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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward continuum gyrokinetic study of high-field mirrors

High-temperature superconducting (HTS) magnetic mirrors under development exploit strong fields with high mirror ratio to compress loss cones and enhance confinement and may offer cheaper, more compact fusion power plant candidates. This new class of devices could exhibit largely unexplored interchange and gradient-driven modes. Such instabilities, and methods to stabilize them, can be studied with gyrokinetics, given the strong magnetization and prevalence of kinetic effects. Our focus here is to (a) determine if oft-used gyrokinetic models for open field lines produce the electron-confining (Pastukhov) electrostatic potential and (b) examine and address challenges faced by gyrokinetic codes in studying HTS mirrors. Here, we show that a one-dimensional limit of said models self-consistently develops a potential qualitatively approaching the analytical Pastukhov level. Additionally, we describe the computational challenges of studying high mirror ratios with open field line gyrokinetic solvers and offer a force softening method to mitigate small time steps needed for time integration in colossal magnetic field gradients produced by HTS coils, providing a 19X speedup.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Erosion Experimentation in Solar Power Systems

An alternate and sustainable form of energy is concentrated solar power (CSP) systems, which capture and store the sun’s energy in the form of heat. To increase the efficiency and overall cost of the plant, particles were used as a heat transfer medium. As particles flow through this system, the movement of particles creates erosion, in the forms of abrasion and attrition. Experiments were tested at room temperature and 800 degrees Celsius for both abrasion wear and attrition erosion. In abrasion wear, specimens were placed inside a container, particles were added, and the specimen rotated in and out of the particles. In attrition erosion, a steel disk rotated amongst particles which resulted in particles rubbing against each other. Abrasion wear increased when temperature increased while attrition showed that the particles broke down as the amount of time increased. For attrition at 800 degrees Celsius, oxide was increased and mixed with the particles. Durability models will be created to predict attrition and abrasion based on the particle and specimen mechanical properties. This discovery shows that this method of utilizing particles for CSP plants would improve efficiency and decrease overall costs.

14 SOLAR ENERGY↗
DOE OSTI2022

Enhanced analysis of experimental x-ray spectra through deep learning

X-ray spectroscopic data from high-energy-density laser-produced plasmas has long required thorough, time-consuming analysis to extract meaningful source conditions. There are often confounding factors due to rapidly evolving states and finite spatial gradients (e.g., the existence of multi-temperature, multi-density, multi-ionization states, etc.) that make spectral measurements and analysis difficult. Here, in this paper, we demonstrate how deep learning can be applied to enhance x-ray spectral data analysis in both speed and intricacy. Neural networks (NNs) are trained on ensemble atomic physics simulations so that they can subsequently construct a model capable of extracting plasma parameters directly from experimental spectra. Through deep learning, the models can extract temperature distributions as opposed to single or dual temperature/density fits from standard trial-and-error atomic modeling at a significantly reduced computational cost compared to traditional trial-and-error methods. These NNs are envisioned to be deployed with high repetition rate x-ray spectrometers in order to provide detailed real-time analysis of experimental spectra.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Autonomous Aerosol and Plasma Co‐Jet Printing of Metallic Devices at Ambient Temperature

Abstract Additive manufacturing of metallic materials holds the potential to revolutionize the fabrication of functional devices unattainable via traditional methods. Despite recent advancements, printing metallic materials typically requires thermal processing at elevated temperatures to form dense structures with desired properties, which presents a major challenge for direct printing and integration with temperature‐sensitive materials. Herein, a unique co‐jet printing (CJP) method is reported integrating an aerosol jet and a non‐thermal, atmospheric pressure plasma jet to enable concurrent aerosol deposition of metal nanoparticle inks and in situ sintering at ambient temperature. A machine learning algorithm is integrated with the CJP to perform real‐time defect detection and autonomous correction, enhancing the yield of printed films with high electrical conductivity from 44% to 94%. Concurrent printing and sintering eliminate the need for post‐printing processing, reducing the overall manufacturing time by multiple folds depending on product size. CJP enables direct printing of functional devices on a variety of temperature‐sensitive materials including biological materials. Direct printing of hydration sensors on living plant leaves is demonstrated for long‐duration monitoring of hydration level in the plant. The versatile CJP method opens tremendous opportunities to harmoniously integrate abiotic and biotic materials for emerging applications in wearable/implantable devices and biohybrid systems.

