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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating a quantum-classical quantum Monte Carlo algorithm with Matchgate shadows

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins et al ., Nature (London) 603 , 416 (2022)] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling postprocessing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [Commun. Math. Phys. 404 , 629 (2023)], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical postprocessing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm.

Monte Carlo methods↗
DOE OSTI2024

Covalent Triazine-Based Frameworks with Ru-tda Based Catalyst Anchored via Coordination Bond for Photoinduced Water Oxidation

Light-induced water splitting (hν-WS) for the production of hydrogen as a solar fuel is considered a promising sustainable strategy for the replacement of fossil fuels. An efficient system for hν-WS involves a photoactive material that, upon shining light, is capable of separating and transferring charges to catalysts for the hydrogen and oxygen evolution processes. Covalent triazine-based frameworks (CTFs) represent an interesting class of 2D organic light-absorbing materials that have recently emerged thanks to their tunable structural, optical and morphological properties. Typically, catalysts (Cat) are metallic nanoparticles generated in situ after photoelectroreduction of metal precursors or directly drop-casted on top of the CTF material to generate Cat-CTF assemblies. Here, in this work, the synthesis, characterization and photocatalytic performance of a novel hybrid material, Ru-CTF, is reported, based on a CTF structure featuring dangling pyridyl groups that allow the Ru-tda (tda is [2,2':6',2'“-terpyridine]-6,6'”-dicarboxylic acid) water oxidation catalyst (WOC) unit to coordinate via covalent bond. The Ru-CTF molecular hybrid material can carry out the light-induced water oxidation reaction efficiently at neutral pH, reaching values of maximum TOF of 17 h -1 and TONs in the range of 220 using sodium persulfate as a sacrificial electron acceptor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Photosynthetic biohybrid systems for solar fuels catalysis

Photosynthetic reaction center (RC) proteins are finely tuned molecular systems optimized for solar energy conversion. RCs effectively capture and convert sunlight with near unity quantum efficiency utilizing light-induced directional electron transfer through a series of molecular cofactors embedded within the protein core to generate a long-lived charge separated state with a useable electrochemical potential. Of current interest are new strategies that couple RC chemistry to the direct synthesis of energy-rich compounds. This Feature Article highlights recent work from our lab on RC and RC-inspired hybrid systems that capture the Sun's energy and convert it to chemical energy in the form of H2, a carbon-neutral energy source derived from water. Further, biohybrids made from the Photosystem I (PSI) RC are among the best photocatalytic H2-producing protein hybrids to date. Targeted self-assembly strategies that couple abiotic catalysts to PSI translate to catalyst incorporation at intrinsic PSI sites within thylakoid membranes to achieve complete solar water-splitting systems. RC-inspired biohybrids interface synthetic photosensitizers and molecular catalysts with small proteins to create photocatalytic systems and enable the spectroscopic discernment of the structural features and electron transfer processes that underpin solar-driven proton reduction. In total, these studies showcase the incredible scientific opportunities photosynthetic biohybrid research provides for harnessing the optimal qualities of both artificial and natural photosynthetic systems and developing materials that capture, convert, and store solar energy as a fuel.

14 SOLAR ENERGY↗
DOE OSTI2021

Techno-Economic Analysis of Product Diversification Options for Sustainability of the Monticello and Prairie Island Nuclear Power Plants

