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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ∼12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2021

A Scalable Process for Upcycling Carbon Dioxide (CO 2 ) and Coal Combustion Residues into Construction Products

Anthropogenic sources of carbon dioxide are generated from a number of sources, but the key among these are ordinary Portland cement (OPC) production and combustion of fossil fuels. Cement production is the largest global CO 2 source from the mineral decomposition of carbonates. Combination of the limestone decomposition and thermal requirements of the clinkering process causes cement production to contribute 8-9% of annual global CO 2 emissions. Combustion of fossil fuels (coal, oil and gas) was shown to contribute a much larger portion of global CO 2 emissions. As of 2018, combustion of fossil fuels accounted for 65% of global CO 2 , where 41% was derived from stationary sources for electricity and heat generation and the other 24% was related to transport. To reduce these contributions, key steps forward in CO 2 utilization technologies are required. The purpose of this project is to demonstrate the feasibility of the Reversa process evolving from a TRL-3 technology at the bench-scale up to TRL-6 technology at the pilot-scale. The reliability of the Reversa technology was tested to prove the effective production of concrete masonry units (CMUs) at bench scale, where the units exceeded the required 13.8 MPa compressive strength requirements. The overall goal of this project was to accelerate the development of a CO 2 mineralization process that synergistically utilizes CO 2 in flue gas and coal combustion residues (CCRs) to synthesize carbonated concrete, a functional replacement for traditional concrete. The culmination of this work resulted in 12 successful production runs at the Integrated Test Center (ITC), Gillette, WY using coal flue gas as a CO 2 source. This was followed by 6 production runs which were completed at the National Carbon Capture Center (NCCC), Wilsonville, AL, using coal-fired and natural gas (NG) flue gas as the CO 2 source. Over the course of the production runs at NCCC and ITC, the CO 2 utilization as a function of time, 24-h CO 2 uptake, electricity usage, and 28-d net area compressive strength recorded for each run. The ITC and NCCC demonstrations achieved an average of 0.19 and 0.1 g CO 2 /g reactant, respectively. Both demonstrations exceeded the project’s target goals of uptake > 0.05 g CO 2 /g reactant. Average compressive strength of the ITC blocks was 18.24 MPa at 28-days. This exceeded the target strength of 13.8 MPa specified by ASTM C90. ITC and NCCC produced an average CO 2 utilization efficiency of 74.9 and 57.9%, respectively. Both demonstration averages were within the target range of 50 to 90% utilization efficiency. For some production runs the NCCC demo did exceeded the a CO 2 utilization efficiency of 75%. An LCA of the Reversa process compared to an industry standard product revealed a net CO 2 reduction of 39% to 42%. This exceeded the target requirement of >25% net CO 2 reduction. Collection of this data was used to determine that the project was successful as the demonstration goals were achieved: (1) achieving in excess of 75% CO 2 utilization efficiency, (2) utilizing greater than 250 kg of CO 2 per production batch/run, and (3) ensuring compliance of carbonated blocks with industry standard specifications (ASTM C90).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Studies on nonequilibrium phenomena in supersonic chemically reacting flows

This study deals with a systematic investigation of nonequilibrium processes in supersonic combustion. The two-dimensional, elliptic Navier-Stokes equations are used to investigate supersonic flows with nonequilibrium chemistry and thermodynamics, coupled with radiation, for hydrogen-air systems. The explicit, unsplit MacCormack finite-difference scheme is used to advance the governing equations in time, until convergence is achieved. For a basic understanding of the flow physics, premixed flows undergoing finite rate chemical reactions are investigated. Results obtained for specific conditions indicate that the radiative interactions vary substantially, depending on reactions involving HO2 and NO species, and that this can have a noticeable influence on the flowfield. The second part of this study deals with premixed reacting flows under thermal nonequilibrium conditions. Here, the critical problem is coupling of the vibrational relaxation process with the radiative heat transfer. The specific problem considered is a premixed expanding flow in a supersonic nozzle. Results indicate the presence of nonequilibrium conditions in the expansion region of the nozzle. This results in reduction of the radiative interactions in the flowfield. Next, the present study focuses on investigation of non-premixed flows under chemical nonequilibrium conditions. In this case, the main problem is the coupled turbulence-chemistry interaction. The resulting formulation is validated by comparison with experimental data on reacting supersonic coflowing jets. Results indicate that the effect of heat release is to lower the turbulent shear stress and the mean density. The last part of this study proposes a new theoretical formulation for the coupled turbulence-radiation interactions. Results obtained for the coflowing jets experiment indicate that the effect of turbulence is to enhance the radiative interactions.

