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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Potential Integration Between Residual Biogenic Process Resources and Greener Hydrogen Production from Steam Reformers

Biomass conversion processes have varying efficiencies towards specific products like liquid fuels; process inefficiencies result in byproducts such as off-gases, heat, and solid residues such as char. The efficient use of these byproducts is key towards getting the maximum sustainability benefits from valuable biomass resources. For example, there are various utility product options that can utilize heat and off-gases from biomass pyrolysis processes; they include process heat and steam, hydrogen, fuel gas, and electricity. Further, there is potential for the use of the off-gases to supplement natural gas feed into steam reformers for hydrogen production. This presentation highlights results from previous analyses on tradeoffs based on utility byproduct choices (https://doi.org/10.1039/D3SE00745F); maximizing hydrogen production from off-gases is one potential winning strategy. This leads to the question regarding the utilization of these off-gases in existing steam reformers and the process impacts from feeding off-gases. Process modeling of a steam reformer system (https://doi.org/10.1002/adsu.20230021) quantifies those impacts and shows how much off-gas substitution is possible within the limits of an existing design with such an integration strategy.

biogenic gases↗
DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Formation trajectories of solution-processed perovskite thin films from mixed solvents

The engineering of mixed-solvent formulations and their evaporation conditions are key to reproducible perovskite coatings for high-performance photovoltaics. Here, we report a lumped-parameter evaporation model to predict the evolution of a perovskite ink liquid film over time (solvent ratio, solute concentration, and film thickness). The drying-rate model is validated via in situ film-thickness measurements, and the predicted transient liquid film state is mapped as a process path. These methods allow for the prediction of process sensitivity to local environmental factors and the understanding and visualization of a broader processing parameter space enabled through the coupling of process and ink engineering. Process maps are applied to create a new framework for scalable perovskite coating development with a goal of improving the reproducibility and transferability of perovskite fabrication. This approach is demonstrated with blade-coated FA 0.83 Cs 0.17 PbI 3 photovoltaic devices, improving the photovoltaic conversion efficiency from 17.5% ± 1.7% to 20.3% ± 0.6%.

14 SOLAR ENERGY↗
DOE OSTI2026

Proton-rich Production of Lanthanides: The vi Process

The astrophysical origin of the lanthanides is an open question in nuclear astrophysics. Besides the widely studied s, i, and r processes in moderately to strongly neutron-rich environments, an intriguing alternative site for lanthanide production could in fact be robustly proton-rich matter outflows from core-collapse supernovae under specific conditions—in particular, high-entropy winds with enhanced neutrino luminosity and fast dynamical timescales. In this environment, excess protons present after charged-particle reactions have ceased can continue to be converted to neutrons by (anti)neutrino interactions, producing a neutron-capture reaction flow up to A ∼ 200. This scenario, christened the νi process in a recent paper, has previously been discussed as a possibility. Here, we examine the prospects for the νi process through the lenses of stellar abundance patterns, bolometric light curves, and galactic chemical evolution models, with a particular focus on hypernovae as candidate sites. We identify specific lanthanide signatures for which the νi process can provide a credible supplement to the r/i processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Pulsed Electric Field Processing of Apples

Pulsed electric field (PEF) processing is a nonthermal technology that has the potential to improve the efficiency of apple processing, particularly for firm cultivars that are growing in popularity. This study evaluated the effects of PEF pretreatment on the processability and product quality of Cosmic Crisp apples, a modern cultivar known for its firmness. Apples were treated with PEF and assessed for tissue softening, mechanical energy requirements, particle size reduction, browning tendencies, and juice yield. PEF treatment significantly softened the apple tissue, as demonstrated by reduced compression forces in Instron testing, and this softening translated to decreased grinding energy during applesauce production. There were no changes to the particle size distribution of the resulting applesauce. PEF treatment also altered browning behavior and produced higher juice yields following mechanical disruption, suggesting enhanced membrane permeability and mass transfer. Together, these results show that PEF pretreatment can reduce mechanical energy demands and increase extractability without reducing product quality in firm apple varieties. These outcomes highlight the potential of PEF to increase the value recovered from processing-grade apples and to support more efficient and sustainable apple processing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
DOE OSTI2025

Benchmarking thermal energy storage cost for industrial process heat

Process heat accounts for roughly half of industrial energy demand, and currently 95% of process heat is derived from the combustion of natural gas, oil, and coal. Electrification of industrial heating could be an alternative, potentially expanding locations suitable for manufacturing; however, industrial facility owners may desire energy storage to stabilize energy costs. In this work, the economic benefits of pairing thermal storage with electrified process heat to reduce the average price paid for energy are analyzed. Cost savings focus on energy arbitrage, or leveraging flexible energy pricing schemes, alone. The cost of natural gas combustion across decades (2019-2060) is compared to the costs of electricity and thermal energy storage in four United States Independent System Operator (ISO) regions. Systems installed today may not yield positive net present value (NPV) compared to the use of natural gas. However, using estimated electricity prices, systems installed in 2030 using arbitrage alone could be profitable when compared to natural gas in some regions of the U.S. Furthermore, if capital expenditures could be reduced by 50% for sensible thermal storage systems by 2030, profitable systems are found across all regions. This implies that electrification of industrial process heat, when paired with inexpensive thermal energy storage systems, could be less expensive than brownfield natural gas systems, using arbitrage as the only source of revenue and without a dependency on any future policy drivers such as pricing externalities that could further incentivize the electrification of industrial process heat.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A numerical study of process complexity in permafrost dominated regions

