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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Decoupling absorption and radiative cooling in mid-wave infrared bolometric elements

We present a spectrally selective, passively cooled mid-wave infrared bolometric absorber engineered to spatially and spectrally decouple infrared absorption and thermal emission. The structure leverages an antenna-coupled metal–insulator–metal resonance for mid-wave infrared normal incidence photon absorption and a long-wave infrared optical phonon absorption feature, aligned closer to peak room temperature thermal emission. The phonon-mediated resonant absorption enables a strong long-wave infrared thermal emission feature limited to grazing angles, leaving the mid-wave infrared absorption feature undisturbed. The two independently controlled absorption/emission phenomena demonstrate decoupling of the photon detection mechanism from radiative cooling and offer a new design approach enabling ultra-thin, passively cooled mid-wave infrared bolometers.

Ware, Alexander (ORCID:0009000256937727)↗
DOE OSTI2024

INL High-Performance and Sustainable Building Strategy

High-performance buildings are reliable, cost effective, and sustainable structures that minimize energy and water use, reduce solid waste and pollutant emissions, and limit the depletion of natural resources. High-performance buildings also provide a thermally and visually comfortable working environment that increases productivity for building occupants. As Idaho National Laboratory (INL) is the nation’s premier nuclear energy research laboratory, the physical infrastructure requires continual updating and repurposing to help accomplish that mission. INL’s infrastructure must incorporate high-performance sustainable design features to be fiscally responsible and reflect an image of innovation to the public and prospective employees. INL is a large consumer of energy with annual energy costs exceeding $16M. This High-Performance and Sustainable Building Strategy will help engineering and construction project teams design sustainable facilities, reduce life cycle operating costs, and support the INL net-zero plan while providing INL employees with a safe and healthy working environment. With these goals in mind, the recommendations described in this document are intended to form INL’s foundation for sustainable and high-performance building standards. This strategy incorporates the latest federal and Department of Energy (DOE) orders and directives, including DOE Order 436.1A, “Departmental Sustainability,” the DOE Sustainability Plan (SP), the INL Site Sustainability Plan (SSP), and Code of Federal Regulations (CFR). This document identifies the requirements of the “Guiding Principles for Sustainable Federal Buildings” (Guiding Principles) and briefly highlights the Leadership in Energy and Environmental Design (LEED) Gold certification. LEED Gold certification can be used to meet many of the requirements of the Guiding Principles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Design of a High-Pressure Fuel System for Use with Dimethyl Ether

The paper documents the modeling and experimental work on a common rail fuel injection system for Dimethyl Ether, a potential diesel substitute with a low carbon intensity signature. The DME fuel system is deployed on a light duty 2.2L compression ignition engine. The paper describes the injector optimization to shift to higher flows to account for the lower heating value and density of the DME when compared to diesel. The type of the injection system used for the DME application is an advanced rendering of the Common rail noted for a one-piece piston-needle injector construction and a solenoid driven spill valve featuring a pressure balanced poppet. A dedicated high-pressure fuel pump designed to pressurize DME is used. The design results in a fast acting open and close injection event, reduced leakage, with reduced cavitation in the fuel injector volume. Design parameters for system optimization included fill and spill orifices, needle lift, bias spring, and injector hole size. The design model provides good correlation of the instantaneous rates of injection with experiments across a wide range of pressure and injection timings. Proposed performance milestones for the design included similar DME injection duration to the diesel counterpart for same fuel energy injected into the cylinder to retain high engine cycle efficiency. Here, the dedicated DME design provided reduced hydraulic delays of 50%. Tests demonstrated sustained operation at pressures of 1000 bar, with capability to reach 1500bar. Durability tests showed no cavitation-deterioration over a 200-hour test cycle by means of spray imaging and hardware inspection.