Tiwari, S. N.↗
NASA NTRS1984

Combustion Fundamentals Research

The various physical processes that occur in the gas turbine combustor and the development of analytical models that accurately describe these processes are discussed. Aspects covered include fuel sprays; fluid mixing; combustion dynamics; radiation and chemistry and numeric techniques which can be applied to highly turbulent, recirculating, reacting flow fields.

Source record↗
NASA NTRS1997

Experimental Observations of PMMA Spheres Burning at Reduced Gravity

Polymer combustion is a very complicated process which involves the coupling of gas-phase combustion, melting, pyrolysis, and possible charring of the condensed phase. Although only a few studies on the combustion of a spherically shaped polymer have been conducted, there is renewed interest in the subject. At the Third International Microgravity Combustion Workshop, we presented a preliminary experimental plan and proposed an apparatus to study the combustion of a PolyMethylMethAcrylate (PMMA) sphere at reduced gravity. In this paper, we describe the experimental hardware in detail, summarize our observations since the last workshop, and describe future studies. The main objective is to determine the burning rate of PMMA spheres at reduced gravity under different ambient oxygen concentrations and total pressures. The dependence of the burning rate on the initial sphere diameter will be examined. It is anticipated that the simple spherical geometry in conjunction with reduced gravity will facilitate an assessment of the effect of condensed phase behavior on polymer burning processes and can be used as a means to test polymer combustion models.

Yang, Ji-Ann C.↗
DOE OSTI2021

Modeling, Design, and Testing of a Novel Biphasic Solvent-Enabled Absorption System for Post-Combustion Carbon Capture

A novel absorption process enabled by a new class of biphasic solvents for post-combustion carbon capture (BiCAP) is presently being developed at the University of Illinois at Urbana-Champaign. The new biphasic solvents are water-lean solvent blends that develop dual liquid phases with the absorbed CO2 highly enriched in one of the phases. These solvents are superior in CO2 capacity and have high thermal and oxidative stability compared to MEA. The BiCAP technology features a unique process configuration of multi-stage CO2 absorption and liquid–liquid phase separation during CO2 absorption, allowing continual separation and removal of the CO2-enriched liquid phase for maintaining low solvent viscosity and a fast absorption rate. A 40 kWe-scale skid has recently been constructed at the Abbott Power Plant in Champaign, IL, to test the BiCAP solvent in continuous operation with a slipstream of real post-combustion flue gas. Along with improvements in the stripping configuration including cold feed bypass, the process is expected to achieve a minimum reboiler duty of 2,210 kJ/kg CO2 captured at a stripper operating pressure of 6 bar.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Initiation of dusting corrosion in high-temperature alloys under CO exposure

Carbon monoxide is commonly encountered in energy systems, yet its reactivity with structural alloys—critical heat-resistant components in these systems—has been largely overlooked compared to the well-documented effects of oxidizing gases. In contrast, we demonstrate the high-temperature reaction of CO with NiAl using in-situ low-energy electron microscopy and X-ray photoemission electron microscopy. Our results show that CO dissociates into atomic oxygen and carbon, resulting in two concurrent reactions: selective oxidation of aluminum to form Al 2 O 3 and the initiation of dusting corrosion through carbon dissolution into the alloy and subsequent carbon deposition on the surface. These reactions produce spatially distinct surface products, preventing the formation of a continuous protective Al oxide layer. These results reveal a preference for the dissociative pathway of CO over the classic Boudouard disproportionation reaction that forms CO 2 . These insights not only advance our understanding of CO-induced alloy degradation but also highlight the practical implications for managing alloy stability and optimizing catalysis in carbon-rich environments, such as those in petrochemical processing and hydrocarbon combustion.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparative life cycle assessment of bioenergy in Japan from residual biomass-based wood pellets produced in the US Pacific Northwest

