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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of a pulsed, variable-energy positron beam for atomic scale defect studies

Positron annihilation spectroscopy provides a sensitive means of non-destructive characterization of materials, capable of probing single atom vacancies in solids with 10 –7 sensitivity. Here, in this work, we detail the development of a magnetically guided, variable energy, pulsed positron beam designed to conduct depth-dependent defect studies in metals, semiconductors, and dielectrics, which will be the first of its kind in the United States. The design of the target stage provides capabilities for measurements during in situ annealing up to 800 °C and incorporates a new approach to minimize the background due to energetic backscattered positrons. The developed beam at Bowling Green State University provides a powerful tool for characterization of thin films, devices, and ion irradiated materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging

Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionalities from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in 130 nm technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous 1 MHz regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

GNSS Installation at the ARM North Slope of Alaska Atmospheric Observatory Field Campaign Report

GRUAN, the Global Climate Observing System (GCOS) Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks) and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Material Control & Accountancy for Molten Salt Reactors (FY2021 Report)

There is significant domestic and international interest, investment, and research and development momentum to pursue advanced nuclear reactor technologies. Molten salt reactor (MSR) concepts display the largest variability in fuel type and design features among the current advanced concepts. MSRs have been proposed with various core designs, sizes (power), and fuel cycles. Salt-fueled molten salt systems represent the only advanced reactor type with fuel that is not in a solid form during operation. These “liquid-fueled” MSRs are unique from perspectives of fuel fabrication, spent irradiated fuel and waste components, licensing, and material control and accountability (MC&A) including the potential of fissile material holdup. The liquid fuel salt is the defining distinction in comparison to other advanced reactors that propose TRI-structural ISOtropic particle fuel pebbles, various coolant options (e.g., molten salts or metals, high temperature gas), or small modular alternatives using solid fuel variants including both light water reactors and non-light water reactors. MSRs are appealing to the nuclear energy industry because of the diverse reactor characteristics they can support including various neutron energy spectra, fueling requirements, fuel cycles, and/or fuel utilization. However, because of the significant deviation and diversity of a salt-fueled system compared to traditional solid fuel light water-cooled reactors (LWRs), the history, regulatory licensing framework, modeling capabilities, and supporting engineering technology are either lacking or, in some cases, nonexistent. Therefore, the research community is actively supporting advanced MSR development on many of these fronts in particular to assist MSR vendors with licensing requirements. ORNL is leading the research and development of respective MC&A approaches for salt-fueled MSRs. This report summarizes the research performed at Oak Ridge National Laboratory (ORNL) under the US Department of Energy, Office of Nuclear Energy, Advanced Reactor Safeguards (ARS) program to investigate safeguards and security by design concepts, licensing and regulatory considerations, and dynamic system-level modeling to understand radioisotope concentrations for salt-fueled MSRs. The report builds upon the previous research and literature, identifies the MC&A challenges inherent to a salt-fueled MSR, reviews current regulatory frameworks for LWRs and their applicability towards salt-fueled MSRs, summarizes the status and progress of an MSR dynamic modeling tool, and discusses a prospective MC&A approach based on the Molten Salt Demonstration Reactor (MSDR) model.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Session 3: Agrivoltaics Pathways [Slides]

This presentation was developed for a webinar series for the USAID's South Asia Group for Energy (SAGE) and South Asia Regional Energy Partnership (SAREP). The presentation includes insights into agrivoltaic site assessment planning, including what is needed for a farm assessment, solar panel system design from the agricultural perspective, variables that go into crop selection, and environmental impact, sustainability, and agritourism, technical parameters for developing an agrivoltaics project, including what makes up a PV feasibility study, understanding variables that go into technology and equipment selection, agrivoltaic installation and agricultural integration, and monitoring and maintenance throughout the life of the project, and agrivoltaic financial planning, risk mitigation, and debt equity issues.

