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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

1.1 eV GaInAs Cell Development for Dual-Use Solar and 1070 nm Laser Power Converters

Dual-use photovoltaic cells can receive solar and laser power simultaneously to generate current, an application relevant to space and terrestrial industries. This study investigates two concepts of solar cells optimized for dual-use 1070 nm laser and solar power conversion, a single-junction and triple-junction cell. The cell designs are based upon the 3- junction inverted metamorphic solar cell, previously shown highly efficient for solar conversion. Because it is closely bandgap-tuned for a 1070 nm laser, the 1 eV bottom junction is incorporated into each of the two designs, making its development key to the success of both concepts. However, each design requires some modification for efficient dual use. The single-junction device requires optimization to reduce short wavelength absorption of the broad solar spectrum. In both devices, the graded buffer layers in the GaInAs cell affect the cell's performance by reducing threading dislocations in the active junction. However, the buffer in the three-junction device also acts as a lateral transport layer and so affects the fill factor depending on its sheet resistance. By varying the buffer thickness, we demonstrate a direct relationship between buffer thickness and sheet resistance reduction, while considering implications to open-circuit voltages. We also performed resistance modeling to determine the optimal grid spacing and thickness of the grid fingers to minimize losses due to sheet resistance and grid shading. Efficiency data for a one junction GaInAs cell demonstrates a laser conversion efficiency of 38% at 1070 nm wavelength without an anti-reflection coating.

dual-use↗
DOE OSTI2021

Interpretation of hydrothermal conditions, production-injection induced effects, and evidence for enhanced geothermal system-type heat exchange in response to >30 years of production at Roosevelt Hot Springs, Utah, USA

The Roosevelt Hot Springs hydrothermal system is located at the base of the Mineral Mountains in southwestern Utah on the eastern side of the Basin and Range. Hydrothermal activity is related to relatively recent bimodal magmatism, and the system is hosted in coarsely crystalline rock made of Oligocene–Miocene granitoids and Precambrian gneiss. The hydrothermal plume covers ~5 km 2 , with a maximum temperature of 268 °C at ~750 m depth, and a vertically extensive fault-fracture mesh east of the Opal Mound fault controls the upflow of hydrothermal fluids. Power generation (currently 38 MWe gross) began in 1984, and up through 2016, four wells were used for fluid production, and three wells were used for edge-field injection. Chemical analyses of produced fluids show that modern reservoir fluid compositions are similar to but more concentrated than those at the start of production, having near-neutral pH, total dissolved solids of 7000–10,000 mg/kg, and ionic ratios of Cl/HCO 3 ~50–100, Cl/SO4 ~50–100, and Na/K ~4–5. Chemical geothermometers indicate equilibration temperatures that mainly range between 240° and 300 °C. Early production induced a steep drop in pressure (~3.0–3.5 MPa), which was accompanied by a 250–300 m lowering of piezometric levels in wells and development of a shallow steam zone across the system. Hydrothermal fluid compositions evolved continuously in response to production-related steam-loss and injection breakthrough, which is reflected by gradual increases in chloride of up to 35% and stable isotope ratios of up to ~2‰δ 18 O and ~10‰δD. Simple mixing model calculations suggest that there has been a significant amount, ~10–20 MW th , of sustained multi-decadal heat mining and enhanced geothermal system (EGS)–type heat transfer by the injectate as it returns to the production zone. Overall, the two factors that have sustained long-term power production (currently 38 MWe gross) are the increased upflow of deep chloride water and, to a lesser extent, the mining of heat at <1 km depth.

58 GEOSCIENCES↗
DOE OSTI2022

An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

Quantum computing is a promising technology that harnesses the peculiarities of quantum mechanics to deliver computational speedups for some problems that are intractable to solve on a classical computer. Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in terms of chip size and error rates. Shallow quantum circuits with uncomplicated topologies are essential for successful applications in the NISQ era. In this work, based on matrix analysis, we derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The depth of the compressed circuits is independent of simulation time and grows linearly with the number of spins. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond O(10 3 ) spins. The resulting quantum circuits have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.

