no requirement to ensure that the resulting condition is
subcritical. If a single keff limit is used (i.e., no credit
for unlikelihood of the abnormal condition), then the limit must be
found acceptable to cover both normal and credible abnormal conditions.
The reviewer should always make this finding considering specific
conditions and controls in the process(es) being evaluated.
(4) Statistical Justification for the MMS
The NRC does not consider statistical justification an appropriate
basis for a specific MMS. Previously, some licensees have attempted to
justify specific MMS values based on a comparison of two statistical
methods. For example, the USLSTATS code issued with the SCALE code
package
[[Page 60573]]
contains two methods for calculating the USL: (1) the Confidence Band
with Administrative Margin approach (calculating USL-1), and (2) the
Lower Tolerance Band approach (calculating USL-2). The value of the MMS
is an input parameter to the Confidence Band approach, but is not
included explicitly in the Lower Tolerance Band approach. In this
particular justification, adequacy of the MMS is based on a comparison
of USL-1 and USL-2 (i.e., the condition that USL-1, including the
chosen MMS, is less than USL-2). However, the reviewer should not
accept this justification.
The condition that USL-1 (with the chosen MMS) is less than USL-2
is necessary, but is not sufficient, to show that an adequate MMS has
been used. These methods are both statistical methods, and a comparison
can only demonstrate whether the MMS is sufficient to bound any
statistical uncertainties included in the Lower Tolerance Band approach
but not included in the Confidence Band approach. There may be other
statistical or systematic errors in calculating keff that
are not included in either statistical treatment. Because of this, an
MMS value should be specified regardless of the statistical method
used. Therefore, the reviewer should not consider such a statistical
approach an acceptable justification for any specific value of the MMS.
(5) Summary
Based on a review of the licensee's justification for its chosen
MMS, taking into consideration the aforementioned factors, the staff
should make a determination as to whether the chosen MMS provides
reasonable assurance of subcriticality under normal and credible
abnormal conditions. The staff's review should be risk-informed, in
that the review should be commensurate with the MoS and should consider
the specific facility and process characteristics, as well as the
specific modeling practices used. As an example, approving an MMS value
greater than 0.05 for processes typically encountered in enrichment and
fuel fabrication facilities should require only a cursory review,
provided that an acceptable validation has been performed and modeling
practices at least as conservative as those in NUREG-1520 have been
utilized. The approval of a smaller MMS will require a somewhat more
detailed review, commensurate with the MMS that is requested. However,
the MMS should not be reduced below 0.02 due to inherent uncertainties
in the cross section data and the magnitude of code errors that have
been discovered. Quantitative arguments (such as modeling conservatism)
should be used to the extent practical. However, in many instances, the
reviewer will need to make a judgement based at least partly on
qualitative arguments. The staff should document the basis for finding
the chosen MMS value to be acceptable or unacceptable in the SER, and
should ensure that any factors upon which this determination rests are
ensured to be present over the facility lifetime (e.g., through license
commitment or condition).
Regulatory Basis
In addition to complying with paragraphs (b) and (c) of this
section, the risk of nuclear criticality accidents must be limited by
assuring that under normal and credible abnormal conditions, all
nuclear processes are subcritical, including use of an approved margin
of subcriticality for safety. [10 CFR 70.61(d)]
Technical Review Guidance
Determination of an adequate MMS is strongly dependent upon
specific processes, conditions, and calculational practices at the
facility being licensed. Judgement and experience must be employed in
evaluating the adequacy of the proposed MMS. In the past, an MMS of
0.05 has generally been found acceptable for most typical low-enriched
fuel cycle facilities without a detailed technical justification. A
smaller MMS may be acceptable but will require some level of technical
review. However, for reasons stated previously, the MMS should not be
reduced below 0.02.
An MMS of 0.05 should be found acceptable for low-enriched fuel
cycle processes and facilities if:
1. A validation has been performed that meets accepted industry
guidelines (e.g., meets the requirements of ANSI/ANS-8.1-1998, NUREG/
CR-6361, and/or NUREG/CR-6698).
2. There is an acceptable number of benchmark experiments with
similar geometric forms, material compositions, and neutron energy
spectra to applications. These experiments cover the range of
parameters of applications, or else margin is provided to account for
extensions to the AOA.
3. The processes to be evaluated include materials and process
conditions similar to those that occur in low-enriched fuel cycle
applications (i.e., no new fissile materials, unusual moderators or
absorbers, or technologies new to the industry that can affect the
types of systems to be modeled).
The reviewer should consider any factors, including those
enumerated in the discussion above, that could result in applying
additional margin (i.e., a larger MMS) or may justify reducing the MMS.
The reviewer must then exercise judgement in arriving at an MMS that
provides for adequate assurance of subcriticality.
Some of the factors that may serve to justify reducing the MMS
include:
1. There is a predictable and dependable amount of conservatism in
modeling practices, in terms of keff, that is assured to be
maintained (in both normal and abnormal conditions) over the facility
lifetime.
