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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

FY2017 Materials Annual Progress Report

The Materials Technology subprogram supports the Vehicle Technology Office’s mission to help consumers and businesses reduce their transportation energy costs while meeting or exceeding vehicle performance expectations. The Propulsion Materials research portfolio seeks to develop higher performance materials that can withstand increasingly extreme environments and address the future properties needs of a variety of high efficiency powertrain types, sizes, fueling concepts, and combustion modes. Advanced Lightweight Materials research enables improvements in fuel economy by providing properties that are equal to or better than traditional materials at a lower weight. Because it takes less energy to accelerate a lighter object, replacing cast iron and traditional steel components with lightweight materials such as high-strength steel, magnesium (Mg), aluminum (Al), and polymer composites can directly reduce a vehicle’s fuel consumption. Materials technology activities focus on the following cost and performance targets: (1) enable a 25 percent weight reduction for light-duty vehicles including body, chassis, and interior as compared to a 2012 baseline at no more than a $5/lb-saved increase in cost; and (2) validate a 25 percent improvement in high temperature (300°C) component strength relative to components made with 2010 baseline cast Al alloys (A319 or A356) for improved efficiency light-duty engines.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development and Validation of Predictive Models for In-Cylinder Radiation and Wall Heat Transfer

The lack of accurate submodels for in-cylinder radiation and heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop and design combustion systems for advanced high-efficiency, low-emissions engines. Measurements of wall boundary layers in engines show discrepancies of up to 100% with respect to standard CFD boundary layer models. And analysis of in-cylinder radiation based on the most recent spectral property databases and high-fidelity radiative transfer equation solvers has shown that at operating pressures and exhaust-gas recirculation levels typical of modern compression-ignition truck engines, molecular gas radiation (mainly carbon dioxide and water vapor) can be more important than soot particle radiation, and that a significant fraction of the emitted radiation (50% or more) can be reabsorbed before reaching the walls, thereby acting to redistribute energy in the combustion chamber. In this project, an integrated experimental and simulation approach has been used to generate new physical insight into in-cylinder heat-transfer processes in engines, and to develop computationally practicable CFD-based models that accurately represent the physics. New experimental data have been taken in an optically accessible single-cylinder research engine, and in a multi-cylinder production engine. Those data have provided new insight into the radiative environment in engines. New CFD models have been developed and validated for in-cylinder heat transfer, focusing mainly on radiative heat transfer and coupling between turbulent boundary layer wall heat transfer and radiative heat transfer. The CFD models have been developed and implemented in an open-source software framework that can be shared with other researchers. The models are compatible both with unsteady Reynolds-averaged Navier Stokes (URANS) formulations and with large-eddy simulations (LES) of in-cylinder processes in engines.

02 PETROLEUM↗
DOE OSTI2021

COTS Data Analytics Software User Manual: Version 1.0

Large volumes of data are being collected by Sandia National Laboratories as part of an active commercial-off-the-shelf (COTS) part testing and surveillance program. This user manual documents Python-based COTS Data Analytics software that has been developed for standardizing, displaying, visualizing, and analyzing the resulting COTS part testing and surveillance data. It is the objective of these software tools to streamline the analysis of COTS testing and surveillance data and improve the efficiency with which test engineers and data analytics experts can pinpoint possible performance and reliability problems in COTS parts.

42 ENGINEERING↗
DOE OSTI2022

Computational Fluid Dynamics Simulations to Predict Oxidation in Heat Recovery Steam Generator Tubes

Heat Recovery Steam Generators (HRSGs) are widely used across United States in combined cycle power plants to recover waste heat from the gas turbine (GT). HRSGs are used either to generate electricity or to produce process steam for industrial applications. The primary components of HRSG consists of a duct and a heat exchanger (HX). It is known across the power industry that the high temperature oxidation and ensuing exfoliation problem is a major cause for the damage of HX materials of HRSG. Alloys and/or coatings that can prevent or mitigate oxidation are very expensive, therefore they must be used or applied on the select regions of the HX tubes where the tendency of oxide formation is the highest. The main goal of this project is to identify such regions through Computational Fluid Dynamics (CFD) simulations. Therefore, in this work, we developed a CFD framework using commercial code StarCCM+ for the prediction of the fluid flow and heat transfer in a HRSG and associated oxidation inside the tubes of the HX. The developed CFD framework was verified and validated with experimental data before deployment. We also developed an innovative method to model the effect of the fins on the heat transfer and the pressure drop using a porous media model (PMM) approach to keep the mesh size within reasonable limits. After validation, we performed high-fidelity CFD simulations of a real-scale HRSG using the PMM, with High Performance Computing (HPC) resources of ORNL. From the simulation results, we acquired oxide thickness maps for all the tubes of the select HX sections of HRSG prone to oxidation. These oxide maps can inform regions of the HX tubes that requires oxide-resistant coatings, thereby guiding engineers for cost-efficient manufacturing of the HX that can combat oxidation in HRSGs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗
DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗
DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Servicing and Repair of High-Performance Equipment

Smooth transition from maintenance of high-performance equipment on the basis of time (after a fixed number of runs or cycles) to maintenance in terms of its condition is considered. A proposal is made for increasing the period between maintenance sessions and also decreasing the time and cost of each session. Approaches have been developed at Irkutsk Aviation Plant for the formulation and implementation of maintenance protocols on the basis of operational experience with the specific equipment.