De Ojeda, William [WM International Engineering, L↗
DOE OSTI2020

Installation and commissioning of the ion source systems for the new spallation neutron source 2.5 MeV injector

The U.S. Spallation Neutron Source (SNS) is a state-of-the-art neutron scattering facility delivering the world’s most intense pulsed neutron beams to a wide array of instruments, which are used to conduct investigations in many fields of engineering, physics, chemistry, material science, and biology. Neutrons are produced by spallation of liquid Hg by the bombardment of short (∼1 µs), intense (∼35 A) pulses of protons delivered at 60 Hz by an accumulator ring which is fed by a high-intensity, 1 GeV, H− LINAC (linear accelerator). This facility has operated nearly continuously since 2006 but has recently undergone a 4-month maintenance period, which featured a complete replacement of the 2.5 MeV injector feeding the LINAC. The new injector was developed at ORNL in an off-line beam test facility and consists of an ion source, low energy beam transport, and a Radio Frequency Quadrupole (RFQ). This report first describes the installed configuration of the new injector detailing the ion source system. The first beam current, RFQ transmission, emittance, and energy measurements from the injector installed on the SNS are reported. These data not only show a significant performance improvement for our existing facility but will also make accessible the higher beam current requirements for future SNS upgrade projects: the proton power upgrade and second target station.

Welton, R. F.↗
DOE OSTI2022

A Unified Testing Platform to Mature Blockchain Applications for Grid Emulation Environments: Preprint

Blockchain technology is a relatively novel technology that can be used to develop more decentralized, autonomous and tamper-evident solutions. A feature that can aid Transactive Energy Systems to reach their goals by enabling individual actors to communicate and reach consensus with other participants in a more decentralized fashion. However, technical barriers to evaluate and adopt this type of technology within the electrical domain still exist. To facilitate this task, BLOSEM Unified Testing Platform (UTP), a DOE-sponsored, multi-lab effort intends to accelerate the development of solutions by offering a common set of reusable services that can be used to interconnect existent grid tools with blockchain services. UTP is intended to serve as development platform that can provide application engineers with the technical means to evaluate potential blockchain solutions, by enabling them to concentrate on the actual application functionalities while at the same time abstracting the connectivity and performance measurement tasks. The use of BLOSEM UTP is further demonstrated by implementing two potential use cases that are intended to validate both the feasibility of implementing these applications as blockchain-based solutions while also demonstrating the features provided by UTP.

blockchain↗
DOE OSTI2021

A Field Enhancement Integration Design Featuring Misalignment Tolerance for Wireless EV Charging Using LCL Topology

This article proposes a magnetic integration design for EV wireless power transfer (WPT) systems, where the compensation and transmitting coils overlap one on top of the other to share the ferrite layer without any decoupling consideration. The magnetic field generated by both the compensation and transmitting coils are exploited to transfer power. To this end, a compensation method is proposed to enable magnetic field enhancement without any reactive power flow between the compensation and the transmitting coils, and to achieve input zero phase angle and constant current output. In addition, an efficient finite element analysis-based coil optimization algorithm is proposed to improve the coil misalignment tolerance on the horizontal plane, in which a reversely connected inner coil is used to stabilize the system output under misalignment conditions. Analytical and simulation results confirm transmission flux density enhancement and reduced leakage field characteristics of the proposed coil design. Finally, a scaled-down WPT prototype is built and tested to verify the performance and effectiveness of the design. The proposed design achieves 91.17% efficiency and misalignment tolerance up to 200 mm in any XY-direction while maintaining the power transfer and its efficiency. The results of this work provide insights into magnetic integration design of high-order compensation topologies featuring higher compactness, less ferrite usage, magnetic field enhancement, and misalignment tolerance for WPT systems.

42 ENGINEERING↗
DOE OSTI2021

Observational Signatures of Planets in Protoplanetary Disks: Temperature Structures in Spiral Arms

High-resolution imaging of protoplanetary disks has unveiled a rich diversity of spiral structure, some of which may arise from disk-planet interaction. Using 3D hydrodynamics with β cooling to a vertically stratified background, as well as radiative-transfer modeling, we investigate the temperature rise in planet-driven spirals. In rapidly cooling disks, the temperature rise is dominated by a contribution from stellar irradiation, 0.3%–3% inside the planet radius but always < 0.5% outside. When cooling time equals or exceeds dynamical time, however, this is overwhelmed by hydrodynamic PdV work, which introduces a ~10%–20% perturbation within a factor of ~2 from the planet’s orbital radius. We devise an empirical fit of the spiral amplitude Δ(T)=(M{sub p}/M{sub th}){sup c}(f{sub PdV}e{sup -t{sub a}{sub r}{sub m}/t{sub c}}+f{sub rad}) to take into account both effects. Where cooling is slow, we find also that temperature perturbations from buoyancy spirals—a strictly 3D, nonisothermal phenomenon—become nearly as strong as those from Lindblad spirals, which are amenable to 2D and isothermal studies. Our findings may help explain observed thermal features in disks like TW Hydrae and CQ Tauri, and underscore that 3D effects have a qualitatively important effect on disk structure.

