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DOE OSTI2024

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Short-term Interventions for Long-term Change: Spreading Stable Green Norms in Networks

Strong empirical evidence suggests that people infer prevailing pro-environmental norms based on the behavior of people they encounter and engage with. These norms seem to be adopted in response to both internal motivation and social pressure. To formalize such behavior, the economic literature has introduced theoretical models that include moral and social drivers. We complement this theoretical literature by analyzing the adoption of green behavior in presence of social networks. Leveraging insights from the network-science literature, we extend an existing model of socially contingent moral motivation to include characteristics of human social behavior that have been shown, empirically, to matter for green behavior, but which have been neglected by most theoretical models. Our network moral-motivation model leads naturally to spatial-heterogeneity in environmental norms. Consistent with non-network models, we show that temporary subsidy can lead to stable equilibria with positive adoption, even when the subsidy is discontinued. In our model, however, regulators can achieve significant savings by targeting subsidies. With our computational exercises, using small semi-realistic networks, we quantify the gains of targeting subsidies, or social interventions, towards optimal seed groups. These gains may be large compared to widespread subsidies, or random selection of seed groups, and depend on the society’s structural characteristics. Furthermore, considering social networks may change radically the performance of initiatives aimed at promoting the adoption of green behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2005

Visualization Methods for Viability Studies of Inspection Modules for the Space Shuttle

An effective simulation of an object, process, or task must be similar to that object, process, or task. A simulation could consist of a physical device, a set of mathematical equations, a computer program, a person, or some combination of these. There are many reasons for the use of simulators. Although some of the reasons are unique to a specific situation, there are many general reasons and purposes for using simulators. Some are listed but not limited to (1) Safety, (2) Scarce resources, (3) Teaching/education, (4) Additional capabilities, (5) Flexibility and (6) Cost. Robot simulators are in use for all of these reasons. Virtual environments such as simulators will eliminate physical contact with humans and hence will increase the safety of work environment. Corporations with limited funding and resources may utilize simulators to accomplish their goals while saving manpower and money. A computer simulation is safer than working with a real robot. Robots are typically a scarce resource. Schools typically don t have a large number of robots, if any. Factories don t want the robots not performing useful work unless absolutely necessary. Robot simulators are useful in teaching robotics. A simulator gives a student hands-on experience, if only with a simulator. The simulator is more flexible. A user can quickly change the robot configuration, workcell, or even replace the robot with a different one altogether. In order to be useful, a robot simulator must create a model that accurately performs like the real robot. A powerful simulator is usually thought of as a combination of a CAD package with simulation capabilities. Computer Aided Design (CAD) techniques are used extensively by engineers in virtually all areas of engineering. Parts are designed interactively aided by the graphical display of both wireframe and more realistic shaded renderings. Once a part s dimensions have been specified to the CAD package, designers can view the part from any direction to examine how it will look and perform in relation to other parts. If changes are deemed necessary, the designer can easily make the changes and view the results graphically. However, a complex process of moving parts intended for operation in a complex environment can only be fully understood through the process of animated graphical simulation. A CAD package with simulation capabilities allows the designer to develop geometrical models of the process being designed, as well as the environment in which the process will be used, and then test the process in graphical animation much as the actual physical system would be run . By being able to operate the system of moving and stationary parts, the designer is able to see in simulation how the system will perform under a wide variety of conditions. If, for example, undesired collisions occur between parts of the system, design changes can be easily made without the expense or potential danger of testing the physical system.

Mobasher, Amir A.↗

NASA NTRS2009

2009 Continued Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by Hamilton Sundstrand and baselined for the Orion Atmosphere Revitalization System (ARS). In three previous years at this conference, reports were presented on extensive Johnson Space Center (JSC) testing of this technology in a sea-level pressure environment with simulated and real human metabolic loads in both open and closed-loop configurations. The test article design was iterated a third time before the latest series of such tests, which was performed in the first half of 2009. The new design incorporates a canister configuration modification for overall unit compactness and reduced pressure drop, as well as a new process flow control valve that incorporates both compressed gas purge and dual-end vacuum desorption capabilities. This newest test article is very similar to the flight article designs. Baseline tests of the new unit were performed to compare its performance to that of the previous test articles. Testing of compressed gas purge operations helped refine launchpad operating condition recommendations developed in earlier testing. Operating conditions used in flight program computer models were tested to validate the model projections. Specific operating conditions that were recommended by the JSC test team based on past test results were also tested for validation. The effects of vacuum regeneration line pressure on resulting cabin conditions was studied for high metabolic load periods, and a maximum pressure is recommended.

