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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2025

Investigating the Vulnerabilities of the Direct Transfer Trip Scheme for Network Protector Units in the Secondary Networks of Electric Power Distribution Grids

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection.

direct transfer trip↗
DOE OSTI2025

Welch Method and Bootstrapping Applied to Subcritical Gamma Noise

We measured the prompt neutron decay constant 𝛼 of the CROCUS zero-power reactor at the Swiss Federal Institute of Technology Lausanne using cross-power spectral density (CPSD) analysis of gamma-gamma correlations from two trans-stilbene organic scintillators positioned near the reactor core. We measured critical and subcritical states, with water levels ranging from 960 mm (critical) to 800 mm (𝜌=−1.4 $ subcritical). Our analysis used the Welch method, dividing signal segments for fast Fourier transform (FFT) frequency analysis and applying bootstrapping uncertainty quantification that uses Welch-defined segments. Results demonstrated a clear increase in the measured 𝛼 as reactor reactivity decreased, distinguishing critical from subcritical conditions. At the 960-mm critical level, 𝛼 was estimated at 155.9 ± 0.7 s −1 , and for the 800-mm subcritical level, 𝛼 increased significantly to 367.3 ± 6.9 s –1 . A linear regression of subcritical states yielded a critical estimate of 154.0 ± 3.1 s –1 , aligning with the static 𝛼 estimate at critical. The bootstrapping method produced normally distributed 𝛼 estimates, confirming data consistency. The gamma CPSD 𝛼 estimates clearly distinguish reactor states and improve monitoring of zero-power reactors. The future deployment of modular and microreactors as potential candidates for noise analysis is demonstrated in CROCUS, particularly zero-power mock-ups of new designs. The improvement of noise analysis in the subcritical domain from this work will support experimental data for reactor deployment and procedure.

CROCUS↗
DOE OSTI2024

Evaluating the Thermal Impacts of Inadvertent Export on Service Transformers

As more DER interconnect to the system there are times where the total generation (or load) of a DER cannot be supported by the system. To more quickly allow these DER to interconnect they may need to regulate their output to a different export capacity than their nameplate capacity via a power control system (PCS). In the time it takes PCS to respond, however, an inadvertent export may occur, where the DER operates beyond its export limit. This report evaluates the potential thermal impact such momentary violations may have on service transformers, to determine whether response times should be more tightly controlled or not.

42 ENGINEERING↗
DOE OSTI2023

Multi-Port Autonomous Reconfigurable Solar Power Plant

As the penetration level of power electronics increases and remote photovoltaic (PV) generation is integrated into the alternating current (ac) grid, the short-circuit ratio (SCR) at the point of interconnection of a hybrid PV–energy storage system (ESS) plant may be low. Additionally, the inertia of the alternating current (ac) grid may be low. The low SCRs and inertias can lead to reliability challenges in the power grid. These operating conditions require additional reinforcements, such as synchronous condensers, static var compensators, static synchronous compensators, and high-voltage direct current (HVdc) links/grids. HVdc links or grids may also provide the additional capability of connecting the plant to asynchronous grids and/or connecting asynchronous grids, among others. This scenario leads to discrete development of solar inverters, energy storage inverters, HVdc converters, and several transformers. Some of the problems associated with this discrete development and inverter-based generation include increased cost, lower reliability, and reduced efficiency associated with duplication of power electronics (PEs); competing controls of several individual discrete inverter-based generators due to the presence of multiple PEs, which leads to derating of the system; and transient stability problems arising from inverter-based generation, such as voltage/frequency events leading inverter shutdowns, voltage instability and control interactions in the formed weak grid, and harmonics caused by resonances of multiple inverters.

14 SOLAR ENERGY↗
DOE OSTI2024

Neural network based emulation of galaxy power spectrum covariances: A reanalysis of BOSS DR12 data

We train neural networks to quickly generate redshift-space galaxy power spectrum covariances from a given parameter set (cosmology and galaxy bias). This covariance emulator utilizes a combination of traditional fully connected network layers and transformer architecture to accurately predict covariance matrices for the high redshift, north galactic cap sample of the BOSS DR12 galaxy catalog. We run simulated likelihood analyses with emulated and brute-force computed covariances, and we quantify the network’s performance via two different metrics: (1) difference in Χ 2 and (2) likelihood contours for simulated BOSS DR 12 analyses. We find that the emulator returns excellent results over a large parameter range. We then use our emulator to perform a reanalysis of the BOSS HighZ NGC galaxy power spectrum, and find that varying covariance with cosmology along with the model vector produces Ω m = $0.27⁢6$$^{+0.013}_{–0.015}$, H 0 = 70.2 ± 1.9 km/s/Mpc, and σ 8 = $0.67⁢4$$^{+0.058}_{–0.077}$. These constraints represent an average 0.46⁢σ shift in best-fit values and a 5% increase in constraining power compared to fixing the covariance matrix (Ω m = 0.293 ± 0.017, H 0 = 70.3 ± 2.0 km/s/Mpc, σ 8 = $0.70⁢2$$^{+0.063}_{–0.075}$). As a result, this work demonstrates that emulators for more complex cosmological quantities than second-order statistics can be trained over a wide parameter range at sufficiently high accuracy to be implemented in realistic likelihood analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Using extended MHD to explore lasers as a trigger for x-pinches