The objective of this work was to perform techno-economic analysis (TEA) of hybrid options that could be integrated with light water reactor (LWR) nuclear power plants (NPPs) in order to improve the viability and sustainability of existing LWRs through product diversification by utilizing nuclear energy not only to produce grid electricity but to also produce carbon free products such as hydrogen, ammonia or synthetic fuels. Much of the analysis herein could be generally applied to any LWR (high temperature steam electrolysis- HTSE design), but hydrogen demand and the optimization of the HTSE was completed with specific collaboration and data from Xcel Energy’s Prairie Island and Monticello nuclear generating stations and the surrounding market and logistics potential in the greater Minneapolis region. Xcel Energy has set aggressive goals with regards to decarbonization, including an 80% reduction in CO 2 emissions from 2005 levels, by 2030 and 100% carbon free by 2050. Other TEAs regarding hydrogen production with nuclear energy have been completed in collaboration with other utility companies previously. Besides being the first TEA in this regard specific to Xcel Energy and the surrounding markets and logistics, this TEA adds to the previously completed TEAs by providing the most up to date and state of the art analysis and optimization of the hydrogen production plant integrated with nuclear power (NPP-HTSE).

08 HYDROGEN↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2025

Correlations between the Neutron Star Mass–Radius Relation and the Equation of State of Dense Matter

We develop an analytic method of inverting the Tolman–Oppenheimer–Volkoff relations to high accuracy. In principle, a specified energy density–pressure relation gives a unique mass–radius (M–R) relation and vice versa. Our method is developed from the strong correlations that are shown to exist between the neutron star mass–radius curve and the equation of state (EOS) or pressure–energy density relation. Selecting points that have masses equal to fixed fractions of the maximum mass, we find a semi-universal power-law relation between the central energy densities, pressures, sound speeds, chemical potentials, and number densities of those stars, with the maximum mass and the radii of one or more fractional maximum mass points. Rms fitting accuracies, for EOSs without large first-order phase transitions, are typically 0.5% for all quantities at all mass points. The method also works well, although less accurately, in reconstructing the EOS of hybrid stars with first-order phase transitions. These results permit, in effect, an analytic method of inverting an arbitrary M–R curve to yield its underlying EOS. We discuss applications of this inversion technique to the inference of the dense matter EOS from measurements of neutron star masses and radii as a possible alternative to traditional Bayesian approaches.

Bayesian statistics↗
DOE OSTI2025

Many-body perturbation theory with hybrid density functional theory starting points accelerated by adaptively compressed exchange

We report on the use of the adaptively compressed exchange (ACE) operator to accelerate many-body perturbation theory (MBPT) calculations, including G 0 W 0 and the Bethe–Salpeter equation (BSE), for hybrid density functional theory starting points. We show that by approximating the exact exchange operator with the low-rank ACE operator, substantial computational savings can be achieved with systematically controllable errors in the quasiparticle energies computed with full-frequency G 0 W 0 and the optical absorption spectra and vertical excitation energies computed by solving the BSE within density matrix perturbation theory. Our implementation makes use of the ACE-accelerated electronic Hamiltonian to carry out both G 0 W 0 and BSE without explicitly computing empty states. We show the robustness of the approach and present the computational gains obtained on both the central processing unit and graphics processing unit nodes. In conclusion, our work will facilitate the exploration and evaluation of fine-tuned hybrid starting points aimed at enhancing the accuracy of MBPT calculations without involving computationally demanding self-consistency in Hedin’s equations.

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

Vapor Phase Infiltration Improves Thermal Stability of Organic Layers in Perovskite Solar Cells

Despite the rapid increase in power conversion efficiency (PCE) of perovskite solar cells (PSCs) over the past decade, stability remains a major roadblock to commercialization. Here, this work shows vapor phase infiltration (VPI) as a tool to create hybrid organic–inorganic layers that improve the stability of organic charge transport layers, such as hole-selective spiro-OMeTAD in PSCs and in other organic electronic devices. Using X-ray photoelectron spectroscopy (XPS), ultraviolet photoelectron spectroscopy (UPS), and grazing incident wide-angle X-ray scattering (GIWAXS), we identify that infiltration of TiO x via VPI hinders the crystallization of the spiro-OMeTAD layer by likely preventing the π–π stacking of the molecules. Infiltrated PSCs retained more than 80% of their original efficiency after an operando stability test of 200 h at 75 °C, double the efficiency retained by devices without infiltration, in which the efficiency rapidly decreases in the first 50 h. This work provides a blueprint for using VPI to stabilize organic charge transport layers via prevention of π–π stacking leading to deleterious crystallization that shortens device lifetimes.

