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

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Progressive Response Surfaces

Response surface functions are often used as simple and inexpensive replacements for computationally expensive computer models that simulate the behavior of a complex system over some parameter space. Progressive response surfaces are ones that are built up progressively as global information is added from new sample points in the parameter space. As the response surfaces are globally upgraded based on new information, heuristic indications of the convergence of the response surface approximation to the exact (fitted) function can be inferred. Sampling points can be incrementally added in a structured fashion, or in an unstructured fashion. Whatever the approach, at least in early stages of sampling it is usually desirable to sample the entire parameter space uniformly. At later stages of sampling, depending on the nature of the quantity being resolved, it may be desirable to continue sampling uniformly over the entire parameter space (Progressive response surfaces), or to switch to a focusing/economizing strategy of preferentially sampling certain regions of the parameter space based on information gained in early stages of sampling (Adaptive response surfaces). Here we consider Progressive response surfaces where a balanced indication of global response over the parameter space is desired.We use a variant of Moving Least Squares to fit and interpolate structured and unstructured point sets over the parameter space. On a 2-D test problem we compare response surface accuracy for three incremental sampling methods: Progressive Lattice Sampling; Simple-Random Monte Carlo; and Halton Quasi-Monte-Carlo sequences. We are ultimately after a system for constructing efficiently upgradable response surface approximations with reliable error estimates.

Romero, V. J.↗
NASA NTRS2016

Relationships Between Vestibular Measures as Potential Predictors for Spaceflight Sensorimotor Adaptation

Introduction: During space exploration missions astronauts are exposed to a series of novel sensorimotor environments, requiring sensorimotor adaptation. Until adaptation is complete, sensorimotor decrements occur, affecting critical tasks such as piloted landing or docking. Of particularly interest are locomotion tasks such as emergency vehicle egress or extra-vehicular activity. While nearly all astronauts eventually adapt sufficiently, it appears there are substantial individual differences in how quickly and effectively this adaptation occurs. These individual differences in capacity for sensorimotor adaptation are poorly understood. Broadly, we aim to identify measures that may serve as pre-flight predictors of and individual's adaptation capacity to spaceflight-induced sensorimotor changes. As a first step, since spaceflight is thought to involve a reinterpretation of graviceptor cues (e.g. otolith cues from the vestibular system) we investigate the relationships between various measures of vestibular function in humans. Methods: In a set of 15 ground-based control subjects, we quantified individual differences in vestibular function using three measures: 1) ocular vestibular evoked myogenic potential (oVEMP), 2) computerized dynamic posturography and 3) vestibular perceptual thresholds. oVEMP responses are elicited using a mechanical stimuli approach. Computerized dynamic posturography was used to quantify Sensory Organization Tests (SOTs), including SOT5M which involved performing pitching head movements while balancing on a sway-reference support surface with eyes closed. We implemented a vestibular perceptual threshold task using the tilt capabilities of the Tilt-Translation Sled (TTS) at JSC. On each trial, the subject was passively roll-tilted left ear down or right ear down in the dark and verbally provided a forced-choice response regarding which direction they felt tilted. The motion profile was a single-cycle sinusoid of angular acceleration with a duration of 5 seconds (frequency of 0.2 Hz), which was selected as it requires sensory integration of otolith and semicircular canal cues. Stimuli direction was randomized and magnitude was determined using an adaptive sampling procedure. One hundred trials were provided and each subject's responses were fit with a psychometric curve to estimate the subject's threshold. Results: Roll tilt perceptual thresholds at 0.2 Hz ranged from 0.5 degrees to 1.82 degrees across the 15 subjects (geometric mean of 1.04 degrees), consistent with previous studies. The inter-individual variability in thresholds may be able to help explain individual differences observed in sensorimotor adaptation to spaceflight. Analysis is ongoing for the oVEMPS and computerized dynamic posturography to identify relationships between the various vestibular measures. Discussion: Predicting individual differences in sensorimotor adaptation is critical both for the development of personalized countermeasures and mission planning. Here we aim to develop a basis of vestibular tests and parameters which may serve as predictors of individual differences in sensorimotor adaptability through studying the relationship between these measures.

Clark, T. K.↗
DOE OSTI2025

Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)

Convective clouds play an important role in Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region’s mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system

Functionally graded materials have the potential to improve upon monolithic parts by locally tailoring compositions to surrounding environmental conditions. Difficulties arise when designing composition gradients as incompatible materials can result in detrimental phase formation and failure of the gradient joint. As many alloys are multi-component, designing a composition gradient free of detrimental phases is difficult due to the large composition space available to explore. A framework was developed that improves the path planning algorithm and surrogate models with adaptive sampling schemes specific to their problem definition. A cost function was created to minimize a property (such as cracking susceptibility) along a path. This framework was applied to the Mo-Nb-Ta-Ti system as a case study to showcase the efficiency in building the surrogate models and in iterating different optimal compositionally graded paths.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated statistical failure analysis of multifidelity TRISO fuel models

Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites

β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand–protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Effect of Geometry, Spin, and Orbital Optimization in Achieving Accurate, Correlated Results for Iron–Sulfur Cubanes

Iron–sulfur clusters comprise an important functional motif in the catalytic centers of biological systems, capable of enabling important chemical transformations at ambient conditions. This remarkable capability derives from a notoriously complex electronic structure that is characterized by a high density of states that is sensitive to geometric changes. The spectral sensitivity to subtle geometric changes has received little attention from correlated, large active space calculations, owing partly to the exceptional computational complexity for treating these large and correlated systems accurately. Here, to provide insight into this aspect, we report the first Complete Active Space Self Consistent Field (CASSCF) calculations for different geometries of the [Fe(II/III) 4 S 4 (SMe) 4 ] -2 clusters using two complementary, correlated solvers: spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density Matrix Renormalization Group (DMRG). We find that the previously established picture of a double-exchange driven magnetic structure, with minute energy gaps (<1 mHa) between consecutive spin states, has a weak dependence on the underlying geometry. However, the spin gap between the singlet and the spin state 2S + 1 = 19, corresponding to a maximal number of Fe-d electrons being unpaired and of parallel spin, is strongly geometry dependent, changing by a factor of 3 upon slight deformations that are still within biologically relevant parameters. The CASSCF orbital optimization procedure, using active spaces as large as 86 electrons in 52 orbitals, was found to reduce this gap compared to typical mean-field orbital approaches. Our results show the need for performing large active space calculations to unveil the challenging electronic structure of these complex catalytic centers and should serve as accurate starting points for fully correlated treatments upon inclusion of dynamical correlation outside the active space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III

Autonomous experiments rely on the integration of control, data acquisition, analysis, and decision-making frameworks. While such systems have been demonstrated at individual facilities, adapting them to additional instruments remains challenging due to differences in local infrastructure. We present a modular workflow that connects existing open-source tools for data access (Tiled), workflow orchestration (Prefect), analysis and visualization (pyFAI, Plotly Dash), and Gaussian-process-based adaptive sampling (gpCAM) into a unified framework for autonomous scattering experiments. The same configuration operates across two synchrotron beamlines (ALS 7.3.3 and PETRA III P03) with only minimal facility-specific adjustments, as shown in proof-of-concept demonstrations. This validates that a consistent design emphasizing modularity and shared interfaces can ease deployment across diverse experimental environments. The resulting framework provides a flexible foundation for extending autonomous control and analysis capabilities beyond a single beamline or instrument.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Simulation of adiabatic quantum computing for molecular ground states

Quantum computation promises to provide substantial speedups in many practical applications with a particularly exciting one being the simulation of quantum many-body systems. Adiabatic state preparation (ASP) is one way that quantum computers could recreate and simulate the ground state of a physical system. In this paper, we explore a novel approach for classically simulating the time dynamics of ASP with high accuracy and with only modest computational resources via an adaptive sampling configuration interaction scheme for truncating the Hilbert space to only the most important determinants. We verify that this truncation introduces negligible error and use this new approach to simulate ASP for sets of small molecular systems and Hubbard models. Furthermore, we examine two approaches to speeding up ASP when performed on quantum hardware: (i) using the complete active space configuration interaction (CASCI) wave function instead of the Hartree–Fock initial state and (ii) a nonlinear interpolation between the initial and target Hamiltonians. We find that starting with a CASCI wave function with a limited active space yields substantial speedups for many of the systems examined, while nonlinear interpolation does not. In additional, we observe interesting trends in the minimum gap location (based on the initial state) as well as how state preparation time can depend on certain molecular properties, such as the number of valence electrons. Importantly, we find that the required state preparation times do not show an immediate exponential wall that would preclude an efficient run of ASP on actual hardware.

Kremenetski, Vladimir↗
DOE OSTI2022

Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment

Abstract Self-Piercing Riveting (SPR) is one of the most commonly used methods for joining dissimilar materials in the automotive industry. These joints are popular due to their adaptability, high performance and short cycle time. However, since these joints involve two dissimilar materials, they are susceptible to galvanic corrosion in the presence of an electrolyte which is driven by the difference in the equilibrium potential of the metals. This can affect the safety and resilience of these joints. In this paper, we focus on galvanic corrosion in Al-Fe SPR joints. A Machine learning (ML) based surrogate model, which is based off of FE simulations, for statistical corrosion analysis is developed. This model enables the resilience and reliability analysis of SPR joints under corrosion environment. In this study, first a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode of a SPR joint. This model takes into account the effect of the crystal microstructure of the Al anode and the precipitation of the corrosion product. Several geometric and environmental factors including crevice gap, roughness of anode, conductivity, pH and the temperature of the electrolyte that effect corrosion are investigated. A thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al SPR joints using a novelistic Probabilistic Confidence-Based Adaptive Sampling (PCAS) technique. PCAS is used to train the surrogate model by identifying the critical sampling points and thus reducing the overall computational costs. It is found that the electrolyte temperature has the largest effects on the material loss and needs to be managed closely for better corrosion control. By understanding the corrosion performance and resultant uncertainty impact on SPR joints, the reliability and resilience of these joints can be improved.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Many-Body Adaptive Configuration Interaction Suite (MACIS) v0.1.0