The US Pacific Northwest (PNW) faces an increase in wildfires due to forest overcrowding and climate change, posing significant environmental and public health risks. Traditional methods of managing surplus biomass, including prescribed burning, have increased air pollution and global warming. While in the PNW, residual woody biomass is being treated as waste, Japan’s growing demand for bio-based energy presents an opportunity to export value-added biomass as energy pellets. This study investigates whether producing wood pellets from the residual woody biomass from forest operations and sawmills for electricity generation in Japan is truly environmentally beneficial. Accordingly, we conducted a cradle-to-grave Life Cycle Assessment (LCA) to evaluate the environmental impact of residual pellets vs. coal for electricity generation. The assessment covered feedstock production, pellet processing, transportation, and combustion phases. Our findings indicate that replacing coal-based electricity in Japan with PNW residual pellet-based electricity can lower the Global Warming Potential (GWP) by about 90% for every unit of electricity displaced. Furthermore, the results show that repurposing the otherwise burnt harvest slash residues for pellet production would improve local air quality by reducing PM 2.5 and smog in the PNW. However, substituting coal with residual pellets marginally increased carcinogenic and ecotoxicity-related emissions. Based on these results, we conclude that substituting coal with residual wood pellets for electricity generation, particularly harvest-slash residual pellets, is environmentally beneficial across most impact categories, including the GWP. This research underscores how an export‑oriented pellet industry can help address the environmental challenges within the regional US wood products industry and global renewable energy supply.

Velappan, Hemalatha [Univ. of Washington, Seattle,↗
DOE OSTI2025

Resonance-stabilized radical clustering bridges the gap between gaseous precursors and soot in the inception stage

Carbonaceous particles are widespread in combustion, atmospheric, extraterrestrial, and nanomaterials environments. Resonance-stabilized radicals (RSRs) are commonly identified in fuel combustion and pyrolysis processes and play an essential role in carbonaceous particle formation. Despite their importance, comprehensive experimental and mechanistic understanding of particle inception through RSR reactions is lacking. This work investigated particle size distribution, chemical composition, and thermal behavior of soot particles generated by the flow reactor pyrolysis reactions of typical RSRs, in particular, 1-indenyl, 1-methylnaphthyl, and 2-methylnaphthyl radicals, and by the pyrolysis of hydrocarbons with a variety of structures. Particle size distributions show soot particles with mobility diameters in an incipient-particle range of 1.3 to 1.6 nm. Laser desorption/ionization mass spectrometry results suggest that soot products consist of much larger covalently bound clusters (CBCs) than those observed in the gas phase. Under our experimental conditions, the CBCs exhibit a phase transition for particles with calculated molecular diameters of around 1.5 nm. Evaporation experiments and thermogravimetric analysis of the soot products reveal distinct thermal characteristics for small and large CBCs. These results implicate CBCs as bridges between gas-phase species and soot particles. The present work provides a soot-inception mechanism called RSR clustering (RSRC) that is characterized by the reactive clustering of RSRs. The RSRC mechanism contrasts with conventional soot formation models that attribute soot inception primarily to the aggregation of large-size polycyclic aromatic hydrocarbons.

