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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 OSTI2022

Automatic parameter selection for electron ptychography via Bayesian optimization

Abstract Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and to study electron-beam sensitive materials with high dose efficiency. In practice, obtaining accurate ptychography images requires simultaneously optimizing multiple parameters that are often selected based on trial-and-error, resulting in low-throughput experiments and preventing wider adoption. Here, we develop an automatic parameter selection framework to circumvent this problem using Bayesian optimization with Gaussian processes. With minimal prior knowledge, the workflow efficiently produces ptychographic reconstructions that are superior to those processed by experienced experts. The method also facilitates better experimental designs by exploring optimized experimental parameters from simulated data.

42 ENGINEERING↗
DOE OSTI2021

Teeport: Break the Wall Between the Optimization Algorithms and Problems

Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Synergistic Improvement of Carbohydrate and Lignin Processability by Biomimicking Biomass Processing

The sustainability and economic feasibility of modern biorefinery depend on the efficient processing of both carbohydrate and lignin fractions for value-added products. By mimicking the biomass degradation process in white-rote fungi, a tailored two-step fractionation process was developed to maximize the sugar release from switchgrass biomass and to optimize the lignin processability for bioconversion. Biomimicking biomass processing using Formic Acid: Fenton: Organosolv (F 2 O) and achieved high processability for both carbohydrate and lignin. Specifically, switchgrass pretreated by the F 2 O process had 99.6% of the theoretical yield for glucose release. The fractionated lignin was also readily processable by fermentation via Rhodococcus opacus PD630 with a lipid yield of 1.16 g/L. Scanning electron microscope analysis confirmed the fragmentation of switchgrass fiber and the cell wall deconstruction by the F 2 O process. 2D-HSQC NMR further revealed the cleavage of aryl ether linkages (β-O-4) in lignin components. These results revealed the mechanisms for efficient sugar release and lignin bioconversion. The F 2 O process demonstrated effective mimicking of natural biomass utilization system and paved a new path for improving the lignin and carbohydrate processability in next generation lignocellulosic biorefinery.

09 BIOMASS FUELS↗
DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗
DOE OSTI2022

Probing elemental speciation in hydrochar produced from hydrothermal liquefaction of anaerobic digestates using quantitative X-ray diffraction

Valorization of hydrochar, a solid byproduct from hydrothermal liquefaction (HTL) of anaerobically-digested agriculture wastes (digestates), requires fundamental knowledge of elemental speciation. This study investigated the effects of reaction temperatures (320–360 °C), digestate pH (3.5–8), and digestate cellulose-to-lignin ratios (0.2–1.8) on the speciation (chemical form) and composition of organics and inorganics in hydrochars produced during hydrothermal treatment. Quantitative X-ray diffraction (XRD) method was the primary technique used to characterize hydrochars. The comprehensive XRD pattern processing including the Rietveld refinement protocols demonstrated that the organic phase was comprised of mostly crystalline monocyclic, heterocyclic, and polycyclic aromatics with diverse aliphatic and aromatic substituents, while the inorganic mineral phase consisted of calcium-phosphates, magnesium-phosphates, calcium-carbonates, and magnesium-carbonates. Further, XRD results were validated by the elemental yields of products and the distribution of chemical functionalities measured using solid-state nuclear magnetic resonance (NMR) spectroscopy. The characterization data were used to evaluate proposed mechanistic pathways using compositional analysis of biocrude and aqueous-phase coproducts. Mechanistic pathways developed in the study suggested that benzoic acids, phenols, benzaldehydes, phenolic aldehydes, α-dicarbonyls, and α-hydroxycarbonyls were responsible for the precipitation of organics through various reactions depending on operating conditions. Meanwhile, the formation of inorganic compounds appeared to be consistently represented by reactions including dehydration, hydrolysis, endergonic reduction, and structure rearrangement of native minerals in the digestates. This study provides basic knowledge needed to create and assess potential elemental speciation pathways. In addition, the results of the study facilitate the specification of process conditions to optimize targeted utilization routes of hydrochar for more economically-feasible and sustainable HTL processing.

