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NASA NTRS1991

Markov reward processes

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

Smith, R. M.↗

DOE OSTI2023

Coordination-Dependent Chemical Reactivity of TFSI Anions at a Mg Metal Interface

Charge transfer across the electrode–electrolyte interface is a highly complex and convoluted process involving diverse solvated species with varying structures and compositions. Despite recent advances in in situ and operando interfacial analysis, molecular specific reactivity of solvated species is inaccessible due to a lack of precise control over the interfacial constituents and/or an unclear understanding of their spectroscopic fingerprints. However, such molecular-specific understanding is critical to the rational design of energy-efficient solid–electrolyte interphase layers. We have employed ion soft landing, a versatile and highly controlled method, to prepare well-defined interfaces assembled with selected ions, either as solvated species or as bare ions, with distinguishing molecular precision. Equipped with precise control over interfacial composition, we employed in situ multimodal spectroscopic characterization to unravel the molecular specific reactivity of Mg solvated species comprising (i.e., bis(trifluoromethanesulfonyl)imide, TFSI – ) anions and solvent molecules (i.e., dimethoxyethane, DME/G1) on a Mg metal surface relevant to multivalent Mg batteries. In situ multimodal spectroscopic characterization revealed higher reactivity of the undercoordinated solvated species [Mg-TFSI-G1] + compared to the fully coordinated [Mg-TFSI-(G1) 2 ] + species or even the bare TFSI – . These results were corroborated by the computed reaction pathways and energy barriers for decomposition of the TFSI – within Mg solvated species relative to bare TFSI – . Finally, we evaluated the TFSI reactivity under electrochemical conditions using Mg(TFSI) 2 –DME-based phase-separated electrolytes representing different solvated constituents. Based on our multimodal study, we report a detailed understanding of TFSI – decomposition processes as part of coordinated solvated species at a Mg-metal anode that will aid the rational design of improved sustainable electrochemical energy technologies.

25 ENERGY STORAGE↗

DOE OSTI2024

Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications

We report digital twin technology has the potential to improve the effectiveness of international safeguards inspectors by providing a tool which can: first, perform an accurate diversion path analysis, identify their indicators, and required sensors to detect them; and second, monitor facilities in real-time using critical data streams that benefit from this safeguards-by-design approach. Safeguards inspectors are required to visit facilities and verify the nuclear material to ensure no diversion has taken place and detect misuse of the facility; however, this analysis and verification effort is time consuming, and with limited funding it is imperative that time spent at a nuclear facility is focused on key areas. A virtual digital twin of three prototypic sodium fast reactors was developed, where diversion and misuse scenarios were explored to determine how a digital twin could provide inspectors with an understanding of how proliferation may occur and where the most likely areas for proliferation would be. For each of the three reactors, an optimization algorithm was able to find core designs which would be difficult to detect via sensors alone; however, the use of a machine learning adapter provided by the digital twin was able to show general trends in where proliferation as likely to take place.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2000

A natural basis for efficient brain-actuated control

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

NASA Discipline Space Human Factors↗

DOE OSTI2024

Computed Tomography Scanning and Geophysical Measurements of UW Enterprises LP 1-250512-129 Well in Southwestern Indiana

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, were used to characterize Illinois Basin core of the Upper Devonian-Early Mississippian New Albany Shale Formation from Posey County, Indiana. The primary impetus of this work is a collaboration between Indiana Geological and Water Survey at Indiana University Bloomington, NETL, and Woolsey Operating Company LLC to characterize and make publicly available core information from the New Albany Shale of the Illinois Basin. Core characterization of this unconventional oil/gas well will aid in understanding the lithology changes and the fracture complexity of the New Albany Shale. There is a potential for this formation to be developed in the future for critical mineral and rare earth element (CM/REE) extraction. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/uw-enterprises. All equipment and techniques used were non-destructive, enabling future examinations and analyses to be performed on this core. None of the equipment used was suitable for direct visualization of the pore space in the fine-grained structures studied; however, fractures, discontinuities, and millimeter-scale features were readily detectable with the methods tested. Imaging with the NETL medical CT scanner was performed on the entire core. Targeted higher resolution CT scanning of select sections was performed with NETL’s industrial and micro-CT scanner. Qualitative analysis of the medical CT images, coupled with X-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were useful in identifying zones of interest for more detailed analysis. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provides a multi-scale analysis of the core; the resulting macro and micro descriptions are relevant to many subsurface energy related examinations traditionally performed at NETL.