14 SOLAR ENERGY↗
DOE OSTI2023

Energy Equity Opportunities in Distributed Wind Hybrid Systems for Rural Loads

Distributed wind (DW) hybrid systems, or combinations of distributed energy resources with wind technologies, have the potential to provide energy equity benefits for rural loads. However, project developers and end-users do not understand these benefits well. This report outlines energy equity opportunities that result directly from DW-hybrid projects and proposes a framework that demonstrates how to consider equity in the DW-hybrid system, which can enhance existing resilience and valuation tools by incorporating a way to include equity considerations. Energy equity opportunities achievable with DW-hybrid systems for rural loads are defined by the core principles of energy justice for use in a proposed equity framework. As well, a catalog of equity resources, metrics, technology-agnostic frameworks, and best practices are linked in an attached Excel file. Electric cooperatives, technical assistance providers (including national laboratory and DOE staff), DW project developers, and technology providers can use this work to understand, evaluate, and define equitable outcomes for potential DW-hybrid projects.

17 WIND ENERGY↗
DOE OSTI2023

One System, Many Models: Designing a Surrogate Model for Sulfur Thermal Energy Storage: Preprint

Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.

computational fluid dynamics↗
DOE OSTI2023

Hybrid Heavy Duty Diesel Powertrain for Off-Road Applications (Final Technical/Scientific Report)

In this program, a heavy-duty hybrid powersystem, consisting of a 30% downsized diesel engine and a front-end accessory drive (FEAD) incorporating a high-speed flywheel (HSFW) energy storage system, a mechanical-drive turbocharger (SuperTurbo), and motor-generator units (MGU) was developed and demonstrated. The high efficiency core 13L engine coupled with the hybrid elements was physically validated across various off-road machine and transient work cycles with the ultimate goal of demonstrating 17% efficiency improvement with equivalent transient response as the 18L diesel engine this concept powersystem would replace. The project culminated in a physical demonstration of the high-efficiency powersystem in a high-capability test cell with the engine, all the hybrid devices, controls, and required performance and emissions measurements. Based on the combination of physical validation and rigorous system simulation, the following program conclusions may be drawn: (1) The developed hybrid H2D2 powersystem demonstrated a range of efficiency improvement of 10.5 - 25.6%, with a midpoint of 17.9%; (2) Transient load response on; (3) The HSFW system was validated to achieve peak assisting of 110kW at 12,000 Nm/sec ramp rates; (4) The powersystem was validated to be capable of meeting U.S. EPA Tier 4 Final off-road emissions through transient NRTC testing; (5) A Total Cost of Ownership (TCO) analysis and found that the core 13L engine would pay back immediately (adoption of start/stop would pay back in less than one year, and the full hybrid system payback was three years.)

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach

Neuromorphic systems allow for extremely efficient hardware implementations for neural networks (NNs). In recent years, several algorithms have been presented to train spiking NNs (SNNs) for neuromorphic hardware. However, SNNs often provide lower accuracy than their artificial NNs (ANNs) counterparts or require computationally expensive and slow training/inference methods. To close this gap, designers typically rely on reconfiguring SNNs through adjustments in the neuron/synapse model or training algorithm itself. Nevertheless, these steps incur significant design time, while still lacking the desired improvement in terms of training/inference times (latency). Designing SNNs that can mimic the accuracy of ANNs with reasonable training times is an exigent challenge in neuromorphic computing. In this work, we present an alternative approach that looks at such designs as an optimization problem rather than algorithm or architecture redesign. We develop a versatile multiobjective hyperparameter optimization (HPO) for automatically tuning HPs of two state-of-the-art SNN training algorithms, SLAYER and HYBRID. We emphasize that, to the best of our knowledge, this is the first work trying to improve SNNs’ computational efficiency, accuracy, and training time using an efficient HPO. We demonstrate significant performance improvements for SNNs on several datasets without the need to redesign or invent new training algorithms/architectures. Our approach results in more accurate networks with lower latency and, in turn, higher energy efficiency than previous implementations. In particular, we demonstrate improvement in accuracy and more than 5× reduction in the training/inference time for the SLAYER algorithm on the DVS Gesture dataset. In the case of HYBRID, we demonstrate 30% reduction in timesteps while surpassing the accuracy of the state-of-the-art networks on CIFAR10. Further, our analysis suggests that even a seemingly minor change in HPs could change the accuracy by 5 - 6×.

