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DOE OSTI2024

The Essence of Cryptol: A Denotational Cryptol Interpreter in Coq for Foundational Assurances for Quantum Resistant Cryptosystems

Systems of the utmost consequence need a means to establish authenticity of software and data. Cryptosystems implement authentication, but can be vulnerable to cryptographic and implementation attacks. With the threat of quantum cryptographic attacks, “post-quantum” cryptosystems (PQCs) must be henceforth used in these systems. However, the new cryptography needs new ways to, rigorously and machine-checkably, prove systems free of vulnerabilities. We propose a retargetable capability to rapidly instantiate proven correct postquantum cryptosystems through novel proof-carrying synthesis and proof-automation technique, extending those proven successful on existing systems. This capability is crucial to meeting the cryptographic requirements for future high-consequence systems. Since specifications for high consequence cryptography are presently captured in a domain specific language known as Cryptol. While this can enable convenient fully automated reasoning about Cryptol specificaitons and implementations via the Software Analysis Workbench (SAW), Cryptol has expressivity gaps, so that cryptosystems with probabilistic programming features like Falcon cannot be fully expressed in the language. Moreover, SAW’s automation fails for programs and specificaitons with inductive and recursive structure, as in the Sphincs+ PQC. Finally, Cryptol and SAW together represent some 200,000 lines of unverified Haskell, so that the any guarantees about high consequence cryptography are presently contingent on a large, unverified, yet trusted computing base. The first step of the larger project of agile, assured crpytography is therefore to provide a formal, mechanized semantics for Cryptol, so that the specifications expressed by cryptographers in Cryptol can be reasoned about and compiled into performant implementations with a foundational, machine checkable certificate of correctness. This report describes our work on this first step, culminating in the design of a certified denotational interpreter, in Coq, for core Cryptol.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

Research on probabilistic information processing

The work accomplished on probabilistic information processing (PIP) is reported. The research proposals and decision analysis are discussed along with the results of research on MSC setting, multiattribute utilities, and Bayesian research. Abstracts of reports concerning the PIP research are included.

Edwards, W.↗

DOE OSTI2024

"Source Term Modeling for Advanced Gas Micro-Reactors"

Maintaining the safety of the public, environment, and operating personnel is the most important factor in designing, operating, maintaining, and decommissioning nuclear reactors. In recent years, there has been a growing interest in the development of micro-reactors employing TRi-structural ISOtropic (TRISO)-coated particle fuel. In gas reactors, TRISO fuel plays an important role in the safety case for high temperature reactors because of the fission product retention properties of the fuel. This ability enables the use of a functional containment strategy for the reactor where multiple barriers are used to prevent fission product release to the environment. Part of the safety analysis of these advanced reactors is the assessment of radionuclide releases under normal and accident conditions through the multiple credited safety barriers. Using conservative assumptions, a mechanistic analysis can be performed to quantify these releases that combines the probabilistic assessment of failure with analytic solutions to radionuclide transport equations. Source term modeling for TRISO fuel has been performed for previous reactor designs; however, these models are outdated, in many cases proprietary, and need updates to be applied to the current state of TRISO fuel technology and alternative gas reactor core configurations [1]. Currently, the only publicly available source term assessment for gas reactors is an expert-based Monte Carlo simulation based on the effectiveness of the fuel kernel, coating layers, and graphite block in a modular high temperature gas reactor [2]. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that could be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. The release is calculated by the diffusion of the key safety important fission products through the kernel, silicon carbide (SiC), graphite for both intact and defective TRISO particles based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system to remove fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. The model then can calculate the fission product release for any transient temperature profile and fission product releases can then be used to assess radiological dose to the workers and the public using conventional dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy (DOE) Advanced Gas Reactor (AGR) TRISO fuel development program. The model is coded in python with inputs and outputs in excel spreadsheets, as well as python plotting utilities to aid in the interpretation of the results. References: [1] INL, NGNP Mechanistic Source Term White Paper, INL-10-17997, July 2010. [2] David A. Petti, Richard R. Hobbins, Peter Lowry, Hans Gougar, “Representative Source Terms and The Influence of Reactor Attributes on Functional Containment in Modular High Temperature Gas-cooled Reactors,” Nuclear Technology, Vol. 184, p. 181-197, Nov. 2013.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Experimental Analysis of the Effects of Simulator Complexity on Human Performance

Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2006

An Overview of the Role of Systems Analysis in NASA's Hypersonics Project

NASA's Aeronautics Research Mission Directorate recently restructured its Vehicle Systems Program, refocusing it towards understanding the fundamental physics that govern flight in all speed regimes. Now called the Fundamental Aeronautics Program, it is comprised of four new projects, Subsonic Fixed Wing, Subsonic Rotary Wing, Supersonics, and Hypersonics. The Aeronautics Research Mission Directorate has charged the Hypersonics Project with having a basic understanding of all systems that travel at hypersonic speeds within the Earth's and other planets atmospheres. This includes both powered and unpowered systems, such as re-entry vehicles and vehicles powered by rocket or airbreathing propulsion that cruise in and accelerate through the atmosphere. The primary objective of the Hypersonics Project is to develop physics-based predictive tools that enable the design, analysis and optimization of such systems. The Hypersonics Project charges the systems analysis discipline team with providing it the decision-making information it needs to properly guide research and technology development. Credible, rapid, and robust multi-disciplinary system analysis processes and design tools are required in order to generate this information. To this end, the principal challenges for the systems analysis team are the introduction of high fidelity physics into the analysis process and integration into a design environment, quantification of design uncertainty through the use of probabilistic methods, reduction in design cycle time, and the development and implementation of robust processes and tools enabling a wide design space and associated technology assessment capability. This paper will discuss the roles and responsibilities of the systems analysis discipline team within the Hypersonics Project as well as the tools, methods, processes, and approach that the team will undertake in order to perform its project designated functions.

Robinson, Jeffrey S.↗

DOE OSTI2021

Benchmarking of Probabilistic Fracture Mechanics Models in Grizzly

In 2020, as part of contract/task order 31310019N0006/31310020F0060, the Nuclear Regulatory Commission tasked Idaho National Laboratory (INL) with benchmarking the MOOSE/GRIZZLY code against the NRC’s Fracture Analysis of Vessels - Oak Ridge (FAVOR), version 16.1 code. Both codes can predict large light-water reactor vessel integrity deterministically and probabilistically. In fact, such predictions are the primary purpose of the FAVOR code, and are a main focus of the GRIZZLY code, although GRIZZLY is capable of modeling a wider range of geometries and problems. The vessel integrity modeling capabilities of the GRIZZLY code are still under development and being gradually expanded, and the capability to model vessel fracture by crack growth has not yet been integrated into GRIZZLY (although it does exist in pre-release versions). Thus, the present benchmark study focused on comparing predictions for the conditional probability of crack growth initiation (CPI) between GRIZZLY and FAVOR v16.1. The present benchmarking study expands on a previous study completed in 2020, which focused on single plate regions, various flaw types, and full vessel studies. The previous study was limited in scope and revealed some relatively large discrepancies, particularly in the case of weld regions. In the present study, detailed investigations into the sources of discrepancies between GRIZZLY and FAVOR were performed, with a focus on weld regions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2003

Development of Probabilistic Life Prediction Methodologies and Testing Strategies for MEMS and CMC's

This effort is to investigate probabilistic life prediction methodologies for ceramic matrix composites and MicroElectroMechanical Systems (MEMS) and to analyze designs that determine stochastic properties of MEMS. For CMC's this includes a brief literature survey regarding lifing methodologies. Also of interest for MEMS is the design of a proper test for the Weibull size effect in thin film (bulge test) specimens. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. A main objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structures/Life (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads. A second set of objectives is to determine the applicability/suitability of the CARES/Life methodology for CMC analysis, what changes would be needed to the methodology and software, and if feasible, run a demonstration problem. Also important is an evaluation of CARES/Life coupled to the ANSYS Probabilistic Design System (PDS) and the potential of coupling transient reliability analysis to the ANSYS PDS.

Jadaan, Osama↗

DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1993

An in-depth probabilistic study of external tank attach ring

This report deals with conducting a probabilistic study of the external tank attach ring (ETA) used as an interface between the external tank attach struts and the solid rocket booster. The ideas were to use probabilistic distributions for material, geometric, and load properties; to calculate probabilistic margins of safety; and then to compare results against the deterministic factors of safety that were used in the actual design process. The report describes how this was done and discusses some of the road blocks and data problems that were encountered during the study and provides some conclusions. A further refinement of this study is being considered for future work which would make more direct use of finite element analysis data coupled with Monte Carlo simulation. The basic conclusion herein indicates that the probabilistic margins of safety for the cases analyzed (by use of existing data) appear to support deterministic results and actually indicate higher reliabilities.