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

Optimal control to handle variations in moisture content and reactor in-feed rate

The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%-2.24%. Furthermore, a biorefinery can adapt HPC to achieve its design capacity.

09 BIOMASS FUELS↗
DOE OSTI2023

Atomically Local Electric Field Induced Interface Water Reorientation for Alkaline Hydrogen Evolution Reaction

The slow water dissociation process in alkaline electrolyte severely limits the kinetics of HER. The orientation of H 2 O is well known to affect the dissociation process, but H 2 O orientation is hard to control because of its random distribution. Herein, an atomically asymmetric local electric field was designed by IrRu dizygotic single-atom sites (IrRu DSACs) to tune the H 2 O adsorption configuration and orientation, thus optimizing its dissociation process. The electric field intensity of IrRu DSACs is over 4.00×10 10 N/C. The ab initio molecular dynamics simulations combined with in situ Raman spectroscopy analysis on the adsorption behavior of H 2 O show that the M–H bond length (M=active site) is shortened at the interface due to the strong local electric field gradient and the optimized water orientation promotes the dissociation process of interfacial water. This work provides a new way to explore the role of single atomic sites in alkaline hydrogen evolution reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Mono-Ether and Alcohol Bioblendstocks to Reduce the Fuel Penalty of Mixing Controlled Compression Ignition (MCCI) Engine Aftertreatment

An integrated approach utilizing catalysis experiments, process systems engineering, fuel property modeling, and engine testing was utilized in this project to optimize the production process and composition of a #2 diesel bioblendstock produced from ethanol consisting primarily of long-chain mono-ethers. The primary objective of the project was to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. The desired bioblendstock needed to be blendable with #2 diesel fuel at > 5 vol. % while still meeting ASTM D975 diesel fuel specification properties and achieving improvements in fuel properties: increased cetane number, decreased sooting, and reduced pour point and cloud point temperatures. At the same time, it needed to reduce the fuel energy penalty associated with MCCI engine aftertreatment resulting in improved system efficiency. The findings of the current project demonstrate that primary objective of the work has been met, i.e., to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. Additionally, the results indicate that the property objectives (increased cetane number, reduced pour and cloud points, and reduced sooting propensity) for the designed bioblendstock composition have also been met. Engine testing performed has also confirmed that the increased reactivity of the bioblendstock can be used to improve catalyst heating operation and to reduce the fuel penalty associated with this operation mode. The fuel property results demonstrate that >5 vol.% blending is easily achieved while meeting the ASTM D975 #2 diesel fuel property specifications tested in this work, as this was achieved for a blend with 43 vol. % of the bioblendstock. The results provide a foundation for future work to scaleup the catalytic production process designed in this work, with many of the challenges and areas for improvement being identified in this work to enable economic production with low GHG lifecycle emissions.

09 BIOMASS FUELS↗
DOE OSTI2023

In situ embedment of type K sheathed thermocouples with directed energy deposition

Advanced nuclear reactor systems require new technologies for heat transfer and system monitoring. Additive manufacturing (AM) offers the design flexibility to allow in-situ sensor embedment through smart manufacturing for real-time monitoring, and performance of these systems. Here, this study focuses on experiments investigating the feasibility of in-situ sensor embedment using directed energy deposition (DED). Type K thermocouples are embedded into 316L stainless steel (SS) samples using two different configurations (e.g., exposed and embedded tips) and two designs (e.g., flush to substrate) within an AM base. Embedded sensor samples are analyzed via in-situ measurements and high-temperature performance validation tests at 350ºC and 900ºC. Temperature performance results at both temperature tests show good agreement with manufacturer specifications proving that these sensors could still capture accurate temperature readings after in-situ embedment during DED processing. An additional optimization experiment was conducted on the exposed tip configuration using a surrogate thermocouple to improve tolerances and the embedment process. Results improved tolerances, lower porosity, smaller gaps between the sensor and base, and better junction contact for the sensor. Although further optimization of this embedment strategy is necessary to improve the structural stability and tolerances within the component, this research strategy provides a proof-of-feasibility for DED embedment with commercial sheathed thermocouples. This research provides early impact on embedment of sensor for multiple materials and complex geometric components..

