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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Metabolic and transcriptomic study of pennycress natural variation identifies targets for oil improvement

Pennycress (Thlaspi arvense L.), a member of the Brassicaceae family, produces seed oil high in erucic acid, suitable for biodiesel and aviation fuel. Although pennycress, a winter annual, could be grown as a dedicated bioenergy crop, an increase in its seed oil content is required to improve its economic competitiveness. The success of crop improvement relies upon finding the right combination of biomarkers and targets, and the best genetic engineering and/or breeding strategies. In this work, we combined biomass composition with metabolomic and transcriptomic studies of developing embryos from 22 pennycress natural variants to identify targets for oil improvement. The selected accession collection presented diverse levels of fatty acids at maturity ranging from 29% to 41%. Pearson correlation analyses, weighted gene co‐expression network analysis and biomarker identifications were used as complementary approaches to detect associations between metabolite level or gene expression and oil content at maturity. The results indicated that improving seed oil content can lead to a concomitant increase in the proportion of erucic acid without affecting the weight of embryos. Processes, such as carbon partitioning towards the chloroplast, lipid metabolism, photosynthesis, and a tight control of nitrogen availability, were found to be key for oil improvement in pennycress. Besides identifying specific targets, our results also provide guidance regarding the best timing for their modification, early or middle maturation. Thus, this work lays out promising strategies, specific for pennycress, to accelerate the successful development of lines with increased seed oil content for biofuel applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

The gradational history of southern Ismenius Lacus

Two epochs of accelerated gradation affected the geomorphic evolution of southern Ismenius Lacus. These periods of enhanced gradational activity were likely related to periods of more clement climate induced by release of either recycled exogenic or juvenile endogenic atmospheric volatiles. Variations in the intensity and duration of gradation during the second epoch are indicated by the variability in the timing of cessation and degree of air fall deposit removal across the study area. Overall intensity of gradation decreased through successive epochs based on: (1) the decreasing diameter at which cumulative statistics from the respective surfaces cease to follow the expected production function; and (2) the decrease in size/increase in density of preserved valley networks incised into surfaces of differing age. A comparable decrease in valley density with time has been noted in the Isidis region.

Grant, J. A.↗
NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗
NASA NTRS2015

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

propulsion simulation↗
DOE OSTI2026

A Measurement of the Pion-Energy Dependence of Muon Neutrino Charged-Current Scattering to Final States With One Charged Pion in NOvA

The study of neutrino oscillations is a main priority for particle physics as the most immediately tractable lever on physics beyond the Standard Model. In particular, more insight into violation of the combined symmetry of charge-conjugation plus parity could yield clues to the origin of matter-antimatter asymmetry, and in some theoretical frameworks the neutrino mass could give insights into dark matter. NOvA is a long-baseline accelerator neutrino experiment with both a near and far detector that seeks to measure several of the parameters of the neutrino mixing matrix, as well as carry out a broad program of additional physics. NOvA has innovated and developed a variety of techniques in the space of neutrino physics, including expanding the use of machine learning techniques in reconstruction. NOvA has also set the stage for the next generation US-based long baseline experiment, DUNE. This dissertation details the creation of a new neutrino interaction vertex reconstruction package for NOvA, which offers enormous improvements in accuracy above the previous vertexer it replaces. This is accomplished using a Convolutional Visual Network trained on large datasets of simulated events. The vertexer is validated thoroughly against data. This dissertation also presents a cross-section measurement for $\nu_\mu + N \rightarrow \mu^- + 1\pi^\pm + X$ (where X does not include additional charged pions) binned in pion kinetic energy, a challenging measurement in general and for NOvA in particular. Measurements like this provide crucial inputs for neutrino interaction models utilized by neutrino oscillation experiments. This measurement in particular is important to the energy ranges relevant for DUNE in a band not well-covered by any other experiment.

Ewart, Erin [Indiana U., Bloomington (main)]↗
DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗
DOE OSTI2026

AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments

The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.

Aney, Noah [Fermilab; U. Chicago (main)]↗
NASA NTRS1993

The MESUR Mission

The MESUR mission will place a network of small, robust landers on the Martian surface, making a coordinated set of observations for at least one Martian year. MESUR presents some major challenges for development of instruments, instrument deployment systems, and on board data processing techniques. The instrument payload has not yet been selected, but the straw man payload is (1) a three-axis seismometer; (2) a meteorology package that senses pressure, temperature, wind speed and direction, humidity, and sky brightness; (3) an alphaproton-X-ray spectrometer (APXS); (4) a thermal analysis/evolved gas analysis (TA/EGA) instrument; (5) a descent imager, (6) a panoramic surface imager; (7) an atmospheric structure instrument (ASI) that senses pressure, temperature, and acceleration during descent to the surface; and (8) radio science. Because of the large number of landers to be sent (about 16), all these instruments must be very lightweight. All but the descent imager and the ASI must survive landing loads that may approach 100 g. The meteorology package, seismometer, and surface imager must be able to survive on the surface for at least one Martian year. The seismometer requires deployment off the lander body. The panoramic imager and some components of the meteorology package require deployment above the lander body. The APXS must be placed directly against one or more rocks near the lander, prompting consideration of a micro rover for deployment of this instrument. The TA/EGA requires a system to acquire, contain, and heat a soil sample. Both the imagers and, especially, the seismometer will be capable of producing large volumes of data, and will require use of sophisticated data compression techniques.

Squyres, S. W.↗
DOE OSTI2026

Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model’s transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures.

