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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Deconsolidation and Leach Burn Leach of Seven As Irradiated AGR 5/6/7 TRISO Fuel Compacts from Capsules 2, 3, 4, and 5

Seven as-irradiated Advanced Gas Reactor (AGR) 5/6/7 compacts underwent destructive post-irradiation examination via deconsolidation-leach-burn leach at Idaho National Laboratory (INL). The selection of the compacts extended the upper and lower limits of time-average volume-average (TAVA) temperature for compacts that had gone through deconsolidation-leach-burn leach so far. The measured inventories of fission products and actinides in the compact matrix and outer pyrolytic carbon were reported. Results indicated unexpectedly higher rates of fuel kernel leaching compared to compacts from AGR-1 and AGR-2. These failure rates were attributed to damage during post-irradiation sample handling, rather than irradiation itself. AGR-5/6/7 compacts have little or no matrix coverage for some particles at the top and bottom ends of cylindrical fuel compacts, making them more fragile. Sixty particles were randomly sampled from each compact, and the gamma results were reported. Three SiC shells from were identified, one of which showed signs of chemical attack in the high-irradiation temperature compact.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Statistical Sampling Plan for AGR-5/6/7 Fuel Materials

Kernels, coated particles, and compacts will be manufactured to predefined specifications for irradiation in the AGR 5/6/7 experiments. Many tests will be performed for quality assurance and most of these tests correspond to criteria that have to be met with 95% confidence, based on random samples. This report describes the statistical acceptance testing methods and gives minimum sample sizes for kernels to achieve 95% confidence. Coated particles and compacts will be addressed in revisions to this report. The methods used for sample size determination are described in detail in INL/EXT 05 00349, Statistical Methods Handbook for Advanced Gas Reactor Fuel Materials (Einerson 2005). The statistical approach is to collect a representative sample, apply an acceptance test to the sample, and accept or reject the product based on this test for each criterion. The criteria or “product specifications” are contained in SPC-1352, “AGR 5/6/7 Fuel Specification,” Revision 1 and are duplicated in this plan.

+↗
DOE OSTI2018

Optimization, Inference and Learning for Advanced Networks

This piece of software aims at providing an implementation of methods to perform efficient optimization, probabilistic inference and model selection and learning for various type of networked systems. The software contains a suite of methods and functions that: 1) perform probabilistic inference and random sampling on networked models 2) select or learn networked models from data 3) solve optimization tasks with a networked structure Network models that are supported by these features include, but are not limited to, factor graphs and Markov random fields such as Ising models, Potts models, p-spins models, Gaussian random fields and restricted Boltzmann machine.

Hijazi, Hassan↗
DOE OSTI2021

Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

New Capabilities for Sampling Tools

This report will discuss new capabilities that have been added to the sample.py and uniform_sampler.py codes. The codes have been updated to allow for both log-uniform sampling and categorical variables. The categorical variables do not have to be numeric. The different values for a categorical variable are specified in a limits file by having spaces between them. The difference between sample.py and uniform_sampler.py is that sample.py is for generating random samples and uniform_sampler.py is for generating samples or points on a fixed grid. After sourcing a file to set the environment, execute the codes with the commands: sample.py and uniform sampler.py .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Hybrid-BPR (Bayesian Personalized Ranking with Feature Embeddings and Explicit Negative Sampling) [SWR-26-039]

Hybrid-BPR is a Python library for Bayesian Personalized Ranking (BPR) with two key capabilities that go beyond standard BPR implementations: 1. User and item feature embeddings - incorporate content-based signals (genres, tags, metadata) alongside collaborative filtering. 2. Implicit negative interactions - use observed non-interactions (e.g. viewed-but-not-clicked) as negative training signal instead of random sampling from the full item space. The software is built for recommender systems research with MLflow experiment tracking, parallel hyperparameter sweeps, and standard ranking metrics.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2017

Substantial soil organic carbon retention along floodplains of mountain streams

Small, snowmelt–dominated mountain streams have the potential to store substantial organic carbon in floodplain sediment because of high inputs of particulate organic matter, relatively lower temperatures compared with lowland regions, and potential for increased moisture conditions. This work (i) quantifies mean soil organic carbon (OC) content along 24 study reaches in the Colorado Rocky Mountains using 660 soil samples, (ii) identifies potential controls of OC content based on soil properties and spatial position with respect to the channel, and (iii) and examines soil properties and OC across various floodplain geomorphic features in the study area. Stepwise multiple linear regression (adjusted r 2 = 0.48, p < 0.001) indicates that percentage of silt and clay, sample depth, percent sand, distance from the channel, and relative elevation from the channel are significant predictors of OC content in the study area. Principle component analysis indicates limited separation between geomorphic floodplain features based on predictors of OC content. A lack of significant differences among floodplain features suggests that the systematic random sampling employed in this study can capture the variability of OC across floodplains in the study area. Mean floodplain OC (6.3 ± 0.3%) is more variable but on average greater than values in uplands (1.5 ± 0.08% to 2.2 ± 0.14%) of the Colorado Front Range and higher than published values from floodplains in other regions, particularly those of larger rivers.

