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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring Professor Motivations and Implementations of a Real-World Problem-Solving Project: A Case Study in Preparing Students for the Emerging Building Science Industry: Preprint

Engineering education literature offers a variety of theoretical and conceptual frameworks for project-based learning. This study explores the implementation of real-world problem-solving projects in engineering education. The research team analyzed the motivations and methods behind professors' adoption of such projects through exploratory qualitative interviews with seven professor participants who integrated a nation-wide student competition into their courses. We analyzed the resulting data using a constructivist grounded theory approach to identify key themes of professor practices. Findings reveal that the real-world aspect of the projects and alignment with values and research interests were primary motivators for implementation. While implementation methods varied significantly based on context (i.e., university setting, course type), we found that these projects could be effectively integrated into various classroom settings. The findings support the recommendation for non-academic institutions to develop and manage competitions that can be integrated into classrooms and which offer a point of engagement that is available to professors from a wide range of disciplines.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. Here, to address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Geophysical Impacts and Spectroscopic Identification of a Hydrous Iron Sulfate on Icy Worlds

Over geologic time-scales, large volumes of exogenic sulfur ions from Io's plasma torus have been supplied to the surface of Europa and Ganymede, which, combined with recent interpretations of orbiter images, dynamical modeling, and surface-subsurface exchange, suggests further sulfur transport into the interior of the icy worlds. These observations motivate mixed-phase spectral modeling for interpreting orbiter spectroscopy data and determination of hydration states of candidate surface materials including hydrous sulfates. In this work, we present a combined experimental and theoretical study of the low temperature and high pressure vibrational spectral signature of the iron-sulfate monohydrate endmember, szomolnokite (FeSO 4 ·H 2 O). By employing synchrotron Fourier-transform infrared spectroscopy (FTIR) in the diamond anvil cell up to 23 GPa and down to 20 K, we explore the extreme range of pressure-temperature domains relevant to icy environments throughout our solar system and beyond. Combined with our density-functional theory quantum-mechanics molecular dynamics results, we demonstrate that experimentally observed infrared features in the O-H stretching region commonly associated with nH 2 O (n > 1) hydration states can be attributed to a pure monohydrate without the need for pressure-induced exsolved ice, other coexisting hydrous iron sulfates, or strong overtone and combination modes. We further discuss the possibility of lateral variations in density and shear properties on icy worlds associated with temperature variations and the high-pressure phases of kieserite group monohydrated sulfates.

Geosciences↗
DOE OSTI2026

Nanophotonic waveguide chip-to-world beam scanning

A seamless chip-to-world photonic interface enables broad advancements in optical ranging, display, communication, computation and quantum information science. The ideal solution enables two-dimensional scanning of a diffraction-limited beam from anywhere on a photonic integrated circuit to a large number of resolvable spots. Current beam-scanning technologies are limited by a fundamental trade-off: photonic-integrated-circuits with diffractive optics offer scalability but have poor mode quality, whereas inertially limited micromechanical scanners provide high-quality beams but lack scalable integration. Here we report a photonic ski-jump—a nanoscale waveguide monolithically integrated on a piezoelectric cantilever—to overcome these limitations. It passively curls ~90° out-of-plane within a less-than-0.1 mm 2 footprint, emits a submicrometre, broadband diffraction-limited beam, and exhibits kilohertz-rate mechanical resonances with quality factors of over 10,000. Fabricated in a volume complementary metal–oxide–semiconductor (CMOS) foundry, our device enables scalable two-dimensional beam scanning. Driven on-resonance at CMOS-level voltages, it achieves a footprint-adjusted spot rate of 68.6 mega spots s –1 mm–², exceeding state-of-the-art micro-electro-mechanical systems mirrors by more than 50-fold, which is sufficient for one million pixels at 100 Hz from an approximately 1.5 mm diameter footprint. We demonstrate full-colour image and video projection, and single-photon initialization and readout from silicon vacancy centres in diamond. Finally, by demonstrating uniformity across a 64 ski-jump array, we establish a pathway to achieving greater than one gigaspot resolution at kilohertz rates within a sub-5-cm-diameter footprint, creating a seamless optical pipeline between integrated photonic processors and the free-space world.

