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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enabling Grid-Forming Control Under Unbalanced Conditions

Standalone microgrids often experience unbalanced loading and faults, which can cause grid-forming control designed for balanced conditions to produce oscillatory responses. To address this issue, a compact time-domain transformation appropriate for inverter control is proposed, allowing the conversion of unbalanced three-phase signals to positive and negative synchronous reference frames. This transformation supports the development of a grid-forming control with fault ride-through, featuring frequency and voltage droop controllers and nested current and voltage control loops that seamlessly integrate an enhanced current limiter. The effectiveness of the proposed control and transformation is demonstrated through analytical results and electromagnetic transient simulation.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improving the Transformation Efficiency of Synechococcus sp. PCC 7002 via Methylome-Guided Premethylation of DNA

Cyanobacteria are promising microbial platforms for a diverse set of biotechnology applications, from living materials to photosynthetic chemical production, but are less well characterized than commonly engineered microbes such as Escherichia coli. This study facilitates genetic engineering in Synechococcus sp. PCC 7002, a fast-growing, halotolerant, and naturally competent strain, by identifying ten native methylation motifs and designing shuttle strains that mimic the native methylation state by expressing a subset of heterologous methyltransferases. DNA methylation in E. coli with as few as two active methyltransferases increased transformation efficiency up to 30-fold across four distinct integration sites in PCC 7002. This work provides an experimental framework to bypass native restriction-modification systems for efficient genome editing and metabolic engineering in nonmodel bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Characterization of thermally heat-treated polyacrylonitrile carbon fibers

This study investigates the graphitization process of polyacrylonitrile (PAN) carbon fibers by subjecting commercial fibers to thermal heat treatment at temperatures ranging from 1400 to 2100 °C in 100 °C increments, using either argon or nitrogen gas atmospheres. Changes in crystallinity, surface morphology, and lattice parameters were analyzed for two commercial carbon fibers using X-ray diffraction, scanning electron microscopy, and Raman spectroscopy. Results indicated minimal changes in surface morphology with increasing heat-treatment temperature; however, crystallinity significantly increased. Crystallinity changes were more strongly dependent on temperature rather than gas atmosphere or fiber type. At intermediate heat-treatment temperatures (1600–1800 °C), fibers treated in argon showed a slight preference for graphitization. The highest level of graphitization was measured at 2100 °C. Crystallite size increased as the intensity ratio of the D1 to G Raman peaks increased, reaching a peak around ~1800 °C, after which the ratio started to decrease. This behavior aligns with Ferrari's three-stage model of carbon crystallization and is consistent with both the Marie-Mering degree of graphitization and Brubaker's Integrated Absolute Differential models, all of which describe the transformation from an amorphous to a more graphitic structure. At the higher heat-treatment temperatures, the changes between atmospheres and fiber types were measured to converge to similar levels of graphitization. In conclusion, this study evaluates the progressive change in commercial grade carbon fibers when heat-treated.

Characterization↗
DOE OSTI2024

An Orbital Basis Set for Double Photoionization of Atoms and Molecules

The ab initio theoretical treatment of one-photon double photoionization processes has been limited to atoms and diatomic molecules by the challenges posed by large grid-based representations of the double ionized continuum wave function. To provide a path for extensions to polyatomics, an energy-adapted orbital basis approach is demonstrated that reduces the dimensions of such representations and simultaneously allows larger time steps in time-dependent computational descriptions of double ionization. Additionally, an algorithm that exploits the diagonal nature of the two-electron integrals in the grid basis and dramatically accelerates the transformation between grid and orbital representations is presented. Excellent agreement between the present results and benchmark theoretical calculations is found for H – and Be atoms, as well as the hydrogen molecule, including for the triply differential cross sections that relate the angular distribution and energy sharing of all of the particles in the molecular frame.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Photosynthetic Biohybrid System for Enhanced Abiotic N 2 -to-NH 3 Conversion under Ambient Conditions