carbonaceous particle↗
DOE OSTI2024

Boron nitride: Novel ceramic reductant for low‐activity waste vitrification

Abstract During vitrification of radioactive wastes, excessive foaming reduces processing rates within melters by hindering heat transfer from the molten glass pool to the reacting melter feed. Formulations of low‐activity waste (LAW) melter feeds, for vitrification at the Waste Treatment and Immobilization Plant at the Hanford Site, conventionally include the addition of sucrose to mitigate excessive foaming by hastening the denitration process. However, incomplete combustion of sucrose produces organics such as acetonitrile (C 2 H 3 N) that may exceed bounding limits of downstream effluent treatment facilities. Using boron nitride (BN) as an alternate reductant to sucrose, in a representative LAW melter feed reduced C 2 H 3 N production by 90% by preventing the low‐temperature sucrose–nitrate reactions. Furthermore, foaming was suppressed due to the higher decomposition temperature of BN than H 3 BO 3 meaning a delayed reaction of a large fraction of boron with the transient glass‐forming melt until above the foam onset temperature, thus reducing the quantity and viscosity of the connected melt and trapping less gas in the foam layer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗
DOE OSTI2021

Visualization of Jet Impingement and Ignition in a Piston-Cylinder Chamber

The transportation sector accounts for almost a third of the United States' energy consumption. We are developing predictive simulations of the complex in-cylinder processes of internal combustion engines to improve performance and decrease pollutant emissions. We provide a first look at the jet impingement and ignition inside a piston-cylinder chamber using volume rendering. Through the visualization, we constructed a complete 3D picture of the jet impingement on the piston-bowl walls leading to the formation of pockets of auto-ignition of the fuel-air mixture. These auto-ignition pockets subsequently lead to flame stabilization, releasing the chemical energy for conversion to useful work. A detailed physical picture of flame stabilization, as provided by the visualization techniques presented in this study, help in the design of next-generation internal combustion engines.

91 SC - Advanced Scientific Computing Reseach (ASC↗
DOE OSTI2024

In-cylinder spray evolution in a motored central-injection gasoline engine: Imaging and simulating the effects of flash-boiling and intake crossflow

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. Further, it is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Geothermal Deep Direct Use for Turbine Inlet Cooling in East Texas

The National Renewable Energy Laboratory (NREL), the Southern Methodist University Geothermal Laboratory (SMU), Eastman Chemical (Longview, TX), and TAS (Houston, TX) evaluated the feasibility of using geothermal heat to improve the performance of a natural-gas power plant in East Texas. The area of interest is the Eastman Chemical plant in Longview, Texas, which is on the northwestern margin of a geologic region known as the Sabine Uplift. The feasibility study focused on determining the potential for accessing a subsurface hot-water geothermal resource within a 10-km radius of the site to provide thermal energy for absorption chillers. Wells within a 20-km radius are included for broader geological comparison to determine the heat flow, temperature-at-depth, field porosity and permeability. The lithologies of most interest are the Lower Cretaceous Trinity Group and Upper Jurassic Cotton Valley Group. The deeper Cotton Valley formations are hotter (averaging 117 to 130°C), yet permeability and porosity are low. The shallower Trinity Group contains more variability in permeability and porosity and lower temperatures averaging about 98 to 117°C. The shallower formations are considered despite the lower temperature because of increased ability to produce larger volumes of water and extract enough heat before reinjection. The complete SMU analysis is available in the National Geothermal Data System (NGDS). Tapping such deep geothermal sources for direct heating (as opposed to power generation) is known as geothermal deep direct use (DDU). Geothermal DDU has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for what are typically small-scale, variable-demand projects. This project examines the feasibility of geothermal energy integration in a natural-gas combined cycle power station in East Texas. The DDU resource is tapped to drive absorption chillers (24/7) for production of chilled water at 5-10°C (41-50°F). This chilled water is stored until needed, which allows for continuous operation with a relatively small-capacity geothermal/absorption chiller system. When conditions are favorable, the chilled water is dispatched to cool the air entering the compressor stage of a gas combustion turbine. This process, known as turbine inlet cooling (TIC), boosts power production during periods of high temperature and high-power demand. Such systems can enhance grid reliability and reduce the cost for peak-demand power. A simulation model of the power plant was developed in IPSEpro software and validated against operational data from the plant. This model allowed the team to estimate the additional power that could be produced by applying TIC under different operating and ambient conditions. Absorption chiller performance was estimated from vendor sources to determine the production rate of chilled water from the geothermal resource. Geothermal drilling and development costs were estimated using NREL's GEOPHIRES 2.0. The expected lower drilling costs in this region led to an estimated cost of geothermal heat of about $4/MMBtu (1.4 cents/kWh t ). The estimated cost for the absorption chillers and TIC hardware were obtained from literature sources and project partners. Hourly data were obtained for weather, natural gas and electricity prices, and plant operating state for 2017, which served as a representative year. NREL estimated the capital cost, operating cost, and additional electricity production and revenue for different combinations of geothermal capacity, chiller capacity, and water storage-tank size. The analysis drove toward smaller geothermal and chiller systems to reduce equipment cost. A relatively low-cost water storage tank accumulated the near-continuous chilled water output for later use when TIC was most valued.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy (Workshop Report)