Du, Yipu [Department of Aerospace and Mechanical E↗
DOE OSTI2022

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Deep Eutectic Solvent Precipitation Synthesis of Metastable Zn 4 V 2 O 9

A precipitation method involving a deep eutectic solvent (DES)–a mixture of hydrogen bond donor and acceptor–is used to synthesize a ternary metal oxide. Without toxic reagents, precipitates consisting of Zn 3 (OH) 2 V 2 O 7 ·nH 2 O and Zn 5 (OH) 6 (CO 3 ) 2 are obtained by simply introducing deionized H 2 O to the DES solution containing dissolved ZnO and V 2 O 5 . Manipulation of the synthetic conditions demonstrates high tunability in the size/morphology of the two-dimensional nanosheets precipitated during the dynamic equilibrium process. According to differential scanning calorimetry and high-temperature powder X-ray diffraction, Zn 3 V 2 O 8 and ZnO obtained by the annealing of the precipitate are intermediates in the reaction pathway toward metastable Zn 4 V 2 O 9 . Intimate mixing of the metal precursors achieved by the precipitation method allows access to the metastable zinc-rich vanadate with unusually rapid heat treatment. Furthermore, the UV–vis and surface photovoltage spectra reveal the presence of sub-band gap states, stemming from the reduced vanadium (V 4+ ) center. Photoelectrochemical measurements confirm weak photoanodic currents for water and methanol oxidation. For the first time, this work shows the synthesis of a metastable oxide with the DES-precipitation route and provides insight into the structure–property relationship of the zinc-rich vanadate.

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

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Design optimization of an additively manufactured prototype recuperator for supercritical CO 2 power cycles

Supercritical CO 2 (sCO 2 ) power cycles are being developed due to their potential for high efficiency and reduced capital cost. It is necessary that these recuperators operate at high pressures and temperatures, up to 30 MPa and 900 K, with effectiveness values > 95% and pressure drops <1% to achieve high cycle efficiencies. Moreover, it is also necessary to have reasonable cost recuperators to control the capital costs of the sCO 2 power cycles. In this study, a Plate Pin-Fin (PPF) heat exchanger has been proposed as an sCO 2 recuperator. This preliminary recuperator design leverages capabilities enabled by additive manufacturing. Although the PPF design has characteristics similar to those of a plate heat exchanger, small diameter and relatively long fins are used to increase surface area, enhance heat transfer, and provide structural support for the partition plates that separate the fluid streams. Existing correlations for heat transfer and pressure drop were adapted for the PPF heat exchanger. These correlations were implemented in a 1D analytical model and used for the optimization of a 5-kW th high temperature recuperator for an indirect sCO 2 cycle by varying the design parameters to minimize the quantity of material required. A 3D conjugate heat transfer numerical simulations were conducted to validate the heat transfer and pressure loss correlations. A steepest descent method was used to minimize heat exchanger mass for a 5-kW prototype recuperator subject to a maximum specified pressure drop. Additionally, the design analysis indicated that an optimum PPF recuperator would be attained for the minimum allowable pin transverse spacing, minimum pin width, minimum pin height and near maximum cell aspect ratio. Moreover, at a low material requirement of 0.216 kg/kW and a pressure drop, which is almost five times lower than the allowable pressure drop design target, the optimized PPF heat exchanger has the high potential to be an alternative to a printed circuit heat exchanger, which is a conservative design basis for the current state-of-the-art sCO 2 recuperators.