14 SOLAR ENERGY↗
DOE OSTI2025

GNSS Installation at the ARM Southern Great Plains (SGP) Atmospheric Observatory Field Campaign Report

GRUAN, the GCOS Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks), and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

GNSS Installation at the Eastern North Atlantic (ENA) Atmospheric Observatory Field Campaign Report

GRUAN, the Global Climate Observing System (GCOS) Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks), and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dissimilar joining of ZEK100 and AA6022 for automotive application

Mg and Al joining is problematic due to different high temperature flow characteristics that often results in weld defects and brittle intermetallic compounds (IMC). We present process development on lap joining of 1.27 mm 6022 (top sheet) and 1.5mm ZEK100 (bottom sheet) for an automotive door application. We conducted process development in linear welding using power control mode (modulating torque and spindle speed) with multiple tool designs, and welding parameters. Process variables were correlated to joint micro-structure and mechanical properties. A 2D computational model was created to understand the failure mechanism and contribution of mechanical interlocking towards joint strength. For a thin (< 2 mm) automotive sheet with softer material as a bottom sheet, adequate control of process variables is critical to maintain weld stability and avoid excessive IMC layer at the interface. A combination of mechanical anchoring and metallurgical reaction is likely responsible for bonding.

Das, Hrishikesh↗
DOE OSTI2023

Experimental Verification of An Electro-Hydraulic Actuation System Driven by An Integrated Electro-Hydraulic Unit

This paper proposes an electro-hydraulic actuator (EHA) system, and a novel-designed electro-hydraulic unit (EHU) consisting of a fixed-displacement hydraulic gear machine and a variable-speed electric machine. The novel EHU design integrates an electric machine and a hydraulic machine in a single housing, targeting optimal compactness, power density, and component reduction. The EHA system features an open circuit design, where hydraulic hoses connect the EHU with a tank, a valve manifold, and the hydraulic cylinder. In this way, the proposed EHA technology can be used to implement distributed hydraulic actuation in a vehicle without requiring changes to the hydraulic actuators or at the overall layout of the hydraulic components with respect to the original vehicle design. A dedicated test rig is developed to verify the performance of the proposed EHA system. Here, the efficiency of the EHA is measured in a steady state and under a realistic duty cycle of a commercial compact loader. The efficiency of the EHA system based on the measurements on the test rig can reach 54% with up to 20 kN load and 6 kW power level. Furthermore, the EHU is implemented on the reference machine to drive the boom function. Preliminary results on the performance of the EHU show an efficiency of up to 68% under different loading conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO 2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2020

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Limited Role of Absolute Humidity in Intraurban Heat Variability

Abstract Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Practical choices for space-filling designs

Space-filling designs are now commonly used as a flexible model-free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make these critical choices is often under-emphasized in the literature. In this paper, we explore several of the practical choices required by the experimenter and describe a sequence of steps to help create an ideal design that matches the goals and constraints of the experiment. These choices include the specification of the input space, the scaling of the variables, the degree of uniformity of the design points across the input space, and the space-filling characteristics. In addition, some new tools for defining the weights to implement non-uniform space-filling designs are provided. Finally, the methods are demonstrated with several illustrative examples and a real-world chemical engineering experiment for carbon capture.

42 ENGINEERING↗
DOE OSTI2023

Crystal chemistry at high pressure

The chemistry we are taught in school, and we experience in our daily existence occurs at 1 atm. However, pressure spans an astounding 62 orders of magnitude in the Universe in going from the void of interstellar space to the crushing conditions at the center of a neutron star. The way in which pressure affects chemistry is important for Earth and planetary sciences, materials science, in understanding the extreme conditions experienced in nuclear explosions, and it may be key in addressing the future energy needs of our society. In this article we outline how the often neglected pressure variable affects chemistry, beginning from the way in which atomic energy levels are altered. This lays the foundation for understanding the unique crystal and electronic structures that emerge when matter is squeezed, as well as pressure’s effect on chemical reactivity. Finally, we give an overview of the main concepts behind conventional, or phonon-mediated, superconductivity, and describe how the pressure variable may be key in discovering and designing light-element based materials whose superconducting critical temperatures approach room temperature. Here, we discuss some of the main families of superconducting hydrides that have been predicted computationally, and the experimental successes in this exciting and rapidly developing field.

Chemical bonding↗