Hamiltonian simulation↗
DOE OSTI2019

Modeling Universal Globally Adaptive Load-Balanced Routing

Universal globally adaptive load-balanced (UGAL) routing has been proposed for various interconnection networks and has been deployed in a number of current-generation supercomputers. Although UGAL-based schemes have been extensively studied, most existing results are based on either simulation or measurement. Without a theoretical understanding of UGAL, multiple questions remain: For which traffic patterns is UGAL most suited? Furthermore, what determines the performance of the UGAL-based scheme on a particular network configuration? Here, we develop a set of throughput models for UGALbased on linear programming. We show that the throughput models are valid across the torus, Dragonfly, and Slim Fly network topologies. Finally, we identify a robust model that can accurately and efficiently predict UGAL throughput for a set of representative traffic patterns across different topologies. Our models not only provide a mechanism to predict UGAL performance on large-scale interconnection networks but also reveal the inner working of UGAL and further our understanding of this type of routing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.

Mechanics↗
DOE OSTI2023

ARES v1.x - Performance Portable Tool to Simulate Supernovae based on Parthenon Framework

Historically, codes for simulating supernovae (such as Arepo, FLASH or LEAFS) have been at the forefront of scientific high-performance computing to the immense computational resources required for full 3D simulations. However, given the shift towards heterogenous HPC architectures, many current-generation codes are at the risk of losing their competitiveness as they are only designed to run on homogeneous CPU-only systems. There exist several efforts to enable these codes for GPU’s, however, these efforts only consider specific architectures or vendors (e.g., implement only CUDA or HIP), limiting themselves to a small range of exascale computing systems. Frameworks such as Kokkos aim to provide a framework which is agnostic of the targeted architecture, enabling the development of performant and portable code. In the Ares code, we develop a performance portable tool to simulate supernovae based on the Parthenon Framework, which in turn uses Kokkos in the background. Here, the Parthenon Framework provides an interface to the underlying mesh-refinement routines, which form the backbone of our code. In addition, we incorporate the already existing Singularity-EOS toolkit to provide us with various equations of state, primarily the Helmholtz equation of state. We also include the JINA Reaclib as a basis for our nuclear network solver. Finally, we implement a gravity solver to complete the required physics. This setup will provide us with a minimal code base to simulate supernova in a similar style to the tried-and-tested Arepo code, but in a futureproof performance portable framework.

Lim, Hyun↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing Tropical Cyclones’ Contribution to Precipitation over the Eastern United States and Sensitivity to the Variable-Resolution Domain Extent

Tropical cyclones (TCs) can subject an area to heavy precipitation for many hours, or even days, worsening the risk of flooding, which creates dangerous conditions for residents of the U.S. East and Gulf Coasts. To study the representation of TC-related precipitation over the eastern United States in current-generation global climate models, a novel analysis methodology is developed to track TCs and extract their associated precipitation using an estimate of their dynamical outer size. This methodology is applied to three variable-resolution (VR) configurations of the Community Atmosphere Model, version 5 (CAM5), with high-resolution domains over the North Atlantic and one low-resolution conventional configuration, as well as to a combination of reanalysis and observational precipitation data. Metrics and diagnostics such as TC counts, intensities, outer storm sizes, and annual mean total and extreme precipitation are compared between the CAM5 simulations and reanalysis/observations. The high-resolution VR configurations outperform the global low-resolution configuration for all variables in the North Atlantic. Realistic TC intensities are produced by the VR configurations. The total North Atlantic TC counts are lower than observations but better than reanalysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On-chip terahertz emission from Floquet-Bloch states [Invited]

Floquet engineering uses time-periodic electromagnetic fields to modify the electronic properties of quantum materials via the creation of Floquet-Bloch states. These photon-dressed states inherit features from both the material and the driving field, enabling the exploration and control of quantum phenomena in light-matter hybrid systems. In non-centrosymmetric materials, shift currents can arise from the quantum geometric properties of electronic wavefunctions. However, shift currents from Floquet-Bloch states remain experimentally unexplored. Here, we employ an on-chip optoelectronic circuit to detect intrinsic terahertz emission from Floquet-Bloch states in T d -WTe 2 under intense optical driving. We observe strong edge-localized terahertz emission that scales linearly with the driving field, consistent with the theoretical prediction for shift currents generated by Floquet-Bloch states. The results advance our understanding of strongly driven quantum materials and provide insights for developing efficient, bias-free terahertz sources for future optoelectronic technologies.