2. Benchmark experiments have nearly identical geometric forms,
material compositions, and neutron energy spectra to applications, and
the validation is specific to this type of application.
3. The validation methodology substantially exceeds accepted
industry guidelines (e.g., it uses a very conservative statistical
approach, considers an unusually large number of trending parameters,
or analyzes the bias for a large number of subgroups of critical
experiments).
4. The system keff is demonstrably much less sensitive
to uncertainties in cross sections or variations in other system
parameters than typical low-enriched fuel cycle processes.
5. There is reliable information besides results of calculations
that provides assurance that the evaluated applications will be
subcritical (e.g., experimental data, historical evidence, industry
standards or widely-accepted handbooks).
6. The MMS is only applied to abnormal conditions, which are at
least unlikely to be achieved, based on credited controls.
Some of the factors that may necessitate increasing (or not
approving) the MMS include:
1. The technical practices employed by the licensee are less
conservative than standard industry modeling practices (e.g., do not
adequately bound reflection or the full range of credible moderation,
do not take geometric tolerances into account).
2. There are few similar critical experiments of benchmark quality
that cover the range of parameters of applications.
3. The validation methodology substantially falls below accepted
industry guidelines (e.g., it uses less than a 95% confidence in the
statistical approach, fails to consider trends in the bias, fails to
account for extensions to the AOA).
[[Page 60574]]
4. The validation results otherwise tend to cast doubt on the
accuracy of the bias and its uncertainty (i.e., the critical
experiments are not normally distributed, there is a large number of
outliers discarded (>=2%), there are distinct subgroups of experiments
with lower keff than the experiments as a whole, trending
fits do not pass goodness-of-fit tests, etc.).
5. The system keff is demonstrably much more sensitive
to uncertainties in cross sections or other system parameters than
typical low-enriched fuel cycle processes.
6. There is reliable information that casts doubt on the results of
the calculational method or the subcriticality of evaluated
applications (e.g., experimental data, reported concerns with the
nuclear data).
The purpose of asking the questions in the individual discussion
sections is to ascertain the degree to which these factors either
provide justification for reducing the MMS or necessitate increasing
the MMS. These lists are not all-inclusive, and any other technical
information that demonstrates the degree of confidence in the
calculational method should be considered.
Recommendation
The guidance in this ISG should supplement the current guidance in
the nuclear criticality safety chapters of the fuel facility SRPs
(NUREG-1520 and -1718). However, NUREG-1718, Section 6.4.3.3.4, states
that the licensee should submit justification for the MMS, but then
states that an MMS of 0.05 is ``generally considered to be acceptable
without additional justification when both the bias and its uncertainty
are determined to be negligible.'' These two statements are
inconsistent. Therefore, NUREG-1718, Section 6.4.3.3.4, should be
revised to remove the following sentence:
``A minimum subcritical margin of 0.05 is generally considered
to be acceptable without additional justification when both the bias
and its uncertainty are determined to be negligible.''
References
ANSI/ANS-8.1-1998, ``Nuclear Criticality Safety in Operations with
Fissionable Materials Outside Reactors,'' American Nuclear Society.
ANSI/ANS-8.17-1984 (R1997), ``Criticality Safety Criteria for the
Handling, Storage, and Transportation of LWR [Light Water Reactor] Fuel
Outside Reactors,'' American Nuclear Society.
IN 2005-13, ``Potential Non-Conservative Error in Modeling
Geometric Regions in the KENO-V.a Criticality Code,'' May 17, 2005.
U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG-1520,
``Standard Review Plan for the Review of a License Application for a
Fuel Cycle Facility.'' NRC: Washington, DC March 2002.
U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG-1718,
``Standard Review Plan for the Review of an Application for a Mixed
Oxide (MOX) Fuel Fabrication Facility.'' NRC: Washington, DC August
2000.
U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG/CR-6698,
``Guide for Validation of Nuclear Criticality Safety Calculational
Methodology.'' NRC: Washington, DC January 2001.
U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG/CR-6361,
``Criticality Benchmark Guide for Light-Water-Reactor Fuel in
Transportation and Storage Packages.'' NRC: Washington, DC March 1997.
Approved: -------- Date: ----
Director, Division of Fuel Cycle Safety and Safeguards, NMSS
Appendix A--ANSI/ANS-8.17 Calculation of Maximum keff
ANSI/ANS-8.17-1984 (R1997), ``Criticality Safety Criteria for
the Handling, Storage, and Transportation of LWR Fuel Outside
Reactors,'' contains a detailed discussion of the various factors
that should be considered in setting keff limits. This is
consistent with, but more detailed than, the discussion in ANSI/ANS-
8.1-1998.