42 ENGINEERING↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2025

Interleaved bond frustration in a triangular lattice antiferromagnet

Frustration of long-range order via lattice geometry amplifies fluctuations and generates ground states that are highly sensitive to perturbations. Traditionally, geometric frustration is used to engineer unconventional magnetic states; however, the charge degree of freedom and bond order can be similarly frustrated. Finding materials that host both frustrated magnetic and bond networks holds promise for engineering structural and magnetic states with the potential of coupling to one another via either magnetic or strain fields. Here we identify an unusual instance of this coexistence in the triangular lattice antiferromagnets LnCd 3 P 3 (Ln = lanthanides). These compounds feature two-dimensional planes of unique trigonal planar CdP 3 units with an underlying bond instability that is frustrated via emergent kagome ice correlations. This bond instability is interleaved in between layers of frustrated magnetic moments. Furthermore, our results establish LnCd 3 P 3 as a rare materials class in which frustrated magnetism is embedded within a dopable semiconductor with a frustrated bond order instability.

Gomez Alvarado, S. J. [University of California, S↗
DOE OSTI2020

Neural Network Molecular Dynamics at Scale

Neural network molecular dynamics (NNMD) simulations could revolutionize atomistic modeling of materials with quantum-mechanical accuracy at a fraction of computational cost. However, popular NNMD frameworks are generally implemented for a single computing node, and conventional energy-based NN models still suffer from large time-to-solution (T2S), prohibiting the application of NNMD to challenging materials simulations encompassing large spatiotemporal scales. Consequently, no leadership-scale NNMD simulation has thus far been reported. Here, we present a scalable parallel NNMD software (RXMD-NN) based on our scalable reactive molecular dynamics simulation engine named RXMD. RXMD-NN has achieved high scalability up to 786,432 IBM BlueGene/Q cores involving 1.7 billion atoms. Furthermore, we have achieved 4.6-fold reduction of T2S by using a novel network that directly predicts atomic forces from feature vectors. Reduced T2S has for the first time allowed the study of large-scale off-stoichiometry effect in a widely used phase change material, Ge 2 Se 2 Te 5 , thereby resolving its "firstsharp diffraction peak mystery".

fist sharp diffraction peak↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
DOE OSTI2020

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗
DOE OSTI2020

Development of an Optimized Gasoline Surrogate Formulation for PACE Experiments and Simulations

New powertrain solutions are needed to address societal challenges stemming in part from a transportation sector which relies heavily on combustion of conventional hydrocarbon fuels like gasoline. One way to advance new solutions is through predictive simulations. Predictive simulations can potentially limit the extent of experimental validations, reduce time spent in engineering design cycles, unlock new strategies for high efficiency combustion with power density, and minimize tailpipe emissions. However, the simulation tools currently available are either too computationally expensive, inadequate in their accuracy, or a combination of the two. A DOE-funded consortium of national laboratories called the Partnership to Advance Combustion Engines (PACE) seeks to address this gap by rapidly delivering new knowledge and tools which enable market-competitive powertrain solutions for light-duty vehicles. Approaches to modeling combustion in powertrain systems typically incorporate computational fluid dynamics (CFD) simulations with chemical kinetic models. These CFD simulations generally use surrogate fuels featuring a limited number of existing components in a reduced kinetic model to limit computational costs. Such surrogate fuels often sacrifice matching several or more combustion and physical properties of the target gasoline fuel. Validation of surrogate fuels is also often only pursued for a small subset of standard metrics such as research or motor octane number (RON, MON). The purpose of this project is to develop an optimal E10 gasoline surrogate fuel suitable for tasks across PACE to facilitate rapid common analysis and progression toward consortium goals. This project received contributions from tasks led by eight PACE principal investigators. The project funding here reflects all tasks under Pitz, whose tasks also contributed to additional PACE projects. Based on the optimal gasoline surrogate fuel composition, a reduced chemical kinetic model will be shared with PACE researchers, industry, and the broader combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Electro-Optic Modulation Using Metal-Free Perovskites