58 GEOSCIENCES↗

DOE OSTI2025

Pressure Gain, Stability, and Operability of Methane/Syngas Based RDEs Under Steady and Transient Conditions (Final Project Report)

The scope of this work addresses key issues associated with losses associated with the detonation wave and other processes internal to the RDE operation, as well as it develops modeling tools for the evaluation of these losses and exhaust emissions in RDEs. The main challenge in studying RDEs is that RDE performance is highly reliant on the specifics of the design so much so that simple/canonical systems alone cannot provide useful engineering information, but practical RDE designs are sufficiently complex and involve extreme operational environments that detailed access either experimentally (laser diagnostics, for instance) or computationally (direct numerical simulations) are as yet to become practical. To overcome this challenge, we have conducted a combined experimental/simulation/analytical study investigating key phenomena that control the characteristics of operation of RDEs. As a result, the study has developed tools and methods that can be used to evaluate performance and design approaches using reduced-physics models, with the assumptions validated using detailed simulations, and the model prediction tested using experimental observations. The specific objectives of the research were: (1) Develop and demonstrate a low-loss fully axial injection concept, taking advantage of stratification effects to alter the detonation structure and position the wave favorably within the combustor; (2) Obtain stability and operability characteristics of an RDE across operating conditions to aid in the development of operability and performance rules for the operations of other systems; and (3) Develop quantitative metrics for performance gain as well as quantitative description of the loss mechanisms through a combination of diagnostics development, reduced-order modeling, and detailed simulations. The work conducted here has made contribution on design of low-loss inlets that has broad application within the power generation industry for use with pressure gain combustion. The operability and stability of different designs, while focusing on axial air inlet designs, has been analyzed. The effect of nozzle and injection conditions was studied. Models and simulations of exhaust emissions, focusing on NOx emission has been developed and used to investigate how operation of the RDE affect NOx production using Lagrangian analysis of RDE simulations. This work has built on previous programs, with the goal of further understanding operation of RDEs and elevate the readiness of design consideration. In addition, a suite of diagnostic and modeling tools have been developed to obtain quantitative metrics on performance based on measurements, which can be readily transferred to other experimental configurations.

08 HYDROGEN↗

DOE OSTI2021

Reconstructing porous media using generative flow networks

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.

58 GEOSCIENCES↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Mechanistic Studies of a Primitive Homolog of Nitrogenase Involved in Coenzyme F430 Biosynthesis

Methyl-coenzyme M reductase (MCR) is the key enzyme in the biological formation and anaerobic oxidation of methane (AOM). Methane is a potent greenhouse gas and the major component of natural gas. Given the abundance of natural gas reserves in remote areas, there is great current interest in a scalable bio-based process for the conversion of methane to liquid fuel or other high-value commodity chemicals. MCR holds much promise for use in such a methane bioconversion strategy. However, MCR cannot currently be produced in an active form in a heterologous host, due in large part to the lack of genetic and biochemical information about the production of holo MCR. In an effort to overcome this deficiency, our laboratory elucidated the biosynthetic pathway of the unique nickel-containing coenzyme of MCR, F430. The key step in coenzyme F430 biosynthesis (Cfb) was found to involve an unprecedented reductive cyclization reaction that converts Ni-sirohydrochlorin a , c -diamide to 15,17 3 -seco-F430-17 3 -acid. This remarkable transformation, which involves a 6-electron reduction of the isobacteriochlorin ring system, cyclization of the c -acetamide side chain to form a γ-lactam ring, and the formation of 7 stereocenters, is catalyzed by a primitive homolog of nitrogenase (CfbCD). Nitrogenase is a two-component metalloenzyme that catalyzes the ATP-dependent reduction of dinitrogen to ammonia (nitrogen fixation). Homologs of nitrogenase are also involved in the biosynthesis of the photosynthetic pigments chlorophyll and bacteriochlorophyll. Phylogenetic analysis of the CfbCD complex suggests that it is representative of a more ancient lineage of the nitrogenase superfamily, and a thorough investigation of its structure and function is likely to shed light on the mechanisms and evolution of these important metalloenzymes that catalyze multi-electron redox reactions. Moreover, a detailed understanding of the mechanism of the CfbCD complex may aid in the development of specific inhibitors to help reduce natural greenhouse gas emissions and can be exploited for the heterologous production of MCR for methane bioconversion. Towards these goals, the following Specific Aims will be pursued to determine the: 1) Identity of the CfbCD reaction product. The exact reaction catalyzed by CfbCD, including the number of electrons transferred and whether it involves enzymatic cyclization, is unclear. Several approaches, including reaction stoichiometry measurements, spectroelectrochemistry, and magnetic resonance spectroscopy will be applied to elucidate the structure of the reaction product and establish whether CfbCD is a reductase or reductive cyclase. 2) Structure, conformational dynamics, and oligomerization state changes of CfbCD. Significant insight into the mechanism and allosteric regulation of CfbCD can be obtained by assessing changes in the structure and dynamics of the complex during the catalytic cycle. To accomplish this, a combination of size-exclusion chromatography, hydrogen-deuterium exchange mass spectrometry, molecular dynamics simulations, and high-resolution structural methods will be employed. 3) Source, order, and stereochemistry of proton additions during CfbCD catalysis. Details regarding the order and stereochemistry of proton additions during the CfbCD reaction will be uncovered using a combined spectroscopic and computational approach. Complementary mechanistic studies employing site-directed mutagenesis and substrate analogs will establish the identity of active site acid residues and the possible involvement of substrate-assisted catalysis during the CfbCD reaction.