X-pinches have been shown to be a source of extremely intense x-ray emissions useful for diagnosing plasma dynamics and imaging biological objects. The most striking feature of an x-pinch is the hotspot, the point source from where all the x rays come from. Unfortunately, the exact timing and location of the hotspot are still unpredictable. Since an x-pinch hotspot forms from instabilities (like an m = 0 mode), we will computationally explore whether we can use a high-power laser to control the timing and location of these instabilities. Our goal is to reduce the temporal and spatial jitter associated with the x-ray burst. Using an extended MHD (XMHD) code, we explore the non-relativistic instability generation using a current profile of a 250 kA linear transformer driver system and laser characteristics of SLAC's Matter in Extreme Conditions laboratory. Our results include both laser-penetration results using a boundary-defined electromagnetic (EM) wave and instability results from a power-deposition method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Transformational Sorbent-Based Process for a Substantial Reduction in the Cost of CO2 Capture

Adsorption processes have the potential to significantly reduce the CO2 capture cost from power plants and industrial flue gases. InnoSepra has developed several sorbent-based technologies to obtain very high CO2 recovery and high purity CO2 while meeting the EOR/sequestration product specifications. A first-generation process was shown to achieve CO2 recoveries greater than 94% at CO2 purities in the range of 98.5-99.5%. The absolute heat energy required for the process was not only 40% lower than MEA but also was needed at a much lower temperature. This work will present the performance of the second-generation InnoSepra process which is based on a breakthrough sorbent regeneration method. This process has a projected 45% lower capital cost compared to MEA, less than 16% loss in plant output for CO2 capture and compression, and a CO2 capture cost at least 55% lower than MEA. Lab scale testing and the process simulations indicate a CO2 capture performance similar to or better than the first-generation process. Pilot scale drying and breakthrough tests were carried out at the Technology Centre Mongstad (TCM), Norway, and lab-scale cyclic tests were carried out at InnoSepra to confirm the process improvement. During these tests, InnoSepra’s flue gas purification technology was also tested at TCM. It can provide a low-cost option for the removal of NO2 and SOX to sub-ppm levels as well as a significant reduction in aerosol emissions.

08 HYDROGEN↗
DOE OSTI2026

Structure-Aware Unsupervised, Transformational Machine Learning for Drug Discovery (DTRA Basic Research Final Report)

The major goal of this project is to develop machine learning (ML) methods to enable improved predictive power on real drug discovery for novel targets. More specifically, we planned to demonstrate the capability and effectiveness of ML tools utilizing unlabeled large-volume protein-ligand datasets. We investigated multiple pre-training approaches for 3D protein-ligand structure-based foundation models, without relying on experimental binding data. We also addressed scenarios in which crystal structures are unavailable or binding data are limited. We also planned to develop a complete pipeline to screen novel compounds as well as to demonstrate the capability and effectiveness of the developed methods by testing on a realistic drug discovery task such as SARS-CoV-2. While the major goals and milestones remain consistent with the original proposal, certain technical details have been adjusted, based on the experimental results and related outcomes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Gravitational soft theorem from emergent soft gauge symmetries

We consider and derive the gravitational soft theorem up to the sub-subleading power from the perspective of effective Lagrangians. The emergent soft gauge symmetries of the effective Lagrangian provide a transparent explanation of why soft graviton emission is universal to sub-subleading power, but gauge boson emission is not. They also suggest a physical interpretation of the form of the soft factors in terms of the charges related to the soft transformations and the kinematics of the multipole expansion. The derivation is done directly at Lagrangian level, resulting in an operatorial form of the soft theorems. In order to highlight the differences and similarities of the gauge-theory and gravitational soft theorems, we include an extensive discussion of soft gauge-boson emission from scalar, fermionic and vector matter at subleading power.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Estimates of the wavenumber wavelet power spectrum of magnetic fluctuations during magnetic reconnection

Fluctuation analyses of experimental observations generally lack high temporal resolution and are in frequency-space f, contrary to theoretical efforts in wavenumber-space k. This is due to the inherent limits of the Fourier transform, though it is prominent due to the ease of diagnostic implementation. Advances in wavelet-based analysis have provided relief due to its temporal resolution, but in its common use, is still hard to compare to theoretical models. By using the two-point correlation technique in conjunction with large data sets, a wavelet power spectrum in wavenumber-space can be created. Dubbed the wavenumber wavelet power spectrum, this spectrum relates wavenumber to power in time. Further, this analysis technique more closely connects characterizations of experimentally observed fluctuations with other system parameters and theoretical predictions. In this article, we develop the wavenumber wavelet power spectrum using magnetic fluctuations caused by tearing instability driven magnetic reconnection in reproducible, high temperature laboratory plasmas. These dynamic magnetic fluctuations generated in reversed field pinch plasmas are broadband, ranging from the low frequency, 10's of kHz, up to the ion gyroradii frequencies, 100's of kHz. The dominant fluctuations have poloidal and toroidal mode numbers (m,n)=(1,6−10) and can grow to 2%–3% of the mean magnetic field. During these reconnection events, ions, and electrons are energized, magnetic fluctuation amplitudes increase, plasma flow is halted, and the toroidal magnetic flux increases, all on a semi-periodic basis. The newly developed spectrum provides better temporal resolution of spectrum characteristics to correlate with these particle energization phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗
DOE OSTI2025

Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images

Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.