Displays↗
DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control

Signalized traffic control problem has been addressed recently with deep Reinforcement Learning (RL) approaches involving diverse state, action, and reward structures. While significant progress has been noted in the literature, open challenges still remain in the areas of adaptive signal phase timing, coordination in a multi-intersection corridor setting, and consideration of real-world traffic conditions. In the context of deep RL-based problem framing, extensions are needed that enable adaptive signal phase timings in an intersection agent's action space, computationally efficient information sharing among neighboring signalized intersection agents along a corridor, and experimentation in realistic simulation environments. In this paper, we develop a deep Advantage Actor Critic (A2C) multi-agent RL (MARL) approach capturing the research extensions above and apply it within a real-world calibrated Aimsun Next traffic corridor simulation model based on traffic data from the City of Coral Gables, Florida. For a multi-intersection corridor control setting, our numerical simulation experiments with a decentralized A2C MARL algorithm applied at different time periods led to a total average corridor travel delay reduction (expressed in seconds/mile averaged over vehicles) from 4.9% to 19.9% compared to state-of-the-art actuated control.

Shuvo, Salman S. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

A Data-Driven Approach to Real-World Degradation of Backsheets

The objectives of this project are as follows: • The population behavior of fielded modules in various conditions of use • Predictions of materials in specific climatic zones • Understanding of a module’s local environment in the field on its degradation It aims to understand how and what backsheet materials of photovoltaic modules degrade in the real world field. • Field Survey Protocol This project is started from the protocol, because all the data, information, domain knowledge are from experience of the real world field surveys, which is based on the protocol. The Protocol is explored from the experience of the field survey observations. With the increasing of the field surveys, it is refined for three versions, which are Task 1.0 (Section 3.1), Task 6.0 (Section 3.6), Task 10.0 (Section 3.10) respectively. It includes a document and a training video, which is able to direct other teams to follow the same procedure with the sites surveyed during this project. The documents include the detailed information but is not limited to the terminology definition, instruments SOP, preparation items for the surveys, form for the data collections, the order of the information collection. The final version of the protocol can be found at Appendix A, see Section 5. Additional, It can also be found at Open Science Framework (OSF), see Section 3.18 for detail. • Written Waiver Request Before staring the field surveys, the request of the waiver for international surveys is completed, because the limitation of climate zone in the United States, see Appendix B in Section 6 for the request documents. Unfortunately, only 1 international site from Taiwan, China can be finished, due to the COVID-19. • Field Survey According to the protocol we built in Section 3.1, 3.6 and 3.10, 41 sites have been surveyed across seven different climate zones (Cfa, Csa, Csb, BSk, Dfa, Dfb, Am). A variety of materials, including Polyethylene Naphthalate (PEN), Polyethylene Terephthalate (PET), Polyvinyl Fluoride (PVF), Polyvinylidene Fluoride (PVDF), Acrylic PVDF, Fluoroethylene Vinyl Ether (FEVE), and Glass, were identified. These sites are located in various states including California, South Carolina, New Mexico, Maryland, Ohio, Tennessee, Florida, Massachusetts, Illinois, Minnesota, Oregon, Colorado, and Taiwan, Republic of China. The ages of the sites ranged from 2 - 38 years in service and the field size varied from 1 MW - 25 MW. All requirements for the modeling have been satisfied. Some observations like ’Edge Effect’ for the rows and Junction box heating will also be a useful knowledge to build the model. Section 3.7 provides detailed information on the sites visited during this reporting period.

14 SOLAR ENERGY↗
DOE OSTI2024

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing the nature's power of entropy increase, this paradigm can solve binary learning problems delivering immense speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. This paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the nature's power of entropy increase to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6970X speedup and 10^5x energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs).

artificial intelligence↗
DOE OSTI2025

Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning

Spatial-Temporal Graph Learning (ST-GL) is a prominent research area due to its unique capability to effectively learn real-world graphs. Applications of ST-GL pose stringent and various demands on not only real-time inference with low energy cost and high ac- curacy but also fast training. Unfortunately, as Moore’s Law approaches its limits and ST-GL model complexity drastically grows, the gap between digital hardware’s computational power and ST- GL application demands is widening. In response, this paper introduces Nature-GL, a nature-powered graph learning paradigm that exploits the principle of entropy increase to advance graph learning. In particular, Nature-GL transforms both the training and inference of real-valued ST-GL into electron-speed natural anneal- ing processes of a parameterized dynamical system that represents the target graphs. Experimental results across four real-world ap- plications with six datasets demonstrate that Nature-GL achieves orders-of-magnitude speedups in both training and inference, delivering higher accuracy compared to Graph Neural Networks.