42 ENGINEERING↗
DOE OSTI2026

Routes to high-performance operation in Wendelstein 7-X: turbulence suppression with shaping of the density profile

Steep density gradients generally lead to improved plasma performance in the neoclassically optimized stellarator W7-X. This is evident in the global energy confinement time as well as in the ion temperature and can be explained by a strong reduction of the ion temperature gradient turbulence. Such conditions can experimentally be realized by several methods: injection of cryogenic hydrogen pellets, appropriate combination of neutral beam and electron cyclotron resonance heating (ECRH) and, in some cases, with low power of ECRH after preconditioning of the first wall. The duration of the improved phases is determined by the ability to sustain the steep density gradient, by technical limitations of the involved systems and, eventually, by the plasma stability. This paper gives an overview of relevant experimental results and presents example discharges where the improved confinement conditions could be extended to multiple seconds: up to 4 s using neutral beam injection and from 14 to 40 s with steady state pellet injection. In these plasmas the turbulent thermal diffusivity is reduced by a factor of 3 to 4 in a broad radial range, which allows high ion temperatures of up to 3 keV at the densities of about 1.5 • 10 20 m −3 .

high performance plasma↗
DOE OSTI2022

Performance Assessment of High-Efficiency Refrigerated Display Cases with Low-GWP Refrigerants: Preprint

As California policy makers enforce new regulations such as SB 1383 to achieve state greenhouse gas (GHG) reduction goals, it will be critical to understand the energy efficiency implications of low global warming potential (GWP) refrigerants in commercial refrigeration applications. The goal of this research project, funded by Commonwealth Edison was to assess the energy performance of two high efficiency medium-temperature, self-contained, reach-in display cases that utilized natural refrigerant propane (R-290), and hydrofluorocarbon (HFC) drop-in hydrofluoroolefin R-513a, respectively. Performance of these display cases were compared to a baseline fixture using HFC (R-134a) under equal conditions in a controlled environment chamber. These display cases were selected due to their widespread use in convenience stores, and small supermarkets. The high efficiency cases were equipped with energy efficient lighting, improved panel insulation, high efficiency evaporator and condenser fan motors, and high effectiveness heat exchangers. The test method used in this project was foundationally inspired by the ANSI/ASHRAE 72-2018 standard method to evaluate each case under equivalent conditions. However, minor modifications to this methodology were implemented to better represent more realistic operation of the units. While maintaining target product temperature, the energy efficient fixtures containing R-290 and R-513a consumed 61.8% to 32.6% less energy, respectively, than a baseline case using R-134a refrigerant. The daily energy consumption of the R-290, R-513a, and baseline display cases at the upper limit of environmental conditions was 4.30, 7.59, and 11.26 kWh/day, respectively.

alternative refrigerants↗
DOE OSTI2021

Surface Modification of Nickel-Rich Cathode Materials by Ionically Conductive Materials at Room Temperature

Nickel-rich cathode materials (LiNi x Mn y Co 1-x-y O 2 , NMC) are promising to push the limits of lithium-ion batteries to higher energy density. This approach is favored in practical applications including electric vehicles and aviation transportation. However, the instability of the NMC at charged state results in safety concerns and poor cycling stability. Surface coating is a practical and effective strategy to address this. Cubic Li7La3Zr2O12 (LLZO) is an ideal coating material regarding the low electron transfer rate and high Li+ conductivity. Thus, a LLZO coating layer can suppress the parasitic reactions in the electrode/electrolyte interface without deteriorating the lithium migration. However, high-temperature calcination seems to inevitably create the chemical couplings between NMC and LLZO, leading to the interdiffusion and degradation of NMC and LLZO crystal structures. This work presents a simple mechanochemical method to effectively coat LLZO on the NMC particle surface. Furthermore, the coated cathode demonstrates superior electrochemical performance compared with the pristine NMC cathode.