42 ENGINEERING↗
DOE OSTI2020

Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks

Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.

09 BIOMASS FUELS↗
DOE OSTI2017

Performance, Cost, and Market Share of Conventional Vehicle Efficiency Technologies? Retrospective Comparison of Regulatory Document Projections for Corporate Average Fuel Economy and Greenhouse Gas Standards

This paper conducted an analysis of regulatory documents on current energy- and greenhouse gas–relevant conventional vehicle efficiency technologies in the corporate average fuel economy standards (2017 to 2025) and greenhouse gas rulemaking context by NHTSA and EPA. The focus was on identifying what technologies today—as estimated now (2015 to 2016)—receive higher or lower expectations with regard to effectiveness, cost, and consumer adoption than what experts projected during the 2010 to 2011 rulemaking period. A broad range of conventional vehicle efficiency technologies, including gasoline engine and diesel engine, transmission, accessory, hybrid, and vehicle body technologies, was investigated in this analysis. Finally, most assessed technologies were found to have had better competitiveness than expected with regard to effectiveness or costs, or both, with costs and market penetration more difficult to predict than technology effectiveness.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Crystalline Orientation–Dependent Spin Hall Effect in Epitaxial Platinum

We report on the spin Hall effect in epitaxial Pt films with well-defined crystalline (200), (220), and (111) orientations and smooth surfaces. The magnitude of the spin Hall effect has been determined by spin–torque ferromagnetic resonance measurements on epitaxial Pt/Py heterostructures. We observed a 54% enhancement of the charge-to-spin conversion efficiency of the epitaxial Pt when currents are applied along the in-plane <002> direction. Temperature-dependent harmonic measurements on epitaxial Pt/Co/Ni heterostructures compared to a polycrystalline Pt/Co/Ni suggest the extrinsic mechanism underlying spin Hall effect in epitaxial Pt. Our work contributes to the development of energy-efficient spintronic devices by engineering the crystalline anisotropy of non-magnetic metals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Advances in S. cerevisiae Engineering for Xylose Fermentation and Biofuel Production: Balancing Growth, Metabolism, and Defense

Genetically engineering microorganisms to produce chemicals has changed the industrialized world. The budding yeast Saccharomyces cerevisiae is frequently used in industry due to its genetic tractability and unique metabolic capabilities. S. cerevisiae has been engineered to produce novel compounds from diverse sugars found in lignocellulosic biomass, including pentose sugars, like xylose, not recognized by the organism. Engineering high flux toward novel compounds has proved to be more challenging than anticipated since simply introducing pathway components is often not enough. Several studies show that the rewiring of upstream signaling is required to direct products toward pathways of interest, but doing so can diminish stress tolerance, which is important in industrial conditions. As an example of these challenges, we reviewed S. cerevisiae engineering efforts, enabling anaerobic xylose fermentation as a model system and showcasing the regulatory interplay’s controlling growth, metabolism, and stress defense. Enabling xylose fermentation in S. cerevisiae requires the introduction of several key metabolic enzymes but also regulatory rewiring of three signaling pathways at the intersection of the growth and stress defense responses: the RAS/PKA, Snf1, and high osmolarity glycerol (HOG) pathways. The current studies reviewed here suggest the modulation of global signaling pathways should be adopted into biorefinery microbial engineering pipelines to increase efficient product yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Knock Mitigation Effectiveness of EGR across the Pressure-Temperature Domain

Exhaust gas recirculation (EGR) has been shown to enable efficiency improvements in SI engines through multiple different mechanisms, including decreasing the knock propensity at high load, which allows higher compression ratio. While many of the benefits of EGR are applicable to both low and high power density engines, including reductions in pumping work and improved specific heat ratio, the knock benefits and corresponding compression ratio increases have been limited to low power density naturally aspirated engines primarily intended for hybrid vehicle architectures. An earlier study [1] indicated that there may be a kinetic limitation for the ability of EGR to mitigate knock under these conditions, but that study only considered a small number of conditions. In this investigation, we expand on that study while also providing data for model validation for the new light-duty combustion consortium from the U.S. Department of Energy: Partnership for Advancing Combustion Engines (PACE). In this investigation, the effectiveness of EGR to mitigate knock is studied with regards to the effect of engine speed (1,500 and 3,000 rpm), changing trajectory in the pressure-temperature domain by varying the intake manifold temperature (35, 60, and 90 deg C), and by considering the effect of minor species by studying the effect of untreated EGR vs. EGR that has been treated by an automotive three-way catalyst. Additionally, to increase the relevance of these data for future modeling studies, the performance of the full boiling range gasoline was compared relative to a surrogate formulation. The study found that the fuel surrogate performs well, confirmed the kinetic limitations of EGR to mitigate knock under boost, and showed improvements in EGR performance with catalyzed EGR.

42 ENGINEERING↗