09 BIOMASS FUELS↗

DOE OSTI2017

APT malware static trace analysis through bigrams and graph edit distance

Research and business organizations are vulnerable to attack by malware, particularly advanced persistent threat malware tailored for a specific target. Malware identification is made more difficult because samples can be subtly altered to avoid detection by methods that check for an identical match to known code. Different versions of an original piece of malware form a malware family. And when new malicious software is identified, reverse engineers seek to identify its origin and purpose. Knowing whether new malware is from a known family or a previously unobserved family aids the efficiency of reverse engineers. Furthermore, this article presents a three-stage method to classify new malware into a family by comparing its similarity to existing static traces, and assigning it to the most similar family. First, a fast filtering method creates a shortlist of samples with some similarity to the new malware, using a simple bigram comparison of the instructions. The second stage takes the call graph view of the shortlisted static traces and uses simulated annealing to estimate the graph edit distance, a measure of dissimilarity between graphs. Finally, a random forest classifier combines the previous two results to predict the family to which a new sample belongs. Our paper also considers how to detect when malware is from a new family.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Risk informed cyber security for nuclear power plants - 342

Nuclear power plants are increasingly adding digital components for plant operation, safety, and security. These digital components fill a gap with legacy equipment where replacement components no longer exist. They also benefit operation of the plant by increasing efficiency in power generation, monitoring of equipment and plant parameters, as well as aiding operator control. However, the addition of digital components and systems also adds cyber risks with previously unanalyzed failure modes and attack vectors are introduced with these new systems. These risks are difficult to identify, analyze, and mitigate due to the increasingly complex nature of the digital components and the integration of these components with additional plant processes and communication networks. The research presented in this paper develops a new method that addresses the cyber risk to inform appropriate levels of protection. EPRI and Sandia are working under a Cooperative Research and Development Agreement to develop an effective method of evaluating the cyber risk in production nuclear power facilities. The Cyber Hazards Analysis Risk Methodology (CHARM) focuses on ensuring adequate controls are in place for appropriate cyber protection of the plant from radiological release or generation risk. Existing plant hazards analyses (e.g., PRA, FTA) do not account for software deficiencies or adversarial intent. This method leverages existing plant analyses and MIT's Systems Theoretic Process Analysis (STPA) to create cyber informed fault trees. These new fault trees will provide the basis for comprehensive cyber risk analysis and help ensure potential gaps in cyber security controls are identified and corrected. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

An electrochemical mesoscale tool for modeling the corrosion of structural alloys by molten salt

Understanding the impact of microstructure on corrosion rates can aid the development of corrosion-resistant alloys for molten salt reactors. Here in this work, we develop an electrochemical phase-field model for capturing the microstructure-dependent corrosion of structural alloys by molten salts. As a demonstration problem, we apply this model to capture the selective depletion of Cr from Ni-Cr grain boundaries during corrosion in molten FLiBe salt. We perform sensitivity analysis and model verification on 1D simulations to confirm that the model predicts diffusion-limited kinetics. The model is validated using 1D, 2D, and 3D simulations against experimental data for Ni-5Cr and Ni-20Cr corrosion in molten FLiBe. The 1D simulations predict the corrosion behavior with reasonable accuracy when using an effective diffusion coefficient that accurately represents the grain boundary diffusion. 2D simulations that represent the grain structure underpredict the corrosion. 3D simulations that represent the grain structure predict the corrosion with reasonable accuracy. The corrosion rate predicted by the 3D simulations is proportional to the average grain size at the alloy/salt interface.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Integrate FARM with PID controllers: IES Simulation Ecosystem Control System Development

