Search NASA⌕ Search

SEARCH · Search NASA

Results for “grey-box predictive energy model”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,060 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

How efficiently can AI recognize Wireless Devices?

This poster presents a hardware benchmarking methodology for a 3-layer CNN waveform classifier deployed using ONNX Runtime on an NVIDIA Jetson AGX Orin. The dataset consist of 9 signal types, -30 to +30 dB SNR with 5dB increments. Benchmarking on the Jetson AGX Orin gave an accuracy of 91.9% and GPU throughput of 107,120 predictions/sec (23× faster than CPU). The Jetson GPU reached approximately 27M samples/sec with stable performance but fell below the 40 MHz rate needed for real-time radio feeds. Sustained testing of 5 minutes confirmed stable performance with no memory leaks, establishing a reproducible benchmarking baseline for future edge-deployment optimization.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Modeling of a Four-Stage Linear Ionization Cooling Channel for a Muon Collider in G4Beamline

A previous study of an eight-stage rectilinear ionization cooling channel in the ICOOL software demonstrated a five-order-of-magnitude reduction in a muon beam’s 6D emittance. In this study, we look to compare the ways ICOOL and Muons, Inc.’s g4Beamline software model ionization cooling by comparing their modeling of the first four stages to this optimized cooling channel constructed in ICOOL. We begin by identifying the parameters used to construct the optimized ionization cooling channel in ICOOL. We then reconstruct this beam in g4Beamline with identical parameter specifications and simulate the cooling of an identical input beam. Finally, we compare the two simulations based on their beam transmission, longitudinal emittance, and transverse emittance along the channel length. Through this process, we demonstrate that G4Beamline accurately reproduces transverse cooling results but predicts systematically different longitudinal emittance evolution while maintaining similar overall cooling performance, reproducing a 97.9% reduction in 6D emittance over four stages.

Keeler, Dominic [Purdue U., West Lafayette] (ORCID↗
DOE OSTI2026

Synthetic Tuning of Exciton–Phonon Coupling in Janus WS 2(1-x) Se 2x Monolayers Revealed by Resonant Raman Excitation Spectroscopy for Optoelectronic Applications

Janus monolayers, such as WSSe, have broken out-of-plane symmetry and an intrinsic dipole moment, impacting exciton transport, lifetime, and phonon interactions while imbuing piezoelectric, photocatalytic, and Rashba spin-splitting properties to transition metal dichalcogenides (TMDs). The new properties of this atomically thin material can be used for optoelectronic device applications. As TMDs are converted into Janus monolayers, e.g., top selenization of WS2 to WSSe, the bandgap and structure smoothly evolve, impacting not only the formation of excitons but also their complex interactions with different phonon modes. Resonant Raman excitation profiles (REPs) are uniquely well-suited to reveal both excitonic transitions and exciton–phonon coupling. Here, the resonant REPs of $A^{'}_{1}$ WS 2 and A 1 WSSe modes are measured to understand the strength of their coupling with the A, B, and C excitonic bands of a WS2 monolayer throughout its stepwise transformation into Janus WSSe by pulsed laser deposition (PLD) of energetic selenium species. In situ Raman spectroscopy during deposition is used to controllably prepare stable intermediate Janus structures, WS 2(1-x) Se 2x (0 ≤ x ≤ 0.5), for ex situ measurement of their resonant REPs. As x increases, REPs reveal not only pronounced excitonic bands that gradually shift toward lower photon energies but also strong, mode-selective exciton–phonon coupling. First-principles resonant Raman simulations independently predict this spectral behavior and are shown capable of matching the spectrally broadened, experimentally observed REP profiles in this model system, indicating their strong predictive capability for future experiments. The combination of controlled synthesis, REP characterization, and predictive theory employed here demonstrates a powerful pathway to understand and ultimately tune exciton–phonon interactions for future quantum optical devices.