Pizzano, Frank↗

DOE OSTI2026

Enabling Dynamic Probabilistic Risk Assessment of Physical Security Using EMRALD and MAAP (Presentation)

The optimization of physical security in nuclear power plants requires sophisticated methodologies that integrate operator actions and plant behavior through advanced simulation tools. Idaho National Laboratory has developed the Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF) methodology, an approach that integrates force-on-force simulations, dynamic probabilistic risk assessment, and thermal hydraulics modeling to enhance security planning while reducing costs. A reduced order model for thermal hydraulic simulations performed by the Modular Accident Analysis Program (MAAP) was developed to evaluate reactor core behavior during attack scenarios. MAAP simulations are computationally intensive and must be run in a secure environment, complicating analysis and validation. By pre-computed scenario outcomes for a small number of modified parameters, the reduced order model significantly decreases the computational cost and enables offsite review of the results.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗

NASA NTRS2017

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Britton, Paul↗

NASA NTRSDate not supplied

Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.

Lauren Mcintyre↗

NASA NTRS2000

A Probabilistic Framework for the Validation and Certification of Computer Simulations

The paper presents a methodology for quantifying, propagating, and managing the uncertainty in the data required to initialize computer simulations of complex phenomena. The purpose of the methodology is to permit the quantitative assessment of a certification level to be associated with the predictions from the simulations, as well as the design of a data acquisition strategy to achieve a target level of certification. The value of a methodology that can address the above issues is obvious, specially in light of the trend in the availability of computational resources, as well as the trend in sensor technology. These two trends make it possible to probe physical phenomena both with physical sensors, as well as with complex models, at previously inconceivable levels. With these new abilities arises the need to develop the knowledge to integrate the information from sensors and computer simulations. This is achieved in the present work by tracing both activities back to a level of abstraction that highlights their commonalities, thus allowing them to be manipulated in a mathematically consistent fashion. In particular, the mathematical theory underlying computer simulations has long been associated with partial differential equations and functional analysis concepts such as Hilbert spares and orthogonal projections. By relying on a probabilistic framework for the modeling of data, a Hilbert space framework emerges that permits the modeling of coefficients in the governing equations as random variables, or equivalently, as elements in a Hilbert space. This permits the development of an approximation theory for probabilistic problems that parallels that of deterministic approximation theory. According to this formalism, the solution of the problem is identified by its projection on a basis in the Hilbert space of random variables, as opposed to more traditional techniques where the solution is approximated by its first or second-order statistics. The present representation, in addition to capturing significantly more information than the traditional approach, facilitates the linkage between different interacting stochastic systems as is typically observed in real-life situations.

Ghanem, Roger↗

DOE OSTI2022

Updated Economic Model for Estimation of GDP Losses in the MACCS Offsite Consequence Analysis Code RDEIM Model Report for MACCS v4.2

This report updates the Regional Disruption Economic Impact Model (RDEIM) GDP-based model described in Bixler et al. (2020) used in the MACCS accident consequence analysis code. MACCS is the U.S. Nuclear Regulatory Commission (NRC) used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. The update of the RDEIM model in version 4.2 expresses the national recovery calculation explicitly, rather than implicitly as in the previous version. The calculation of the total national GDP losses remains unchanged. However, anticipated gains from recovery are now allocated across all the GDP loss types – direct, indirect, and induced – whereas in version 4.1, all recovery gains were accounted for in the indirect loss type. To achieve this, we’ve introduced new methodology to streamline and simplify the calculation of all types of losses and recovery. In addition, RDEIM includes other kinds of losses, including tangible wealth. This includes loss of tangible assets (e.g., depreciation) and accident expenditures (e.g., decontamination). This document describes the updated RDEIM economic model and provides examples of loss and recovery calculation, results analysis, and presentation. Changes to the tangible cost calculation and accident expenditures are described in section 2.2. The updates to the RDEIM input-output (I-O) model are not expected to affect the final benchmark results Bixler et al. (2020), as the RDEIM calculation for the total national GDP losses remains unchanged. The reader is referred to the MACCS revision history for other cost modelling changes since version 4.0 that may affect the benchmark. RDEIM has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS. It is based on I-O theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in in its treatment and estimation of indirect loss multipliers, elimination of double-counting associated with inter-industry trade in the affected area, and that it is intended to be used for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