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM was designed to support the HERON software module in the solution of the optimal dispatch problem for IES units. As the result of HERON-FARM dispatch simulation, the set-point trajectories are optimized to meet constraints on both the production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and the process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.) at a coarse time resolution (every 10 or 100 seconds) over long time horizons (several days or weeks). In case the operational constraints need to be met at finer time resolution, the computational burden of HERON-FARM would linearly increase with the sampling rate, and sub-optimal solutions might be obtained. System responses characterized by overshoots and damped oscillations temporarily violating the imposed constraints might occur during abrupt power transients. In this report, a hierarchical control system architecture for the operation of the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility constructed at INL was proposed. First, the preliminary studies on the proposed control strategy for operating the facility and the designed PI controllers were reviewed. In particular, the current approach for generating the set-point trajectories was studied, and its limits were identified. To this aim, the inclusion of a Supervisory Control layer embedding a modified version of the FARM algorithm for preserving the system safe operation over both long and real-time horizons was proposed. In this way, FARM would be applied twice, i.e., the original version (“FARM Validator”) aiding the solution of the power dispatch problem, and the modified version (“FARM Supervisory” coordinating the PI controllers to address the real-time control tasks. Despite the kernel of the two modules is the same algorithm, their roles, tasks, and capabilities are quite different. A detailed description of the role of FARM at addressing low-level control tasks is provided, along with tentative operational procedures for training the models embedded into the algorithm by using the collected experimental data.

42 ENGINEERING↗

DOE OSTI2022

Elucidating size effects on the yield strength of single-crystal Cu via the Richtmyer–Meshkov instability

Capturing the dynamic response of a material under high strain-rate deformation often demands challenging and time consuming experimental effort. While shock hydrodynamic simulation methods can aid in this area, a priori characterizations of the material strength under shock loading and spall failure are needed in order to parameterize constitutive models needed for these computational tools. Moreover, parameterizations of strain-rate-dependent strength models are needed to capture the full suite of Richtmyer–Meshkov instability (RMI) behavior of shock compressed metals, creating an unrealistic demand for these training data solely on experiments. Herein, we sweep a large range of geometric, crystallographic, and shock conditions within molecular dynamics (MD) simulations and demonstrate the breadth of RMI in Cu that can be captured from the atomic scale. In this work, yield strength measurements from jetted and arrested material from a sinusoidal surface perturbation were quantified as Y RMI = 0.787 ± 0.374 GPa, higher than strain-rate-independent models used in experimentally matched hydrodynamic simulations. Defect-free, single-crystal Cu samples used in MD will overestimate Y RMI , but the drastic scale difference between experiment and MD is highlighted by high confidence neighborhood clustering predictions of RMI characterizations, yielding incorrect classifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants

Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Optimization Algorithm for Criticality Experiment Design Using Whisper

Many criticality experiments performed to aid in nuclear data evaluation are designed to maximize the sensitivity of the system’s effective neutron multiplication factor to a certain nuclide reaction pair over an energy region of interest. This is typically done by evaluating possible designs in a transport code such as MCNP and selecting the one with the highest desired sensitivity. A designer has many tools to try to maximize this sensitivity such as different moderators, reflectors, fuels, and geometries. This balancing act of identifying a critical and maximally sensitive system become very computationally expensive as more variables are added and higher precisions are desired. In order to identify these optimal configurations more efficiently a Particle Swarm Optimization (PSO) algorithm coupled with MCNP has been developed by Los Alamos National Laboratory (LANL). This algorithm has been used to design two upcoming criticality experiments that will be performed at the National Criticality Experiments Research Center (NCERC), located at the Nevada National Security Site, and operated by LANL, the only general-purpose critical experiments laboratory in the United States. PSO uses a population (swarm) of candidate solutions (particles) on a search space of dimensions such as moderator and reflector thicknesses or enrichments and concentrations. These particles move around the search space from generation to generation according to simple rules. Eventually, the swarm converges on the configuration that is both critical and maximally sensitive to a piece of nuclear data. PSO is well suited for criticality experiments as the algorithm is agnostic to the underlying physics, meaning it is effective on many different experimental setups. This algorithm has been modified to maximize the nuclear data similarity coefficient between an application case and an experiment aimed at replicating the application case using WHISPER, a nuclear criticality safety analysis tool. This allows for the efficient design of critical experiments informed by nuclear data sensitives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