Li, Xinyu [Max Planck Institute for the Structure ↗
DOE OSTI2024

Spinning metasurface stack for spectro-polarimetric thermal imaging

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffers from limitations in size, spectral resolution, and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce an approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressive sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro-polarimetric stack is compact (<10×10×10cm) and robust, and it offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).

Optics↗
DOE OSTI2025

Preparation of 1,3-Dihydroxyphenazine

The ability to effectively store energy produced by intermittent renewable sources is a critical challenge for chemists and materials scientists. Redox flow batteries (RFB), which generate current by the flow of electrons between dissolved redox active compounds in separate solutions, are envisioned as a method to store renewable energy at the electrical grid scale. The best known examples of RFBs are driven by redox active metal or main group complexes. Within the past decade, redox active organic molecules have begun to be used in the construction of RFBs with high cell potential and cycle stability, at economical price points. Of particular interest are substituted dihydroxy phenazines, a class of heterocycles, that have recently been used as an anolyte for aqueous organic RFBs. Recent work indicates that different regioisomers of dihydroxyphenazine show dramatically different solubility and stability under electrochemical cycling conditions. Of particular interest was 1,3-dihydroxyphenazine (1,3-DHP), which showed greater than 1.5 M solubility in 2M KOH and excellent electrochemical stability. Current methods of producing 1,3-DHP, however, are low yielding and cumbersome. In the article proposal that follows, we offer an improved method of producing 1,3-DHP at high purity and moderate yield. The method detailed below was used by the Materials Engineering Research Facility (MERF) at Argonne National Lab to deliver more than 1.5 kg of this compound for use in the construction of aqueous organic RFBs.

Dzwiniel, Trevor [Argonne National Laboratory (ANL↗
DOE OSTI2026

Automated Vehicle Feasibility Study

This study collected automated vehicle (AV) performance data on public roadways in Athens, Ohio. The route for the study contained a combination of roads with different functional classifications, conditions, annual average daily traffic, and ownership responsibilities for maintenance and repair. Preparation for the public road deployment was done in a controlled environment at Transportation Research Center’s SMARTCenter, a dedicated AV test facility in East Liberty, Ohio. Researchers analyzed data and extracted insights relevant for both AV developers and infrastructure owners and operators. The study found that rural environments offer a unique set of roadway features such as hills and curves, which can challenge the driving behavior of an AV. Rural regions can also contain a large number of low-traffic gravel roads that lack pavement markings, which appear to be a crucial infrastructure element for operation of current generation AVs. Similarly, the presence of well-maintained lane lines along curves can influence the AV’s roadway departure tendencies. The study found that curvature-related behavior of an AV is also influenced by driving speed on the roadway segment. Such findings were consistent regardless of the time of day along the route or season of data collection. However, commentary about AV performance in active adverse weather cannot be made, as this is still an area of active research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Advanced Microscopy and Spectroscopy for Probing and Optimizing Electrode-Electrolyte Interphases in High Energy Lithium Batteries (Final Scientific/Technical Report)