Button, Amy Lin↗

NASA NTRS2010

2009 Continued Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by Hamilton Sundstrand and baselined for the Orion Atmosphere Revitalization System (ARS). In three previous years at this conference, reports were presented on extensive Johnson Space Center (JSC) testing of this technology in a sea-level pressure environment, with simulated and real human metabolic loads, in both open and closed-loop configurations. The test article design was iterated a third time before the latest series of such tests, which was performed in the first half of 2009. The new design incorporates a canister configuration modification for overall unit compactness and reduced pressure drop, as well as a new process flow control valve that incorporates both compressed gas purge and dual-end vacuum desorption capabilities. This newest test article is very similar to the flight article designs. Baseline tests of the new unit were performed to compare its performance to that of the previous test articles. Testing of compressed gas purge operations helped refine launchpad operating condition recommendations developed in earlier testing. Operating conditions used in flight program computer models were tested to validate the model projections. Specific operating conditions that were recommended by the JSC test team based on past test results were also tested for validation. The effects of vacuum regeneration line pressure on resulting cabin conditions was studied for high metabolic load periods, and a maximum pressure is recommended

Button, Amy B.↗

NASA NTRS2018

Simulated Water Well Performance on Mars

The surface of Mars once had abundant water flowing on its surface, but now there is a general perception that this surface is completely dry. Several lines of research have shown that there are sources of potentially large quantities of water at many locations on the surface, including regions considered as candidates for future human missions. Recent discovery of exposed water ice scarps in Martian mid-latitudes has bolstered the evidence for massive amounts of almost pure water in these regions. These favorable indications of massive quantities of water have initiated studies of changes that could be made to human Mars missions if a means could be devised that would make this water available to these crews. The proposed paper will describe progress towards developing one approach for accessing and extracting water from these mid-latitude sources. This approach relies on mechanical drills to access the water ice through overlying debris. Once the ice has been accessed, a technique known as a Rodriguez Well is used to melt the ice, store the resulting water until it is needed, and then pump the water to the surface for use. Previous work in this area has utilized a computer simulation to predict the performance of the Rodriguez Well. This simulation was developed originally to predict performance in terrestrial polar regions. While the basic approach used in this model is appropriate for a similar well on Mars, several parameters were known to require a change to correctly model the Martian environment. Some of these parameters are empirical and require experiments simulating the Martian environment to determine their value. The proposed paper will describe the experiments set up to determine the value of these parameters and compare their numerical value to the terrestrial equivalent. Finally, the proposed paper will show results from the updated computer simulation and compare results with those determined from the original version of the simulation.

water↗

DOE OSTI2012

Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools

Background: MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results: BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. Conclusions: BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization

Dense LC-MS datasets have convoluted extracted ion chromatograms with multiple chromatographic peaks that cloud the differentiation between intact compounds with their overlapping isotopic distributions, peaks due to insource ion fragmentation, and noise. Making this differentiation is critical in glycomics datasets because chromatographic peaks correspond to different intact glycan structural isomers. The GlyQ-IQ software is targeted chromatography centric software designed for chromatogram and mass spectra data processing and subsequent glycan composition annotation. The targeted analysis approach offers several key advantages to LC-MS data processing and annotation over traditional algorithms. A priori information about the individual target’s elemental composition allows for exact isotope profile modeling for improved feature detection and increased sensitivity by focusing chromatogram generation and peak fitting on the isotopic species in the distribution having the highest intensity and data quality. Glycan target annotation is corroborated by glycan family relationships and in source fragmentation detection. The GlyQ-IQ software is developed in this work (Part 1) and was used to profile N-glycan compositions from human serum LC-MS Datasets. The companion manuscript GlyQ-IQ Part 2 discusses developments in human serum N-glycan sample preparation, glycan isomer separation, and glycan electrospray ionization. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad N-glycan profile from a high resolution (100K/60K) nESI-LS-MS/MS dataset including CID and HCD fragmentation acquired on a Velos Pro Mass spectrometer. 101 glycan compositions and 353 isomer peaks were detected from a single sample. 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high resolution mass spectra and mass accuracies less than 7 ppm.

Environmental Molecular Sciences Laboratory↗

NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗

DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗

NASA NTRS2012

HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

Hua, Hook↗

NASA NTRSDate not supplied

Closed-Loop Simulations of Human-Scale Mars Lander Descent Trajectories on Frontier

A computational campaign was performed to run high-fidelity, free-flight simulations of a human-scale Mars lander concept vehicle decelerating under retropropulsion through the Martian atmosphere with closed-loop flight control. A novel approach is used to couple computational fluid dynamics (CFD) software with a mature flight mechanics package, where the two applications communicate in real-time across two geographically-dispersed computational facilities. The CFD is performed on the Frontier exascale system located at Oak Ridge National Laboratory, and the flight mechanics are executed on a system located at NASA Langley Research Center. In the current campaign, CFD is performed using finite-rate chemistry to account for the interactions between the LOXCH 4 engines and the CO 2 Martian atmosphere. A simulation of a closed-loop main engine throttling and RCS actuation is presented, demonstrating that the vehicle and model are able to maintain stability in a long-duration CFD-in-the-loop flight simulation. Comparisons are made to a reduced order model ignoring aero-propulsive interactions.