Lupo Pasini, Massimiliano [ORNL] (ORCID:0000000249↗
DOE OSTI2022

Detailed analysis of the D-galactose catabolic pathways in Aspergillus niger reveals complexity at both metabolic and regulatory level

The current impetus towards a sustainable bio-based economy has accelerated research to better understand the mechanisms through which filamentous fungi convert plant biomass, a valuable feedstock for biotechnological applications. Several transcription factors have been reported to control the polysaccharide degradation and metabolism of the resulting sugars in fungi. However, little is known about their individual contributions, interactions and crosstalk. D-galactose is a hexose sugar present mainly in hemicellulose and pectin in plant biomass. Here, we study D-galactose conversion by Aspergillus niger and describe the involvement of the arabinanolytic and xylanolytic activators AraR and XlnR, in addition to the D-galactose-responsive regulator GalX. Our results deepen the understanding of the complexity of the filamentous fungal regulatory network for plant biomass degradation and sugar catabolism, and facilitate the generation of more efficient plant biomass-degrading strains for biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Abstract for CRADA among NETL, Mälardalen University, University College Cork (acting through its Tyndall National Institute), and Micro Electricity Generation Association

GeoCoHorT aims to accelerate the transition to 4 th generation district heating and cooling (4GDHC) in Europe and globally, by assessing, optimizing, and demonstrating the integration of shallow geothermal heat extraction with other renewable sources and smart buildings. The target geo-source is ground water from River Shannon (Limerick, Ireland), which will provide both an efficiency and noise-reduction benefit to the micro-district and a method of combatting climate-change-warming of the Shannon Estuary. The heat from this low-temperature source will be recycled and supplied to a smart district located in Limerick, Ireland, through heat pumps powered by renewable electricity and by means of a suitable DH network design. The mandate is to find solutions that work for entire communities in a fair and sustainable manner, with the involvement of the communities themselves to highlight their needs. The project brings together four partners from Ireland, Sweden, and the U.S.A. with strong multi-disciplinary competencies. The Tyndall Institute at University of Cork (UCC) and Mälardalen University (MDU) will design and optimize the 4GDH micro-district system, and the Micro Electricity Generation Association (MEGA) will work with the community to build a sense of ownership of the Climate Challenge and ensure close community involvement. The National Energy Technology Laboratory of the U.S. Department of Energy (NETL) will design the prospective geo-fluid loop to optimize heat-extraction effectiveness with ecological safety and assess the environmental benefits of the proposed solutions. Industrial advisors (Eskilstuna Strängnäs Energi och Miljö (ESEM) and Mimer in Sweden, and Smart MPOWER in Ireland) will steer the technology development to meet the needs of public utilities and consumers. Current infrastructure in the smart district in Limerick (demonstration site of an EU Lighthouse project) will be exploited and modified with support of MEGA, UCC, and MPOWER to integrate micro-DH from low-temperature heat sources, such as the river heat, and to allow increasing penetration of prosumers in the grid. Additional case studies will be developed with the help of industrial advisors to extend the results to other regions (e.g., Sweden and USA) and provide valuable insights for the development of 4GDH worldwide.

15 GEOTHERMAL ENERGY↗
NASA NTRS2016

The Nexus for Exoplanet System Science

NExSS is a research coordination network dedicated to the study of planetary habitability. A NASA cross-division initiative bringing astrophysicists, planetary scientists, Earth scientists, and heliophysicists together to bring a systems science approach to this problem. NExSS's goals being to investigate the diversity of planets, understanding how planet history, geology, and climate interact to create the conditions for life. Also, to put planets into an architectural context as stellar systems built over time by dynamical processes and sculpted by stars. Use experience from solar system (including Earth) history to identify where habitable niches are most likely to occur and which planets are most likely to be habitable. Leverage NASA investments in research and missions to accelerate discovery and characterization of potential life-bearing worlds.

coordination↗
DOE OSTI2023

Exponential acceleration of macroscopic quantum tunneling in a Floquet Ising model

The exponential suppression of macroscopic quantum tunneling (MQT) in the number of elements to be reconfigured is an essential element of broken symmetry phases. This suppression is also a core bottleneck in quantum algorithms, such as traversing an energy landscape in optimization, and adiabatic state preparation more generally. In this work, we demonstrate exponential acceleration of MQT through Floquet engineering with the application of a uniform, high frequency transverse drive field. Using the ferromagnetic phase of the transverse field Ising model in one and two dimensions as a prototypical example, we identify three phenomenological regimes as a function of drive strength. For weak drives, the system exhibits exponentially decaying tunneling rates but robust magnetic order; in the crossover regime at intermediate drive strength, we find polynomial decay of tunnelling alongside vanishing magnetic order; and at very strong drive strengths both the Rabi frequency and time-averaged magnetic order are approximately constant with increasing system size. We support these claims with extensive full wavefunction and tensor network numerical simulations, and theoretical analysis. An experimental test of these results presents a technologically important and novel scientific question accessible on NISQ-era quantum computers.

Grattan, George↗
DOE OSTI2022

Agile Acceleration of LLVM Flang Support for Fortran 2018 Parallel Programming

The LLVM Flang compiler ("Flang") is currently Fortran 95 compliant, and the frontend can parse Fortran 2018. However, Flang does not have a comprehensive 2018 test suite and does not fully implement the static semantics of the 2018 standard. We are investigating whether agile software development techniques, such as pair programming and test-driven development (TDD), can help Flang to rapidly progress to Fortran 2018 compliance. Because of the paramount importance of parallelism in high-performance computing, we are focusing on Fortran’s parallel features, commonly denoted “Coarray Fortran.” We are developing what we believe are the first exhaustive, open-source tests for the static semantics of Fortran 2018 parallel features, and contributing them to the LLVM project. A related effort involves writing runtime tests for parallel 2018 features and supporting those tests by developing a new parallel runtime library: the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine).

Rasmussen, Katherine↗
DOE OSTI2024

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