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design

Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multipurpose 10 yr optical survey of the Southern Hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the solar system, exploring the transient optical sky, and mapping the Milky Way. The survey’s massive data throughput will be transformational for many other astrophysics domains and Rubin’s data access policy sets the stage for a huge community of potential users. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

Laser-bed powder fusion (LBPF) additive manufacturing is increasingly being used to produce components of complex geometries using the Ni-base superalloy Inconel 718. The composition and the microstructure of the alloy are currently well optimized for wrought components made using conventional manufacturing processes such as rolling, forging, extrusion, etc. The attractive mechanical properties of the alloy result from the underlying austenitic matrix with fine equiaxed grains, and a high density and uniform distribution of the precipitation hardening phase, γ". Heat treatment steps such as homogenization, solutioning and aging are well documented for the wrought alloy. However, when the same wrought alloy compositions are used for the additive manufacturing (AM) processes, the asprocessed microstructure is significantly different, because of the different thermal history associated with LBPF, including rapid solidification and multiple temperature excursions that lead to multiple re-melting and reheating in the solid state. Rapid solidification introduces potential non-equilibrium effects at the moving solid-liquid interfaces that impact the extent of solute segregation, as well as the morphology of the dendritic grains that form. In order to recover the target mechanical properties, AM components have to undergo post-process heat treatments. However, such heat treatments have to be custom designed for the AM process and the component geometry because of the expected vast differences in the microstructure at various locations of a component with complex geometry. The homogenization and precipitation steps should be optimized for the component so that target mechanical properties can be obtained throughout the part. The objective of this research is to utilize High Performance Computing in phase field simulations of microstructure evolution during post-processing of AM components. The physics-based modeling will be beneficial in reducing the experimental effort required for heat treatment process selection, optimization, and certification, thus leading to a significant reduction in energy consumption for AM and post-processing heat treatment. The optimization study will help identify heat treatments steps that are critical for development of a final desired microstructure with the minimum energy input. This combined with shortening of the production cycle (time-to-market) by reducing the number of failed parts (property targets), and reduction in the number of iterations for process optimization, will enable 30-40% savings in the energy costs. Phase field simulations of the degree of homogenization and the effect of local matrix composition on the nucleation and growth of competing precipitating phases were performed using the Microstructure Evolution Using Massively Parallel Phase Field Simulations code developed in-house at the Oak Ridge National Laboratory. The simulations were able to successfully capture the kinetics of nucleation and growth, and morphologies of various precipitating phases as a function of local matrix compositions and composition gradients characteristic of local microstructures arising from location-dependent variations in the thermal conditions. Future work will involve extending the simulations to a length scale consisting of multiple dendrites, so that the effect of homogenization on the coarsening of the dendrites can be simulated and used as an additional input to the optimization of the heat treatment process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling powder spreadability in powder-based processes using the discrete element method