The Many-Body Adaptive Configuration Interaction Suite (MACIS) is a modern C++ software infrastructure for the development of selected-configuration interaction (sCI) methods for the fermionic many-body problem in quantum chemistry and materials science. MACIS provides a high-performance, parallel implementation of the Adaptive Sampling Configuration Interaction (ASCI) method as related solvers such as self-consistent orbital optimization and the evaluation of excited states and spectral functions. Unlike existing sCI software, MACIS is developed as a modular software infrastructure which allows it to exist both as a standalone sCI solver and as a library to integrate into existing many-body codes. This modularity allows MACIS to be quickly extensible, including its integration with other sCI methods and high-performance solvers.

Williams-Young, David↗
DOE OSTI2024

Efficient training of physics-informed neural networks

Open-source software package designed for the efficient training of Physics-Informed Neural Networks (PINNs) and their variants, integrating advanced methodologies such as adaptive weighting and adaptive sampling

Chen, Wenqian [Pacific Northwest National Laborato↗
DOE OSTI2026

GP Cosmology Surrogate v1.0

GP Cosmology Surrogate is a Python library for building and training a generalized multi-output Gaussian process (GP) framework of @takhtaganov2021cosmic. In this approach, the surrogate is constructed sequentially, guided by a Bayesian optimization acquisition function that targets reduction of emulation error in the regions most consistent with the observational data. This adaptive design concentrates computational resources where they have the greatest impact on inference accuracy. The library supports efficient training for separable GP kernels, which allows the use of Kronecker algebra to handle high-dimensional input spaces and large numbers of correlated outputs. This makes it well suited for applications such as modeling cosmological power spectra, large-scale physical simulations, and multi-output hyperparameter tuning. By combining scalable multi-output GP modeling with data-driven adaptive sampling, GPsurrogate enables parameter inference and optimization with substantially fewer simulations than conventional space-filling designs.

Lukic, Zarija [Lawrence Berkeley National Laborato↗
DOE OSTI2023

CSAPR2 Optimized Convective Cell Tracking Data during TRACER

One of the challenges in analyzing convective cell properties is to observe the quick evolution of individual convective cells. While the operational radar data provide a volumetric data set to analyze radar observables of convective precipitation clouds, previous studies also suggested the quick evolution of cell life cycle that might not be captured by conventional radar volume scan strategies that take ~5-7 minutes. Aiming at enhancing our understanding of the links between convective cloud kinematic and microphysical processes as well as life cycles, the Tracking Aerosol Convection Interactions ExpeRiment (TRACER; Jensen et al. 2019) was conducted at Houston, Texas, in 2022. The TRACER campaign deployed the 2nd generation C-band Scanning ARM Precipitation Radar (CSAPR2), which performed frequent updates of range height indicator (RHI) and sector plan position indicator (PPI) scans to track individual convective cells every < 2 minutes, guided by a new cell tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. We provide the processed CSAPR2 cell tracking data in CfRadial format collected during the TRACER field campaign from June to September 2022. The data files include processed radar variables: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification. Figure 1 provides an example of a 3D image of CSAPR2 reflectivity from the lowest PPI scan and an RHI scan after data processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

MULTI-LEADER: MULTI-source LEarning-Accelerated Design of high-Efficiency multi-stage compRessor (Final Technical Report)

The objective of MULTI-LEADER is to cut design costs by 80% while generating more energy-efficient designs of multi-stage compressors by developing and implementing novel machine learning (ML) techniques, which enable faster and fewer design iterations, improved solver performance, and concurrent multi-disciplinary design. Current industrial practices for the design of multi-stage compressors involve simulation-based design optimization with successive levels of model fidelity, iteratively evaluated between distinct disciplines, one stage at a time to tackle the high dimensional design variations. This project addresses these key design challenges: (1) concurrent optimization of multiple stages under many non-linear constraints; (2) multitude of evaluation of high-fidelity and expensive solvers and their gradients during optimization convergence in high-dimensional design; (3) multi-disciplinary design to maximize aerodynamic performance while guaranteeing structural integrity and additive manufacturability; (4) utilization of multiple fidelity of solvers with disparate parameterization and modeling assumptions. MULTI-LEADER achieved more than 5x speed up in detailed design of more energy-efficient compressors via these machine learning (ML) innovations: (i) rapid design surrogates by multi-source learning from diverse fidelities across multiple disciplines, (ii) physics-constrained data-augmented modeling for improved empiricism, (iii) generative manifold embedding for high dimensional concurrent design without gradient information; (iv) budget-constrained fidelity-adaptive sampling towards fewer design iterations.

33 ADVANCED PROPULSION SYSTEMS↗