The earliest developed LiCoO 2 layered oxide cathode material sparked the development of other layered cathode materials, dominating the positive electrode materials for lithium ion batteries. Within the practical operating conditions of today, the current generation layered oxide materials do not meet the future energy storage demands of 350 Wh kg -1 at cell level. Li-rich layered oxide (LRLO) materials have the potential to meet the high energy demands. Unlike the classical layered oxides, LRLO materials exhibit capacities that go beyond conventional topotactic mechanistic theoretical values. Despite its high capacities, this material has several challenges (voltage fading, structural instability, sluggish kinetics, cathode electrolyte interphase instability, etc.) that must be overcome in order to reach commercialization. In the past five years, our research group has made great progress on developing advanced characterization techniques coupled with atomic scale modeling to properly characterize the dynamic phenomena that govern the performance limitations of LRLO materials. Furthermore, our efforts have guided the material synthesis and surface modification to improve capacity retention. By carefully controlling the oxygen activities through the creation of uniform oxygen vacancies, we were able to avoid structural decomposition in LRLO. Our materials deliver a discharge capacity as high as 306 mAh g -1 with an initial coulombic efficiency of 90.6%. Furthermore, they do not exhibit obvious capacity decay with a reversible capacity over 300 mAh g -1 after 100 cycles at 0.1 C-rate. Our approach demonstrates the critical needs for advanced diagnosis and characterization. It is through the in-depth understanding of these high voltage cathode materials at atomistic and molecular level and their dynamic changes during the operation of batteries; we can successfully formulate strategies to further optimize this class of cathode materials, especially for the voltage instabilities. The diagnostic tools developed here can also be leveraged to study anode materials such as Li metal anode. The challenge of probing Li metal is the low dose tolerance (high beam sensitivity) of Li metal. We will have to apply cryogenic method (low-temperature) and low-dose electron microscopy and specialized camera to enable the characterization of the lithium metal.

25 ENERGY STORAGE↗
DOE OSTI2020

High Penetration Power Electronics Grid: Modeling and Simulation Gap Analysis

Increased penetration of power electronics in the grid is happening through development of high-power drives (like in Type 3 or 4 wind turbines, industrial drives, etc.), high-voltage direct current (HVdc) systems, flexible alternating current transmission systems (FACTS), energy storage systems (ESSs), inverter-based renewables like solar and wind, electric vehicle chargers, and other technologies. Ongoing research and development in new power electronic technologies including, but not limited to, solid-state power substations (SSPS), extreme fast charging (XFC), solid-state transformers, and multi-port power electronics that integrate multiple sources/loads will further increase penetration levels. To ensure stakeholders can integrate high penetration of power electronic technologies safely and reliably requires tools and methods to assess and evaluate their impact on the grid. Objectives: This report surveys, assesses, and analyzes commercially available and open-source tools that can support the assessment and evaluation of power electronics in future grids with high penetration levels. The study includes aspects that range from power flow analysis to dynamics evaluation (including hardware-in-the-loop – HIL testing) for such systems. The challenges and gaps associated with the current generation of toolsets available to assess the technical impact of introducing high penetration of power electronics are reported. The method is summarized in Figure ES-1.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

High Temperature Gas Sensor for Coal Combustion System

Robust gas sensors that offer real-time feedback about combustion in coal-fired power plants promise greater energy efficiency with fewer harmful emissions while also improving the bottom line for power generators. Currently, no technology meets this objective. Therefore, the objectives of this project are to develop an accurate, robust, reliable, high temperature sensor offering near real-time feedback to plant operators using a novel catalytic/non-catalytic sensor design to detect target gases available for oxidization. Such a sensor would inform power plant operators how to adjust the amount of fuel and air for an optimal performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Solid State Ionics: Materials Development by Multiscale Modeling and Advanced Manufacturing Techniques

Solid-state ionic materials are an important enabling technology for energy conversion and storage. Solid-state batteries would be a safer and higher energy density alternative to commercially available lithium ion batteries (LIB), however their implementation requires ion conduction in solids at room temperature to occur on the same level as the current generation of liquid electrolytes. Microstructural modifications have been demonstrated to play a major role on ion transport through the control of grain boundary interfaces, which traditionally serve as “blocking” layers. Ultimately, these materials will be fabricated in thin films form as electrolytes in order to minimize ohmic losses in electrochemical devices. This work uses advanced manufacturing techniques in combination with theoretical modeling to implement a science-based approach in the deposition of thin films ion conductors with controlled microstructures used in ceramic energy conversion and storage devices.

25 ENERGY STORAGE↗