The research challenges in security and safeguards for nuclear fission batteries

This paper discusses the nuclear security and safeguards research challenges presented by the development and deployment of nuclear fission batteries. These are defined as easily transportable and deployable nuclear systems which are designed to operate either unattended or autonomously. We start by defining the current landscape of domestic and international safeguards and security and discuss how it can be affected by the introduction these new nuclear systems. We then specifically discuss the technology gaps and technologies to be developed to facilitate their practical deployment. We find that, as expected, we can leverage conclusions from existing security and safeguards studies for small modular rectors and develop bespoke target set analyses to inform security postures based on probabilistic risk assessment. We find also that specific fission battery security economic analysis tools are needed and security by design must be applied early. However, the most important finding is that these new systems will require a new comprehensive set of cyber tools covering transportation, installation, operation, maintenance security of the fission battery systems. Altogether, we find that the development of the necessary security and safeguards requirements at the design phase of such a technology will be of great benefit in the smooth deployment of this modern nuclear energy system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

PRISM3D: a 3-D reference seismic model for Iberia and adjacent areas

SUMMARY We present PRISM3D, a 3-D reference seismic model of P- and S-wave velocities for Iberia and adjacent areas. PRISM3D results from the combination of the most up-to-date earth models available for the region. It extends horizontally from 15°W to 5°E in longitude, 34°N to 46°N in latitude and vertically from 3.5 km above to 200 km below sea level, and is modelled on a regular grid with 10 and 0.5 km of grid node spacing in the horizontal and vertical directions, respectively. It was designed using models inferred from local and teleseismic body-wave tomography, earthquake and ambient noise surface wave tomography, receiver function analysis and active source experiments. It includes two interfaces, namely the topography/bathymetry and the Mohorovičić (Moho) discontinuity. The Moho was modelled from previously published receiver function analysis and deep seismic sounding results. To that end we used a probabilistic surface reconstruction algorithm that allowed to extract the mean of the Moho depth surface along with its associated standard deviation, which provides a depth uncertainty estimate. The Moho depth model is in good agreement with previously published models, although it presents slightly sharper gardients in orogenic areas such as the Pyrenees or the Betic-Rif system. Crustal and mantle P- and S-wave wave speed grids were built separately on each side of the Moho depth surface by weighted average of existing models, thus allowing to realistically render the speed gradients across that interface. The associated weighted standard deviation was also calculated, which provides an uncertainty estimation on the average wave speed values at any point of the grid. At shallow depths (<10 km), low P and S wave speeds and high VP/VS are observed in offshore basins, while the Iberian Massif, which covers a large part of western Iberia, appears characterized by a rather flat Moho, higher than average VP and VS and low VP/VS. Conversely, the Betic-Rif system seems to be associated with low VP and VS, combined with high VP/VS in comparison to the rest of the study area. The most prominent feature of the mantle is the well known high wave speed anomaly related to the Alboran slab imaged in various mantle tomography studies. The consistency of PRISM3D with previous work is verified by comparing it with two recent studies, with which it shows a good general agreement.The impact of the new 3-D model is illustrated through a simple synthetic experiment, which shows that the lateral variations of the wave speed can produce traveltime differences ranging from –1.5 and 1.5 s for P waves and from –2.5 and 2.5 s for S waves at local to regional distances. Such values are far larger than phase picking uncertainties and would likely affect earthquake hypocentral parameter estimations. The new 3-D model thus provides a basis for regional studies including earthquake source studies, Earth structure investigations and geodynamic modelling of Iberia and its surroundings.

Arroucau, P.↗

DOE OSTI2023

ML for microbiomes

The software provides machine learning analysis and visualization to detect patterns in microbiome data, including topic modeling, probabilistic graphical modeling, conventional machine learning methods, and deep learning. The software is written in python and R, it uses some python and R libraries as well as big open-source libraries like sklearn, networkX, pytorch (python), pgmpy (python), and bnlearn (R). It also has a script to use for MALLET and DTM (open-source packages for topic modeling, written in Java).

Kim, Anastasiia↗