CFD↗

DOE OSTI2024

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1992

Purposive discovery of operations

The Generate, Prune & Prove (GPP) methodology for discovering definitions of mathematical operators is introduced. GPP is a task within the IL exploration discovery system. We developed GPP for use in the discovery of mathematical operators with a wider class of representations than was possible with the previous methods by Lenat and by Shen. GPP utilizes the purpose for which an operator is created to prune the possible definitions. The relevant search spaces are immense and there exists insufficient information for a complete evaluation of the purpose constraint, so it is necessary to perform a partial evaluation of the purpose (i.e., pruning) constraint. The constraint is first transformed so that it is operational with respect to the partial information, and then it is applied to examples in order to test the generated candidates for an operator's definition. In the GPP process, once a candidate definition survives this empirical prune, it is passed on to a theorem prover for formal verification. We describe the application of this methodology to the (re)discovery of the definition of multiplication for Conway numbers, a discovery which is difficult for human mathematicians. We successfully model this discovery process utilizing information which was reasonably available at the time of Conway's original discovery. As part of this discovery process, we reduce the size of the search space from a computationally intractable size to 3468 elements.

Sims, Michael H.↗

NASA NTRS2006

Using Apex To Construct CPM-GOMS Models

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

John, Bonnie↗

NASA NTRS2011

A Transfer of Training Study of Control Loader Dynamics

The control inceptor used in a simulated vehicle is an important part in maintaining the fidelity of a simulation. The force feedback provided by the control inceptor gives the operator important cues to maintain adequate performance. The dynamics of a control inceptor are typically based on a second order spring mass damper system with damping, force gradient, breakout force, and natural frequency parameters. Changing these parameters can have a great effect on pilot or driver control of the vehicle. The neuromuscular system has a very important role in manipulating the control inceptor within a vehicle. Many studies by McRuer, Aponso, and Hess have dealt with modeling the neuromuscular system and quantifying the effects of a high fidelity control loader as compared to a low fidelity control loader. Humans are adaptive in nature and their control behavior changes based on different control loader dynamics. Humans will change their control behavior to maintain tracking bandwidth and minimize tracking error. This paper reports on a quasi-transfer of training experiment which was performed at the NASA Langley Research Center. The quasi transfer of training study used a high fidelity control loader and a low fidelity control loader. Subjects trained in both simulations and then were transferred to the high fidelity control loader simulation. The parameters for the high fidelity control loader were determined from the literature. The low fidelity control loader parameters were found through testing of a simple computer joystick. A disturbance compensatory task is employed. The compensatory task involves implementing a simple horizon out the window display. A disturbance consisting of a sum of sines is used. The task consists of the subject compensating for the disturbance on the roll angle of the aircraft. The vehicle dynamics are represented as 1/s and 1/s2. The subject will try to maintain level flight throughout the experiment. The subjects consist of non-pilots to remove any effects of pilot experience. First, this paper discusses the implementation of the disturbance compensation task. Second, the high and low fidelity parameters used within the experiment are presented. Finally, an explanation of results from the experiments is presented.

Cardullo, Frank M.↗

NASA NTRSDate not supplied

Developing New Tools for Modeling Rocket Plume-Surface Interactions

With NASA’s goal to land the next human on the lunar surface in the next few years, it has become vitally important that we have a better understanding of how future landing spacecraft will interact with the unique properties of regolith¬¬––the layer of loose, unconsolidated dust and rock on the lunar surface¬¬––which can cause hazards like visual obstructions, particulate clouds, and cratering of the landing zone. Researchers from the Fluid Dynamics Branch at NASA’s Marshall Space Flight Center are performing plume-surface interaction (PSI) simulations between lander engine plumes and unprepared regolith surfaces, and have developed new tools to provide predictive PSI environments for various NASA projects and missions, including the Human Lander System (HLS), Commercial Lunar Payload Services (CLPS), and future Mars landers. These tools allow the researchers to determine how to best meet the simulation and time requirements for each project by varying model fidelity. The highest fidelity tool is the Gas Granular Flow Solver (Loci/GGFS) that models gas-particle multi-phase interactions to predict regolith cratering and ejection of particles into the immediate surroundings of the lander. At its highest fidelity, it can model microscopic regolith particle interactions with a particle size/shape distribution that statistically replicates actual regolith, however, to be most effective with today’s computing resources, it is currently run using only one to three equivalent particle sizes/shapes. The team also incorporated engineering models into their software suite to create production-ready hybrid tools with reduced fidelity. At the lowest fidelity, the computational fluid dynamics (CFD) code Loci/CHEM+DIGGEM can predict crater depth over time by relating local CFD-predicted surface shear stresses to a model of erosion mass flux.

plume surface interaction↗

DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