58 GEOSCIENCES↗
DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Uncertainty analysis for VERA problem 2 using the cell-code Condor v2.8.05

Condor is a cell-level neutronic calculation code that applies multi-group collision probabilities with heterogeneous response coupling method within generic geometry configurations. Under the Condor's code continuous development, the incorporation of up-to-date methodologies and state-of-the-art practices in reactor analysis represents a driving force. In this work, the capabilities of Condor v2.8.05 to develop an uncertainty analysis for realistic PWR-kind fuel assemblies are studied. The Total Monte Carlo approach is applied to quantify the impact of fabrication tolerances in the code's results for reactivity and power distributions, by means of randomly sampled input values using the VERA problem 2 as basis. The VERA problem 2 proposes a series of Westinghouse 2D 17 x 17-type fuel lattices, to be calculated reflected at beginning-of-life without Xe. The configurations correspond to a modern PWR. Selected neutronic parameters from Condor runs are thus analyzed in terms of the observed spread as well as the obtained distributions for the randomly perturbed cases, showing the capability of the code to handle the required input data, as well as its ability to provide valuable insights regarding uncertainty quantification.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation

Herein, we present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases. Published by AIP Publishing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Cryo-STEM Tomography with Inpainting

Traditionally, microscopists have worked with the Nyquist-Shannon theory of sampling, which states that to be able to reconstruct the image fully it needs to be sampled at a rate of at least twice the highest spatial frequency in the image. This sampling rate assumes that the image is sampled at regular intervals and that every pixel contains information that is crucial for the image (it even assumes that noise is important). Images in general, and especially low dose S/TEM images, contain significantly less information than can be encoded by a grid of pixels (which is why image compression works). Mathematically speaking, the image data has a low dimensional or sparse representation. Through the application of compressive sensing methods [1,2,3] this representation can be found using pre-designed measurements that are usually random for implementation simplicity. These measurements and the compressive sensing reconstruction algorithms have the added benefit of reducing noise. This reconstruction approach can be extended into higher dimensions, whereby the random sampling in each 2-D image can be extended into: a sequence of tomographic projections (i.e. tilt images); a sequence of video frames (i.e. incorporating temporal resolution and dynamics); spectral resolution (i.e. energy filtering an image to see the distribution of elements); and ptychography (i.e. sampling a full diffraction image at each location in a 2-D grid across the sample). This approach has been employed experimentally for materials science samples requiring low-dose imaging [2], and can be readily applied to biological samples. Figure 1 shows the resolution possible in a complex biological system, mouse pancreatic islet beta cells [4], when tomogram slices are reconstructed using subsampling. Reducing the number of pixels (1/6 pix and 1/3*1/3) shows minimal degradation compared to the reconstructions using all pixels (all data and 1/3 tilt). Although subsampling 1/6 of the tilts (1/6 of overall dose) degrades the reconstruction to the point that the cellular structures cannot be identified. Using 1/3 of both the pixels and the tilts provides a high quality image at 1/9 the overall dose even for this most basic and rapid demonstration of the CS methods. Figure 2 demonstrates the theoretical tomogram reconstruction quality (vertical axis) as undersampling (horizontal axis) is increased; we examined subsampling pixels and tilt-angles individually and a combined approach in which both pixels and tilts are sub-sampled. Note that subsampling pixels maintains high quality reconstructions (solid lines). Using the inpainting algorithm to obtain tomograms can automatically reduce the dose applied to the system by an order of magnitude. Perhaps the best way to understand the impact is to consider that by using inpainting (and with minimal hardware changes), a sample that can normally withstand a dose of ~10 e/Å2 can potentially be imaged with an “equivalent quality” to a dose level of 103 e/Å2. To put this in perspective, this is approaching the dose level used for the most advanced images, in terms of spatial resolution, for inorganic systems. While there are issues for biological specimens beyond dose (structural complexity being the most important one), this sampling approach allows the methods that are traditionally used for materials science to be applied to biological systems [5]. References: [1] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2] L Kovarik, A Stevens, A Liyu et al. Appl. Phys. Lett. 109, 164102 (2016) [3] A Stevens, L Kovarik, P Abellan et al. Adv. Structural and Chemical Imaging 1(10), (2015), pp. 1. [4] MD Guay, W Czaja, MA Aronova et al. Scientific Reports 6, 27614 (2016) [5] Supported by the Chemical Imaging, Signature Discovery, and Analytics in Motion Initiatives at PNNL. PNNL is operated by Battelle Memorial Inst. for the US DOE; contract DE-AC05-76RL01830.