The US Department of Energy’s (DOE’s) Advanced Manufacturing Office held the virtual workshop entitled “Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy” in November and December 2020. The workshop brought together participants from universities/laboratories, industries, equipment manufacturers, technology vendors, nongovernmental organizations, and subject-matter experts to discuss transformative technologies and strategies to substantially improve the performance (e.g., energy productivity, thermal efficiency, reduced greenhouse gas [GHG] emissions, reduced number of process steps) of thermal processing systems in the industrial sector. The US industrial sector accounts for 32% of the nation’s primary energy use (including feedstocks), and refining, chemicals, pulp and paper, iron and steel, and food products represent the top energy-consuming sectors. Thermal processing or process heating represents the largest energy use category; it accounts for 63% of all energy use in manufacturing. Additionally, thermal processing is the largest contributor of carbon dioxide (CO2) generation, resulting from combustion of fuels and process related chemical reactions, such as in the case of cement and lime production. The challenge of achieving net-zero industrial GHG emissions is colossal considering the established industrial base that depends mainly on carbon-based processes and energy sources; the time frame and cost to replace carbon-based energy sources and feedstocks; and the long-term outlook for development with large-scale adaptation of alternative non carbon–based technologies. The goals of the DOE Advanced Manufacturing Office Thermal Process Intensification Workshop were as follows: (1) Identify R&D gaps and opportunities to facilitate transformative improvement in industrial thermal processes beyond current technologies and allow for entirely new methods for processing materials; (2) Gain insight into new and innovative approaches to thermally intensify processes, reduce heat demand, harness waste heat, and use fuels and hydrocarbon feedstocks more efficiently; (3) Identify the R&D pathways to thermal process intensification (TPI) with the highest potential for impact and adoption by the industrial sector; (4) Define areas of research, development, and demonstration (RD&D) activities to accelerate development and application of emerging and transformative technologies to intensify thermal processes in industry. The scope and focus of the workshop were defined to meet these goals. Based on the available data for energy use and GHG emissions, the industries that collectively use more than 80% of the total process heating energy consumption were selected as primary focus areas. The chosen industries were combined into the following four groups based on similarities in their thermal processes: high-temperature metal processing (iron and steel industry, alumina-aluminum industry); high-temperature nonmetal and mineral processing (cement and glass industry); medium- to low-temperature thermal processing (food processing and pulp and paper industry as part of forest products sector); and hydrocarbon processing (petroleum refining and chemical industry). Furthermore, all potential TPI technologies associated with the processes defined were considered as part of the workshop. Different types of TPI technologies possible in industrial in the document.

42 ENGINEERING↗
DOE OSTI2024

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ~12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

Materials processing in space bibliography, 1983, revised

Flight experiments utilizing a low gravity environment to elucidate and control various processes, or ground based activities that provide supporting research are compiled. Six major categories: crystal growth; solidification of metals, alloys, and composites; fluids, transports, and chemical processes; glasses and ceramics; ultrahigh vacuum and containerless processing technologies; and combustion are included. A list of patents and appendices providing a compilation of anonymously authored collections and reports and a cross reference index are included.

Pentecost, E.↗