Powder-bed fusion (PBF) processes refer to a subset of Additive Manufacturing (AM) techniques where powder is spread on the build-plate before melting (by a laser or electron beam). While PBF processes are attractive due to their ability for realizing complex structures that are either difficult or impossible to create through conventional means, the parts fabricated with these techniques can exhibit defects such as pores, inclusions, and excessive surface roughness. To minimize these defects, much research has been dedicated towards process maturation by optimizing laser or electron beam parameters. However, these developmental efforts typically do not address the recoating process where achieving dense and uniform layers of powder is a necessity for ensuring process repeatability and part quality. While the recoating process can be studied through experimentation, the dynamics of particle movement are difficult to analyze experimentally. Therefore, here, in this study, powder spreading in PBF was simulated through the Discrete Element Method (DEM) to elucidate the mechanisms that control powder-bed quality. Utilizing the Buckingham Pi theorem, a dimensionless metric referred to as the spreading index is developed that combines powder-bed density, roughness, and particle size to assess the quality of powder layers. The formulated spreading index is then related to several dimensionless quantities that provide insight into the mechanisms dominating powder spreading in PBF. The DEM simulations conducted in this work focused on the scenario where powder is spread onto an existing powder bed and revealed that a reduction in the recoating velocity causes an increase in the spreading index while little to no impact on the spreading index was observed when varying layer thickness from 30 μm to 75 μm.Particle size effects on the powder-bed quality were also investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. 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 variable limit 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 with 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 with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

The Critical Influence of Spin–Dry Technique on the Surface Passivation Quality of Crystalline Silicon Solar Cell Structures

This study examines the effects of spin-dry (SD) and N 2 blow-dry (ND) techniques on the quality and surface passivation performance of silicon oxide grown in ozone-dissolved deionized water. The SD method achieves greater oxide thickness uniformity, averaging 1.39 nm ± 4.17% across 49 points, compared to 1.68 nm ± 21.67% for the ND wafers. However, persistently poor passivation of ozonated oxide-grown wafers following the SD process is exhibited, with carrier lifetime, τ eff < 0.3 ms and saturation current density, J 0 (per side) ranging from 26 to 45 fA cm 2 . These findings are analyzed in the context of the fundamental phenomena involved in the drying processes of both techniques. Following this, an optimized spin-drying process is developed, resulting in improved τ eff and J 0 values of 1.4 ms and 5.6 fA cm –2 , respectively. Scanning electron microscopy further confirms that the oxide films dried with the enhanced SD technique are free of pinholes.

14 SOLAR ENERGY↗
DOE OSTI2024

Integrated Technology for Cost-Effective CO2 Capture and Formic Acid Production: Modeling, Optimization, and Economic Analysis

A novel reactive technology is being investigated that electrochemically converts CO2 into valuable chemicals, particularly formic acid. This work focuses on identifying the optimal design and operation of an integrated membrane-based CO2 capture unit with the electrochemical conversion process. In this setup, the CO2 in the flue gas permeates through a CO2-selective membrane and enters an electrolyzer to produce formic acid, creating an integrated reaction module. To refine the chemical product, gas products from the electrolyzer are directed to a pressure swing adsorption unit, while the liquid product undergoes refinement to achieve commercial-grade formic acid using reactive distillation. A membrane CO2 capture model and an electrochemical conversion model have been developed using the IDAES Integrated Platform (Institute for the Design of Advanced Energy System), facilitating rigorous flowsheet modeling and process design and optimization.

Wang, Maojian↗
DOE OSTI2024

Department of Energy/IDEA: More best-practices research

This article continues the overview of the U.S. Department of Energy and IDEA collaboration by highlighting additional case studies that demonstrate innovative engineering approaches in district energy systems. It focuses on how campuses are improving efficiency, integrating advanced technologies, and reducing energy use and emissions through data-driven strategies. The University of Cincinnati is featured for its highly efficient chilled water system, using advanced algorithms and upgraded “smart chillers” to optimize performance, reduce energy consumption, and lower costs. Wake Forest University emphasizes a data-informed modernization process, using system optimization and targeted building improvements to significantly boost efficiency and reduce overall energy use. Arizona Western College showcases creative solutions to extreme cooling demands, including system redesign, smart controls, and thermal storage to improve reliability and cut peak energy costs. Overall, the article demonstrates that combining innovative engineering, real-time data analysis, and system optimization can significantly enhance performance, reduce emissions, and deliver cost savings in district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