Janus monolayers↗
DOE OSTI2022

Renewable Energy and Efficiency Technologies in Scenarios of U.S. Decarbonization in Two Types of Models: Comparison of GCAM Modeling and Sector-Specific Modeling

Energy system projections from analytic models inform actions ranging from short-term and local decisions, such as technology and infrastructure deployment, to global and long-term negotiations and targets. Computational limits require the designers of these models to trade off between coverage and resolution. Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses but at a relatively coarse level of resolution. GCAM balances global supply and demand of all energy carriers by endogenously projecting prices for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This global model was used to frame the Long-Term Strategy released by the White House in 2021 and has been used to inform national and global economy-wide decarbonization discussions and strategy development for decades. Other models instead focus on a portion of the energy sector with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects capacity expansion with an emphasis on integration of variable renewable energy into the grid of the future. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices in adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas (GHG) mitigation strategies in the United States. These global and sector-specific modeling approaches can complement each other. The global approach ensures consistent, endogenous energy pricing and resource allocation, which can substantially diverge from current conditions in transformative scenarios, while the sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and tradeoffs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and discusses their potential relevance and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗
DOE OSTI2025

Thermo-Mechanical Phase-Field Modeling of Fracture in High-Burnup UO2 Fuels Under Transient Conditions

This study presents a novel multiphysics phase-field fracture model to analyze high-burnup uranium dioxide (UO2) fuel behavior under transient reactor conditions. Fracture is treated as a stochastic phase transition, which inherently accounts for the random microstructural effects that lead to variations in the value of fracture strength. Moreover, the model takes into consideration the effects of temperature and burnup on thermal conductivity. Therefore, the model is able to predict crack initiation, propagation, and complex morphologies in response to thermal gradients and stress distributions. Several simulations were conducted to investigate the effects of operational and transient conditions on fracture behavior and the resulting cracking patterns. High-burnup fuels exhibit reduced thermal conductivity, elevating temperature gradients and resulting in extensive radial and circumferential cracks. Transient heating rates and temperatures significantly affect fracture patterns, with higher heating rates generating steeper gradients and more irregular crack trajectories. This approach provides critical insights into fuel integrity during accident scenarios and supports the safety evaluation of extended burnup limits.

Chemistry↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2026

Demonstration of gold nanorod systems for enhanced total efficiency: Experimental and numerical analysis

This study investigates the photothermal performance of gold nanorods engineered to exhibit longitudinal plasmon resonances at 695 nm, 780 nm, and 970 nm. The work combines synthesis, structural characterization, extinction measurements, numerical modeling, and controlled temperature experiments to quantify how nanorod geometry, resonance tuning, concentration, and chamber shape jointly influence heat generation. Transmission electron microscopy confirms that increasing nanorod aspect ratio systematically shifts the longitudinal plasmon peak toward the near-infrared region. Extinction measurements show strong agreement with theoretical predictions, validating the numerical model across two independent datasets. Three chamber geometries were tested under laser excitation at 640 nm, 808 nm, and 980 nm: an ascending stepped base, a flat base, and a descending stepped base. Without nanorods, the ascending geometry produced the highest efficiency due to enhanced natural convection. After introducing gold nanorods, all geometries exhibited substantial thermal enhancement, with total efficiencies exceeding 20%. The strongest improvement was obtained for nanorods resonant at 780 nm with a mass concentration of 4.6 mg/mL implemented on the descending stepped-base geometry. This performance resulted from the combined effect of spectral overlapping with the 808 nm laser, the highest nanorod concentration, and localized heat accumulation that intensified buoyancy-driven flow. The findings demonstrate that total efficiency is governed by a synergistic interplay between optical resonance, nanoparticle concentration, and macroscopic chamber design, revealing the system-level coupling between nanoscale plasmonic absorption and macroscale heat-transfer phenomena. The results provide a validated framework for tuning nanoscale plasmonic absorbers and optimizing thermal systems for applications requiring efficient light-to-heat conversion.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
DOE OSTI2024

REDOTHERM (Redox Countercurrent Thermodynamic Limits Model) [SWR-24-88]

REDOTHERM is an open-source, MATLAB-based thermodynamic modeling framework developed to evaluate the performance of redox-active materials for water (H2O) and carbon dioxide (CO2) splitting. It includes models of all major unit operations and supports comparative analysis of different redox-active material candidates. The model is tailored for systems of moving oxide under a parallel/cocurrent flow (PF) and countercurrent flow (CF) configurations. Unvalidated mixed flow reactor (MFR, also known as CSTR) model is also included as an optional addition.

Lidor, Alon [National Renewable Energy Laboratory ↗