Photosynthetic biohybrid systems (PBSs) offer an eco-friendly approach to transforming solar energy into value-added products by integrating biological entities with inorganic semiconductors. However, the chemical conversion capacity of most PBSs has inherent limitations, as whole-cell bacteria and isolated enzymes require fine-tuning of environmental conditions. Here, in this study, we report a new PBS developed by introducing free-standing ceria nanoparticles into the purple membrane (PM) of Halobacterium salinarum archaea, which can unidirectionally transfer charge carriers in response to incident photons, even after separation from living archaea at various conditions. Our microscopy, spectroscopy, and synchrotron X-ray scattering analyses confirm that the electrostatic assembly between ceria and PM creates seamless interfacial contact, thereby enhancing the photocatalytic capacity of ceria. Although the conversion of dinitrogen (N 2 ) to ammonia (NH 3 ) is thermodynamically challenging due to the triple bond in N 2 and a series of charge-transfer reactions, our PM–ceria (PMC) hybrid nanoparticle efficiently produces NH 3 by reducing N 2 using solar energy even under atmospheric pressure and room temperature while simultaneously converting glycerol into value-added derivatives. Additionally, our PMC nanoparticle involves neither toxic/precious metals nor bioengineering processes to achieve enhanced photocatalytic N 2 -to-NH 3 conversion. This study sheds light on the new aspect of PBSs by employing PM to potentially resolve the global energy and environmental challenges posed by the conventional Haber–Bosch process.

Jang, Jinhyeong [Argonne National Laboratory (ANL)↗
DOE OSTI2026

Urban agrivoltaics enhance crop resilience and food-energy synergies in a changing climate

Urban agrivoltaics, the synergistic integration of solar photovoltaics with urban agriculture, offers a transformative solution to food and energy insecurity, which are major barriers to sustainable urban development, especially in low-income urban areas facing intensified heat and water stress due to climate change. With 70% of the global population projected to live in cities by 2050, maximizing underutilized urban spaces is critical. Our study presents the first evaluation of ground-based agrivoltaics in an urban context, demonstrating that, while early-season yields may decline due to light reduction in temperate climates, productivity rebounds during periods of extreme heat, extending harvest windows and enhancing crop resilience. As cities seek climate-adaptive infrastructure, converting just a fraction of vacant land and rooftops to urban agrivoltaics can yield significant co-benefits such as generating renewable energy for thousands of households while supplying fresh produce to help alleviate food deserts.

14 SOLAR ENERGY↗
DOE OSTI2025

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nucleation and growth of polar clusters with in-phase tilts into a long-range ferroelectric matrix in a sodium niobate based complex relaxor

In this study, we have investigated the temperature dependence of atomic ordering at multiple length scales in a lead-free sodium niobate-based relaxor, i.e., 0.75 NaNbO 3 -0.25 Ba 0.9⁢ Ca 0.1⁢ TiO 3 (NN-25BCT) via synchrotron x-ray diffraction, Raman spectroscopy, and pair distribution function analysis. High-resolution synchrotron x-ray powder diffraction (SXRD) measurements reveal a ferroelectric phase transition in the relaxor ferroelectric NN-25BCT below the Vogel-Fulcher freezing temperature (𝑇 VF ≈ 270 K). In addition, SXRD analysis demonstrates the competition between in-phase octahedral tilting and ferroelectric order at the long-range scale using mode crystallography. On the other hand, Raman spectroscopic analysis provides evidence of polar ordering for 𝑇 > 𝑇 VF (with tetragonal symmetry) persisting up to the Burns temperature (𝑇 B ). Furthermore, pair distribution function (PDF) analysis reveals the presence of a polar antiferrodistortive tetragonal phase with 𝑃⁢4⁢𝑏𝑚 space group at short ranges throughout the studied temperatures (i.e., 110 K ≤ 𝑇 ≤500 K), irrespective of nonpolar long-range ordering above 𝑇 VF . Therefore, our measurements provide direct evidence for the presence of polar ordering at short ranges and their gradual transformation into long-range polar ordering using an integrated multiscale structural analysis. In conclusion, as a result of a transition from relaxor to a ferroelectric phase in the vicinity of room temperature, NN-25BCT can be exploited for applications in pyroelectric detectors, electrocaloric devices, and multilayered ceramic capacitors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