Byford, Brandon K. [New Mexico State Univ., Las Cr↗
DOE OSTI2023

Thiol‐Functionalized Conjugated Metal–Organic Frameworks for Stable and Efficient Perovskite Photovoltaics

Abstract Metal–organic frameworks (MOFs) have been investigated recently in perovskite photovoltaics owing to their potential to boost optoelectronic performance and device stability. However, the impact of variations in the MOF side chain on perovskite characteristics and the mechanism of MOF/perovskite film formation remains unclear. In this study, three nanoscale thiol‐functionalized UiO‐66‐type Zr‐based MOFs (UiO‐66‐(SH) 2 , UiO‐66‐MSA, and UiO‐66‐DMSA) are systematically employed and examined in perovskite solar cells (PSCs). Among these MOFs, UiO‐66‐(SH) 2 , with its rigid organic ligands, exhibited a strong interaction with perovskite materials with more efficient suppression of perovskite vacancy defects. More importantly, A detailed and in‐depth discussion is provided on the formation mechanism of UiO‐66‐(SH) 2 ‐assisted perovskite film upon in situ GIWAXS performed during the annealing process. The incorporation of UiO‐66‐(SH) 2 additives substantially facilitates the conversion of PbI 2 into the perovskite phase, prolongs the duration of stage I, and induces a delayed phase transformation pathway. Consequently, the UiO‐66‐(SH) 2 ‐assisted device demonstrates reduced defect density and superior optoelectronic properties with optimized power conversion efficiency of 24.09% and enhanced long‐term stability under ambient environment and continuous light illumination conditions. This study acts as a helpful design guide for desired MOF/perovskite structures, enabling further advancements in MOF/perovskite optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2022

COMET: A Domain-Specific Compilation of High-Performance Computational Chemistry

The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to 1.98×speedup for 30 tensor contractions commonly used in computational chemistry and beyond.

Mutlu, Erdal↗
DOE OSTI2024

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Photoelectrochemical CO 2 Reduction to CO Enabled by a Molecular Catalyst Attached to High-Surface-Area Porous Silicon

A high-surface-area p-type porous Si photocathode containing a covalently immobilized molecular Re catalyst is highly selective for the photoelectrochemical conversion of CO 2 to CO. It gives Faradaic efficiencies of up to 90% for CO at potentials of –1.7 V (versus ferrocenium/ferrocene) under 1 sun illumination in an acetonitrile solution containing phenol. Here, the photovoltage is approximately 300 mV based on comparisons with similar n-type porous Si cathodes in the dark. Using an estimate of the equilibrium potential for CO 2 reduction to CO under optimized reaction conditions, photoelectrolysis was performed at a small overpotential, and the onset of electrocatalysis in cyclic voltammograms occurred at a modest underpotential. The porous Si photoelectrode is more stable and selective for CO production than the photoelectrode generated by attaching the same Re catalyst to a planar Si wafer. Further, facile characterization of the porous Si-based photoelectrodes using transmission mode FTIR spectroscopy leads to highly reproducible catalytic performance.

14 SOLAR ENERGY↗
DOE OSTI2026

Carbon doping in GeTe drives differences in local structure and properties

Advances in low-power, energy-efficient information storage and computing require understanding and controlling the atomic and nanoscale structures of functional materials, such as phase-change materials. Phase-change memory technology enables nonvolatile, low-power memory in devices by storing information through reversible changes in a phase-change material's atomic structure (i.e., transformations between amorphous and crystalline phases) that have corresponding changes in properties, including electronic resistivity and optical reflectivity. Here, we apply complementary X-ray absorption spectroscopy and X-ray pair distribution function analyses to experimentally identify the local- and medium-range atomic structure differences of GeTe and C-doped GeTe thin films. Upon controlled heating, composition- and temperature-dependent atomic structure evolution in GeTe and C-doped GeTe films shows differences in bonding behavior and local structure that directly influence crystallization onset temperature. We find that the introduction of C interrupts Ge–Ge bonds in amorphous GeTe, altering the as-deposited structure to be more similar to the distorted rocksalt structure of crystalline α–GeTe. The change alters the response of the amorphous atomic structure to heating and also lowers the crystallization onset temperature, from 230 °C in GeTe to 220 °C in the C-doped film. The combined insights from both X-ray techniques provide understanding of structural transformations that enables the development and optimization of next-generation memory and computing materials.

36 MATERIALS SCIENCE↗