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Calibration↗
DOE OSTI2025

Process intensification in hydrothermal liquefaction of biomass: A review

Hydrothermal liquefaction (HTL) presents a promising pathway for converting wet biomass resources into biofuels, offering significant advantages over conventional methods. However, numerous challenges must be addressed for HTL scale-up, including energy provision for the endothermic process, heat and mass transfer limitations, slurry concentration and pumpability challenges, char and coke formation, and continuous phase separation. This review explores key strategies such as autothermal HTL, which improves process efficiency and reduces external energy requirements by coupling exothermic and endothermic reactions within the same reactor, thereby simplifying reactor design and reducing operational costs. Additionally, multistage HTL processes are highlighted for their ability to optimize biocrude quality and yield by fractionating biomass conversion stages, resulting in higher energy returns on investment and better-quality biocrude. Solvothermal HTL and integration techniques for aqueous phase are also discussed. Furthermore, the HTL patent landscape is discussed to provide insights into current technological advancements. This review aims to offer a comprehensive understanding of process intensification in HTL, highlighting innovative solutions to enhance the efficiency and scalability of the process for sustainable biofuel production.

Biocrude oil↗
DOE OSTI2026

Use of friction stir processing to synthesize nanocrystalline, grain boundary segregating Fe-Ti alloys

Grain boundary segregating alloys, a class of alloys designed such that nanocrystalline grain sizes are thermodynamically stabilized by the presence of high segregation energy solutes at the grain boundaries, are typically produced through geometrically limited processing techniques such as equal channel angular extrusion or thin film sputtering. Here, this study explores the use of friction stir processing (FSP) as a novel means of studying and producing these alloys using a test system of Fe-6at%Ti. Spot FSPs with a range of processing parameter sets were produced on bulk, coarse grained bars of material and optimal parameters were identified. Microscopy identified a range of processed materials which achieved nanocrystalline grains on the order of 100 nm, delineating a critical window of processing parameters which limit heat input while inducing sufficient plastic deformation for grain refinement. The finest grained nanocrystalline Fe-Ti achieved a hardness of 7.68 GPa, a significant increase in hardness over pure Fe with a similar microstructure due to increased dislocation density from FSP as well as several strengthening mechanisms produced through the presence and segregation of Ti. These results demonstrated the feasibility of using FSP to produce nanocrystalline grain boundary segregating alloys.

Fe alloys↗
DOE OSTI2025

Process and environmental safety of thermochemical conversion of biomass

Biomass presents a promising opportunity for converting waste into valuable energy, fuels, and chemicals through various thermochemical processes, including hydrothermal carbonization, hydrothermal liquefaction, pyrolysis, gasification, and combustion. However, these processes operate under extreme conditions, introducing significant safety hazards that necessitate meticulous management to prevent accidents. This review discusses potential hazards, risk mitigation strategies, and safety management practices, emphasizing the importance of integrated safety measures into the design and operation of these processes. It also highlights the critical need for robust safety engineering and environmental management strategies tailored to each thermo-chemical process. As these processes transition from laboratory to industrial scale, there is an imperative to develop a clear and concise pathway for emerging industries to achieve regulatory compliance, achieve safety certification, and enable effective stewardship of potential fugitive emissions. By addressing safety and environmental concerns, stakeholders can optimize economic benefits, rural development, and achieve sustained domestic energy generation benefits offered by biomass conversion technologies. Further research on plant design optimization, operational safety, environmental evaluation standardization, and innovative waste management solutions will support the safe and effective scaling of these technologies, ultimately contributing to sustainable management and resource recovery.

09 - BIOMASS FUELS↗
DOE OSTI2024

Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

14 SOLAR ENERGY↗
DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗
DOE OSTI2026

Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process‐Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process-based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data-constrained representation of the sterile triploid Miscanthus (IL clone) within the process-based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam-derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO 2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field-measured biomass. Building on the calibrated operating state, parameter-response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post-calibration GPP R 2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R 2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

ecosys↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
DOE OSTI2025

Cardinal: Seismic and Geoacoustic Array Processing

Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.

Adaptive Array↗
DOE OSTI2026

Design and Optimization of Processing Pathways for Rare Earth Element Recovery from End-of-Life Products

In this presentation, we first discuss the benefits of targeting end-of-life (EOL) products as a feedstock. We then discuss the problem statement of designing a processing facility for recovering rare earth elements (REEs) from EOL HDDs and motors from EOL EVs/HEVs. We then discuss the most profitable pathway, and then discuss the environmental impacts associated with the processing pathway, comparing it to a processing facility in China, and find it to have lower impacts overall. We also investigate the trade-off solutions for other pathways that are not as profitable, but have lower impacts by generating Pareto Fronts. In the next portion of the presentation, Critical Materials Recycling, Inc. is discussed. We discuss how their process was found to be the optimal pathway after performing superstructure optimization, and how we are currently working with them to further optimize their process to increase profits. Finally, we wrap up with conclusions summarizing the main takeaways of the presentation.

36 MATERIALS SCIENCE↗