Poster presented at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. This poster highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗
DOE OSTI2026

Grid Architecture Mapping to Understand Transformation (GAMUT): Methods and Framework Architecture

Grid architecture (GA) is a concept that was developed to address the need for a comprehensive view of power grid challenges. GA can be viewed as a relatively consistent and fixed high-level approach; however, for any instantiation of grid structures, a combinatorial explosion results from each lower-layer expansion. This constitutes the main challenge with GA—it is a grid architect’s view of the system, which might not be very informative at the implementation level. Grid Architecture Mapping to Understand Transformation (GAMUT project) seeks to bridge that gap by integrating subject matter expertise across GA structures, providing users who lack expertise in GA approaches with valuable insights and informational materials. GAMUT seeks to answer feasibility questions for the approach. System-level expectations are that a GA baseline needs to be established in order for GA to be the common framework to which any lower layer approach is tied. This report explores a potential information ingestion and documentation framework to support GAMUT. The main concepts that enable the solution domain of GAMUT are discussed, and examples are provided. The solution domain leverages already-existing technology and concepts related to GA, knowledge management, and other relevant areas. To assess GAMUT building blocks and the overall approach, a feasibility assessment is proposed, rooted in systems engineering and GA architecture evaluation concepts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗
DOE OSTI2024

Four Channel Time Multiplexed Photonic Doppler Velocimetry using an Optical Switch

Photonic Doppler Velocimetry (PDV) is a diagnostic commonly used in shock physics and dynamic compression experiments to reliably get velocity information from experiments. In PDV systems, a common method of reducing experimental cost is to use time and frequency multiplexing to increase the number of PDV probes. With time multiplexing, interference between probes is a frequent problem. In this report, we look at using a high-speed optical switch to reduce this interference, including measuring the amount of interference generated to determine if it has the potential to affect experiments and integrating a time multiplexing system into an experiment. We find that, when applied to PDV systems, there is approximately (-23.4 ± 0.9) dB of interference measured in the short time Fourier transform between switch inputs. When an optical switch based time multiplexing system was integrated into a dynamic compression experiment, the system was able to successfully combine the signals from four different PDV probes onto a single optical cable without unacceptable levels of interference in the spectrogram. An optical switch based time multiplexing system appears to be a promising method for reducing the cost of fielding larger numbers of PDV probes in an experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Safe Deep Reinforcement Learning for Active Distribution System Model Predictive Control with EVs and DERs

The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging

Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

kokkos-fft: A shared-memory FFT for the Kokkos ecosystem

kokkos-fft provides a unified, performance-portable interface for Fast Fourier Transforms (FFTs) within the Kokkos ecosystem (C. Trott et al., 2021). It seamlessly integrates with leading local FFT libraries including FFTW, cuFFT, rocFFT, and oneMKL. Designed for simplicity and efficiency, kokkos-fft offers a user experience akin to numpy.fft for in-place and out-of-place transforms, while leveraging the raw speed of vendor-optimized libraries. A demonstration solving 2D Hasegawa-Wakatani turbulence with the Fourier spectral method illustrates how kokkos-fft can deliver significant speedups over Python-based alternatives without drastically increasing code complexity, empowering researchers to perform high-performance FFTs simply and effectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗
DOE OSTI2026

A Monte Carlo Laplace Transform Estimator for Radiation Transport

This work formulates and implements a Laplace transform estimator in a simple Monte Carlo radiation transport code. The estimator maps flux-based quantities of interest, like reaction rates, from a desired phase-space dimension to the complex Laplace domain. This on-the-fly Monte Carlo integration technique enables the spectral analysis of arbitrary nuclear systems via the Laplace transform. A simple code tests the estimator in neutron slowing-down problems across various infinite media, and the results compare well with Ganapol’s uninverted analytical solution of the neutron slowing-down equation.

97 MATHEMATICS AND COMPUTING↗