Stevens, Andrew↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2017

Stellar clusterings around 'isolated' Massive YSOs in the LMC

Observations suggest that there is a significant fraction of O stars in the field of the Milky Way that appear to have formed in isolation or in low-mass clusters (<100 M{sub ⊙}). The existence of these high-mass stars that apparently formed in the field challenges the generally accepted paradigm, which requires star formation to occur in clustered environments. In order to understand the physical conditions for the formation of these stars, it is necessary to observe isolated high-mass stars while they are still forming. With the Hubble Space Telescope, we observe the seven most isolated massive (>8 M{sub ⊙}) young stellar objects (MYSOs) in the Large Magellanic Cloud. The observations show that while these MYSOs are remote from other MYSOs, OB associations, and even known giant molecular clouds, they are actually not isolated at all. Imaging reveals ∼100 to several hundred pre-main-sequence (PMS) stars in the vicinity of each MYSO. These previously undetected PMS stars form prominent compact clusters around the MYSOs, and in most cases they are also distributed sparsely across the observed regions. Contrary to what previous high-mass field star studies show, these observations suggest that high-mass stars may not be able to form in clusters with masses less than 100 M{sub ⊙}. If these MYSOs are indeed the best candidates for isolated high-mass star formation, then the lack of isolation is at odds with random sampling of the initial mass function. Moreover, while isolated MYSOs may not exist, we find evidence that isolated clusters containing O stars can exist, which in itself is rare.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Megadrought: A Series of Unfortunate La Niña Events?

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Recent Progress on the Stochastic Objective Decision Aide (SODA) Application

Idaho State University, in collaboration with Idaho National Laboratory, is engaged in development of a simple to use software application that utilizes stochastic techniques for the calculation of radiation doses associated with hypothetical radioactive material releases. This work is being performed through a grant from the U.S. Department of Energy (DOE) Nuclear Safety Research and Development (NSR and D) Program. Initial progress on the application was reported previously. Recent additions to the application are reported here. The Stochastic Objective Decision Aide (SODA) provides an easy to use software application to help decision makers support their judgment about the need or lack of need for safety structures, systems, and components. SODA is not intended to replace traditional design basis accident dose calculation methodology, nor is it intended to replace existing software used to perform formal dose consequence calculations. Rather, SODA is intended to supplement information obtained from existing codes to give a decision maker more information about an accident scenario. SODA also provides a simple method to perform parametric or sensitivity studies on individual input parameters. Non-reactor nuclear facilities operated under the approval authority of the US DOE use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge dose evaluation guidelines. Unmitigated events that sufficiently challenge dose evaluation guidelines merit the selection of safety structures, systems, or components (SSCs) to prevent or mitigate the hazard. Dose consequence calculations traditionally involve use of the 'five-factor' formula: ST = MAR.DR.ARF.RF.LPF where ST is the source term (Bq), MAR is the total available material-at-risk (Bq), DR is the damage ratio (no units), ARF is the airborne release fraction (no units), RF is the respirable fraction (no units), and LPF is the leak path factor (no units). Potential radiation doses are then calculated using: CED = χ/Q.BR.ST.DCF (2) where CED is the committed effective dose (Sv), χ/Q is the plume dispersion (s/m{sup 3}), BR is the breathing rate (m{sup 3}/s), ST is the source term (Bq), and DCF is the dose conversion factor (Sv/Bq). Conservative single value input parameters are typically used to represent ARF, RF, LPF and BR. The traditional methodology, while conservative, can lead to skewed conclusions in the balance between cost and risk reduction resulting in over engineered systems with greater design, construction and operating costs. Rather than using a bounding single point value for each parameter in the dose consequence calculation, SODA uses distributions for some or all of the parameters. Each input parameter distribution is stochastically sampled and the resulting dose consequence calculation is repeated many times to develop a dose consequence distribution. The resulting distribution provides more information for the decision maker to rely upon when determining the need for safety SSCs. The SODA application was developed using MATLAB and it incorporates the use of Monte Carlo techniques as well as a graphical user interface (GUI). The application includes user selection of the governing distribution for parameters such as the MAR, DR, ARF, RF, LPF,χ/Q, BR, and DCF. While MATLAB was used for the development work, the application is distributed as a self-contained executable program. Features of the application include pull down menus with available distributions for the various parameters. The user has the option to plot the input parameter distribution that results from the random sampling process. The resulting dose consequence distribution is automatically analyzed to determine its similarity to well know distributions. This was done using the Bayesian Information Criterion method. If the resulting distribution is sufficiently similar to a well-known distribution, the application provides information about the distributions such as the mean value and other parameters. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