Liu, Chuan [University of Rochester]↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2024

Nuclear Energy Cost Estimates for Net Zero World Initiative – 2024 Update

This report provides recommended parameters for incorporating nuclear energy systems into decarbonization modeling scenarios. The values are primarily intended for the Net Zero World (NZW) Initiative but are expected to prove useful to other related efforts. Both costs and operational metrics are provided in the study for large reactors and small modular reactors (SMR).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Large deformations of Tr(Φ 3 ) and the world at infinity

The amplitudes of the non-linear sigma model can be obtained from those of Tr(Φ 3 ) theory by sending the kinematic (Mandelstam) variables to infinity in a certain direction. In this paper we characterize the behavior of Tr(Φ 3 ) amplitudes under a general class of large kinematic shifts called g-vector shifts. The objects that live in this world at infinity retain certain key amplitude-like properties, most notably factorization, and admit descriptions in terms of polytopes, but they are not generally amplitudes of any cognizable theory. We identify particular g-vector shifts that lead at infinity to mixed amplitudes involving two pions and any number of scalars, allowing us to provide polytopal descriptions of these amplitudes.

effective field theories↗
DOE OSTI2025

Wormholes with ends of the world

We study classical wormhole solutions in 3D gravity with end-of-the-world (EOW) branes, conical defects, kinks, and punctures. These solutions compute statistical averages of an ensemble of boundary conformal field theories (BCFTs) related to universal asymptotics of OPE data extracted from the 2D conformal bootstrap. Conical defects connect BCFT bulk operators; branes join BCFT boundary intervals with identical boundary conditions; kinks (1D defects along branes) link BCFT boundary operators; and punctures (0D defects) are endpoints where conical defects terminate on branes. We provide evidence for a correspondence between the gravity theory and the ensemble. In particular, the agreement of the g-function dependence results from an underlying topological aspect of the on-shell EOW brane action, from which a BCFT analog of the Schlenker-Witten theorem also follows.

AdS-CFT Correspondence↗
DOE OSTI2025

LandScan HD: a high-resolution gridded ambient population methodology for the world

Unwarned population distributions accounting for routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (LSHD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (approximately 90 m). Although LSHD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LSHD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LSHD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LSHD baseline dataset, we contrast unwarned and residential (WorldPop) population distributions by (1) exploring a practical application of flood risk assessment and (2) evaluating their congruence with outcomes of collective human activities (subnational CO 2 emissions). Finally, we discuss plans to address current LSHD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.

Building morphology↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Refining seasonal performance metrics for room air-conditioning in emerging markets: Integrating building simulations with real-world equipment performance data

Buildings significantly impact worldwide energy consumption, emphasizing the need to reduce the cooling energy demand, especially in warm climates. Minimum energy performance standards (MEPS) and seasonal performance metrics such as the Cooling Seasonal Performance Factor (CSPF) are crucial for improving room air conditioning (RAC) efficiency. However, challenges remain, particularly in emerging markets like Brazil, where seasonal performance metrics have recently been introduced. This study assesses the factors influencing country-level seasonal efficiency metrics and proposes a framework to refine these calculations by considering local climates and expected RAC usage in real-world households via building simulations. Key considerations include outdoor air temperature binning for different climates, RAC usage patterns (i.e., daytime and nighttime usages), envelope thermal performance of households, and urban heat island (UHI) effects. The results reveal that CSPF values can vary significantly based on climate conditions, with observed CSPF ranging from 4.10 to 11.59 Wh/Wh across 577 Brazilian climates. The inclusion of UHI effects led to a reduction in CSPF values by up to 29% during nighttime operations in hot urban areas. Additionally, building envelope efficiency showed contrasting impacts on RAC performance, with CSPFs reaching up to 15.35 Wh/Wh under specific optimized conditions. These findings highlight the need for transparent policymaking in RAC performance databases, facilitating the application of approaches like those proposed in this study and supporting diverse stakeholders in decision-making.

Bavaresco, Mateus↗