The subcriticality criterion from Section 5.1 of ANSI/ANS-8.17-
1984 (R1997) is:
ks <= kc - [Delta]ks -
[Delta]kc - [Delta]km
where ks is the calculated keff corresponding
to the application, [Delta]ks is its uncertainty,
kc is the mean keff resulting from the
calculation of critical experiments, [Delta]kc is its
uncertainty, and [Delta]km is the MMS. The types of
uncertainties included in each of these ``delta'' terms is provided,
and includes the following:
[Delta]ks = (1) Statistical uncertainties in
computing ks; (2) convergence uncertainties in computing
ks, (3) material tolerances; (4) fabrication tolerances;
(5) uncertainties due to limitations in the geometric representation
used in the method; and (6) uncertainties due to limitations in the
material representations used in the method.
[Delta]kc = (7) uncertainties in the critical
experiments; (8) statistical uncertainties in computing
kc; (9) convergence uncertainties in computing
kc; (10) uncertainties due to extrapolating kc
outside the range of experimental data; (11) uncertainties due to
limitations in the geometric representations used in the method; and
(12) uncertainties due to limitations in the material
representations used in the method.
[Delta]km = an allowance for any additional
uncertainties (MMS).
To the extent that not all 12 sources of uncertainty listed
above have been explicitly taken into account, they may be allowed
for by increasing the value of [Delta]km. The more of
these sources of uncertainty that have been taken into account, the
smaller the necessary additional margin [Delta]km. As a
general principle, however, the MMS should be large compared to
known uncertainties in the nuclear data and limitations of the
methodology. However, a value of the MMS below 0.02 should not be
used.
Frequently, the terms in the above equation relating to the
application are grouped on the left-hand side of the equation, so
that the equation is rewritten as follows:
ks + [Delta]ks <= kc -
[Delta]kc - [Delta]km
where the terms on the right-hand side of the equation are often
lumped together and termed the Upper Subcritical Limit (USL), so
that the USL = kc - [Delta]kc-
[Delta]km.
Relation to the Minimum Subcritical Margin (MMS)
The MoS has been defined as the difference between the actual
value of keff and the value of keff at which
the system is expected to be critical. The expected (best estimate)
critical value of keff is the mean keff value
of all critical experiments analyzed (bias), including consideration
of the uncertainty in the bias (i.e., kc-
[Delta]kc). The calculated value of keff for
an application generally exceeds the actual (physical)
keff value due to conservative assumptions in modeling
the system. In terms of the above USL equation, the MoS may be
expressed mathematically as:
MoS = kc - [Delta]kc - (ks -
[Delta]ksa) - [Delta]ks
where the term in parentheses is equal to the actual (physical)
keff of the application, ksa. A term,
[Delta]ksa, has been added to represent the difference
between the actual and calculated value of keff for the
application (i.e., [Delta]ksa = change in keff
resulting from modeling conservatism). In terms of the USL:
MoS = USL + [Delta]km -ks +
[Delta]ksa - [Delta]ks
The minimum allowed value of the MoS is reached when the
calculated keff for the application, ks +
[Delta]ks, is equal to the USL. When this occurs, the
minimum value of the MoS is:
MoS >= [Delta]km + [Delta]ksa
Thus, adequate margin (MoS) may be assured either by
conservatism in modeling practices or in the explicit specification
of [Delta]km (MMS). This is discussed in the ISG section
on modeling conservatism.
Glossary
Application: calculation of a fissionable system in the facility
performed to demonstrate subcriticality under normal or credible
abnormal conditions.
Area of applicability: the ranges of material compositions and
geometric arrangements within which the bias of a calculational
method is established.
Benchmark experiment: a critical experiment that has been peer-
reviewed and published and is sufficiently well-defined to be used
for validation of calculational methods.
Bias: a measure of the systematic differences between
calculational method results and experimental data.
[[Page 60575]]
Bias uncertainty: a measure of both the accuracy and precision
of the calculations and the uncertainty in the experimental data.
Calculational method: includes the hardware platform, operating
system, computer algorithms and methods, nuclear reaction data, and
methods used to construct computer models.
Critical experiment: a fissionable system that has been
experimentally determined to be critical (with keff [ap]
1).
Margin of safety: the difference between the actual value of a
parameter and the value of the parameter at which the system is
expected to be critical with critical defined as keff = 1
= bias = bias uncertainty.
Margin of subcriticality (MoS): the difference between the
actual value of keff and the value of keff at
which the system is expected to be critical with critical defined as
keff = 1 = bias = bias uncertainty.
Minimum margin of subcriticality (MMS): a minimum allowed margin
of subcriticality, which is an allowance for any unknown
uncertainties in calculating keff.
Subcritical limit: the bounding value of a controlled parameter
under normal case conditions.
Upper subcritical limit (USL): the maximum allowed value of
keff (including uncertainty in keff), under
both normal and credible abnormal conditions, including allowance
for the bias, the bias uncertainty, and a minimum margin of
subcriticality.
[FR Doc. 05-20785 Filed 10-17-05; 8:45 am]
BILLING CODE 7590-01-P