Parsa, Maryam↗
DOE OSTI2020

Hybrid real- and reciprocal-space full-field imaging with coherent illumination

In this work, we present a novel diffractive imaging method that harnesses a low-resolution real-space image to guide the phase retrieval. A computational algorithm is developed to utilise such prior knowledge as a real-space constraint in the iterative phase retrieval procedure. Numerical simulations and proof-of-concept experiments are carried out, demonstrating our method's capability of reconstructing high-resolution details that are otherwise inaccessible with traditional phasing algorithms. With the present method, we formulate a conceptual design for the coherent imaging experiments at a next-generation x-ray light source.

42 ENGINEERING↗
DOE OSTI2026

Achieving high rate performance in hybrid pristine-recycled cathodes using model-informed electrode designs

Direct recycling lithium-ion battery cathodes, a process that retains the engineered oxide structures from end-of-life materials, presents a cost-effective and energy-efficient alternative to other battery recycling methods. However, while direct-recycled cathodes have demonstrated performance comparable to that of pristine materials at low cycling rates, their high-rate performance remains uncertain. Morphology changes in cathode particles, a main mode of degradation, directly impact rate performance by limiting surface kinetics and solid-phase diffusion. If direct recycling processes do not sufficiently restore pristine-like morphologies, the recycled materials may retain structural defects that hinder high-rate performance. The present work uses a physics-based pseudo-2D model to simulate hybrid electrodes with pristine and artificially “aged/recycled” NMC materials to investigate potential impacts of incorporating performance-limited aged cathode materials into cells. The study highlights how differences in transport and kinetic properties can influence rate capabilities in mixed electrodes — particularly in high-loading cells in high-demand applications. However, model results also reveal a possible mitigation strategy via dual-layer electrode architectures with lower-performing materials positioned near the current collector. Simulations of 4.0 mAh cm −2 cells cycled at 4C using a dual-layer architecture provided approximately 5%–30% more capacity in constant-current protocols compared to homogeneously blended electrode architectures with the same loadings and mixed-material compositions. These findings highlight the importance of strategic electrode design in minimizing potential performance losses and facilitating the integration of recycled materials into high-performance batteries, advancing sustainable and cost-effective battery manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2024

Single-event burnout in homojunction GaN vertical PiN diodes with hybrid edge termination design

GaN devices play a major role in modern electronics, providing high-power handling, efficient high-frequency operation, and resilience in harsh environments. However, electric field crowding at the edge of the anode often limits its full potential, leading to single-event effects (SEEs) at lower bias voltages under heavy ion radiation. Here, in this work, we report on the performance of homojunction GaN vertical PiN diodes with a hybrid edge termination design under heavy ion irradiation, specifically, oxygen ions, chlorine ions, Cf-252 fission fragments, and alpha particles from an Am-241 source. The unique hybrid edge termination (HET) design provides better electric field management, preventing breakdown from occurring at the edge of the anode at lower voltages. The results of this study reveal that these devices exhibit excellent tolerance to 12-MeV oxygen and 16-MeV chlorine ions, owing to their low linear energy transfer (LET) and range in GaN. However, single-event burnout (SEB) is observed during the Cf-252 exposure at about 50% of the diodes' electrical breakdown voltage due to the presence of higher LET and longer-range ions. Optical and scanning electron microscopy (SEM) reveal that the damage that caused by SEB lies close to the center of these devices rather than the anode edge. Devices with junction termination extension (JTE) instead of HET edge termination also show similar SEB when irradiated with Cf-252 fission fragments. Physical damage due to SEB occurs at the edge of the anode for these devices. These comparative results show the benefits of HET for enhancing the resistance of GaN-based PiN diodes to heavy ion irradiation.