Electro-optic (EO) modulation is of interest to impart information onto an optical carrier. Inorganic crystals—most notably LiNbO 3 and BaTiO 3 —exhibit EO modulation and good stability, but are difficult to integrate with silicon photonic technology. Solution-processed organic EO materials are readily integrated but suffer from thermal degradation at the temperatures required in operating conditions for accelerated reliability studies. Hybrid organic–inorganic metal halide perovskites have the potential to overcome these limitations; however, these have so far relied on heavy metals such as lead and cadmium. Here, we report linear EO modulation using metal-free perovskites, which maintain the crystalline features of the inorganic EO materials and incorporate the flexible functionality of organic EO chromophores. We find that, by introducing a deficiency of cations, we reduce the symmetry in the perovskite crystal and produce thereby an increased EO response. The best-engineered perovskites reported herein showcase an EO coefficient of 14 pm V –1 at a modulation frequency of 80 kHz, an order of magnitude higher than in the nondefective materials. Furthermore, we observe split peaks in the X-ray diffraction and neutron diffraction patterns of the defective sample, indicating that the crystalline structure has been distorted and the symmetry reduced. Density functional theory (DFT) studies link this decreased symmetry to NH 4 + deficiencies. This demonstration of EO from metal-free perovskites highlights their potential in next-generation optical information transmission.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Small subunits can determine enzyme kinetics of tobacco Rubisco expressed in Escherichia coli

Rubisco catalyses the first step in carbon fixation and is a strategic target to improve photosynthetic efficiency. In plants, Rubisco is composed of eight large and eight small subunits and its biogenesis requires multiple chaperones. We optimised a system to produce tobacco Rubisco in Escherichia coli by co-expressing chaperones in auto-induction medium. We successfully assembled tobacco Rubisco in E. coli with each small subunit that is normally encoded by the nuclear genome. Even though each enzyme carries only a single type of small subunit in E. coli, the enzymes exhibit carboxylation kinetics very similar to that of the native Rubisco. Tobacco Rubisco assembled with a recently discovered trichome small subunit has a higher catalytic rate and a lower CO 2 affinity than those assembled with other small subunits. Finally, our E. coli expression system will allow probing of features of both subunits of Rubisco that affect its kinetic properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling thin layers of analytes on substrates for spectral analysis: use of solid/liquid n and k values to model reflectance spectra

Since solids are only sometimes seen en masse in a pure bulk form, and for liquids other than water almost never, a capability to model reflectance spectra from analytes deposited on various substrates would be highly advantageous. If available, the real, n ( ν ) , and imaginary, k ( ν ) , components of the complex refractive index, n n + i k , can be used to simulate infrared spectra, accounting for reflection, refraction, and absorption phenomena as a function of wavelength. We focus on using the Pacific Northwest National Laboratory (PNNL) derived n / k vectors for solid and liquid analytes deposited as thin layers on different types of substrates including conductors, such as aluminum, and inorganic dielectrics, such as glass. The model is an adaptation of the Monte Carlo ray trace modeling program, TracePro, extended through the use of its macrolanguage. The model is tested using thin films of organic liquids including silicone oil and no. 2 diesel fuel, as well as organic solids such as caffeine and acetaminophen on aluminum and glass. The predicted spectra for the solid films were compared to experimental hemispherical reflectance data measured using a Fourier transform spectrometer with an integrating sphere. The thickness of the calculated layer is a parameter for predicting the (transflectance) spectra and is obtained using the areal density measured from gravimetric methods to generate the thin-layer samples. Comparison of the calculated spectra with experimental hemispherical reflectance data shows excellent agreement, indicating promise for the use of measured n / k data to synthesize reference spectral data. In particular, accounting for the inhomogeneity of the deposits greatly improved the match with experimental data. Finally, the theoretical modeling shows that for thicker layers (ca. 20 to 100 μm) of typical organics possessing moderately strong k values, the longwave infrared features are often saturated and better spectral contrast is obtained from the overtone/combination bands in the shortwave infrared.

42 ENGINEERING↗