25 ENERGY STORAGE↗
DOE OSTI2022

Influence of microstructural heterogeneities on small-scale mechanical properties of an additively manufactured Al-Ce-Ni-Mn alloy

Laser powder bed fusion-based additive manufacturing (AM) is a promising method to fabricate creep-resistant Al-rare earth alloys (Al-Ce-Ni-Mn) with stable microstructures at up to 400°C. However, creep testing of these alloys at high temperatures shows that void coalescence and failure initiation occurs along the melt pool boundaries in the microstructure. Hence it is crucial to understand how the local mechanical behavior of the melt pool boundaries would influence the global properties of the AM produced alloy. In this study, in situ nanoindentation conducted at room temperature and 300°C revealed a reduced hardness at the melt pool boundaries. Similarly, micro-pillar compression showed a slight decline in yield strength at these boundaries, indicating that they are the weak spots in the microstructure. Such multimodal local mechanical property studies are necessary for understanding the influence of melt pool boundaries on the bulk response of fusion-based AM alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Joining Light Metals with Polymer Composites Through Metal Overcasting

This work investigates a unique technique to join aluminum (Al) and magnesium (Mg) alloys to carbon fiber reinforced polymer (CFRP) composites without the use of adhesives or mechanical fasteners. The joints were made using an overcasting technique where the molten alloys were cast around the polymer composites using high pressure die casting method. Rapid cooling during casting allowed the composite to be embedded inside the cast metal without causing any gross damage to the former even though the molten metal temperature exceeded the melting point of the composite matrix by several 100’s of deg. C. The metal/composite interface was examined using optical microscopy and x-ray imaging and the joint strength was examined through tensile testing. Although evidence of polymer melting was evident in the x-ray images, the embedded section of the composite coupons retained their overall shape and integrity. Preliminary tension tests on Al/CFRP composites showed strength degradation of the CFRP and it failed near the metal/CFRP interface (outside the joint). Strength degradation in the CFRP suggests that the overcasting process needs further optimization to minimize thermal excursion in the CFRP. Nevertheless, the overcasting process shows great promise as a unique joining technique with application in fabricating light-weight structures for automotive and transportation industries.

overcasting, Casting, Mg Castings, Al Casting, Joi↗
DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Additive manufacturing of defect-free TiZrNbTa refractory high-entropy alloy with enhanced elastic isotropy via in-situ alloying of elemental powders

Laser powder-bed fusion (L-PBF) additive manufacturing presents ample opportunities to produce net-shape parts. The complex laser-powder interactions result in high cooling rates that often lead to unique microstructures and excellent mechanical properties. Refractory high-entropy alloys show great potential for high-temperature applications but are notoriously difficult to process by additive processes due to their sensitivity to cracking and defects, such as un-melted powders and keyholes. Here, we present a method based on a normalized model-based processing diagram to achieve a nearly defect-free TiZrNbTa alloy via in-situ alloying of elemental powders during L-PBF. Compared to its as-cast counterpart, the as-printed TiZrNbTa exhibits comparable mechanical properties but with enhanced elastic isotropy. This method has good potential for other refractory alloy systems based on in-situ alloying of elemental powders, thereby creating new opportunities to rapidly expand the collection of processable refractory materials via L-PBF.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Photoelectron spectroscopy in molecular physical chemistry

Photoelectron spectroscopy has long been a powerful method in the toolbox of experimental physical chemistry and molecular physics. Recent improvements in coincidence methods, charged-particle imaging, and electron energy resolution have greatly expanded the variety of environments in which photoelectron spectroscopy can be applied, as well as the range of questions that can now be addressed. In this Perspectives Article, we focus on selected recent studies that highlight these advances and research areas. Furthermore, the topics include reactive intermediates and new thermochemical data, high-resolution comparisons of experiment and theory using methods based on pulsed-field ionisation (PFI), and the application of photoelectron spectroscopy as an analytical tool to monitor chemical reactions in complex environments, like model flames, catalytic or high-temperature reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