42 ENGINEERING↗
DOE OSTI2023

Reliable, High Power Density Inverters for Heavy Equipment Applications

With this final report, the combined team of the University of Arkansas (UA), University of Illinois, Urbana-Champaign (UIUC), Wolfspeed, Caterpillar, and Ampaire have successfully met all of their project objectives. Noteworthy for the heavy equipment portion of the project with Caterpillar is that the team made its project milestones two years into the project by designing a power dense motor drive for a permanent magnet synchronous machine. Upon finding out that Caterpillar had pivoted to switched-reluctance machines (SRMs), the team subsequently redesigned and implemented the SRM drive with a coolant temperature of 105°C! The other major task that the UA, UIUC, and Wolfspeed teams took on was the design of a PMSM drive for a hybrid aircraft that was flown on Feb. 20, 2023 by Ampaire after extensive testing and evaluation. While there were also technical objectives in thermal management, integrated gate drivers, reliability studies, and high temperature capacitors with integrated bussing, each of these have been fully reported on in quarterly reports. In brief, advances in thermal management and high temperature capacitors were utilized in order to achieve a 105°C motor drive. The integrated gate driver work resulted in a higher density drive with no loss of efficiency. Most of the last year, during a no-cost extension, was spent waiting for the Ampaire motor drive to be tested (outside of our project). Many months passed with the device just sitting in California while the company dealt with battery-related issues. This delayed the integration and testing activities until Fall 2022. Once those began, then the process took about 4-5 months to complete culminating in the test flight in Feb. 2023. By providing technical advances and integration into final platforms, the barrier to economic impact has been lowered. This project benefits the public by overcoming key technical barriers to electrified and hybrid electric heavy equipment and aircraft. This, in turn, leads to lower greenhouse gas emissions and a cleaner environment. This final report summarizes the integrated gate driver work and the Ampaire hybrid electric aircraft integration and test flight efforts. All other information has been previously reported in quarterly reports. A summary of the motor drives created during this project is provided along with a listing of publications.

42 ENGINEERING↗
DOE OSTI2025

A Computational Procedure for Assessing I$_{c}(\varepsilon$) in Nb$_{3}$Sn/Bi-2212 Hybrid Magnets

The critical current of superconductors is commonly measured by testing unloaded wires under an external magnetic field. While stressed by intense Lorentz forces, the existing HTS/LTS superconductors are prone to a reduction in critical current before reaching their structural mechanical limit. Here, in this work, the magnetic and mechanical analysis of the FNAL 4-layer Bi-2212/Nb$_{3}$Sn hybrid dipole magnet is reported, aimed at predicting the critical current degradation for both the superconductors during powering at 16T. All the Rutherford cables in the coils of the hybrid magnet were modeled at the strand level in Ansys APDL with the heterogeneous cable model. Utilizing this detailed geometry, it was possible to evaluate the effects of strain on the critical current degradation for both the Nb$_{3}$Sn and Bi-2212 superconductors under the intense Lorentz forces. The analysis presented in this paper integrates strain-dependent critical current laws, with parameters derived from experimental data, to simulate the hybrid magnet's performance for all possible current-powering configurations. The proposed methodology enables a detailed assessment of conductor integrity and I$_{C}(\varepsilon$) reduction in existing hybrid magnet designs, providing a versatile and rigorous framework for optimizing future high-field hybrid magnets.

D'Agliano, A. [Lawrence Berkeley National Laborato↗