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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

RapidClave® Technology Demonstrations - I

This demonstration sought to show a dramatic reduction in tool cost for RapidClave® tooling, taking advantage of recent modifications made on the RapidClave® system. Instead of building tools with complex internal heating, simple aluminum tools would be produced and heated externally through the recently added hot plate and air blower inside the RapidClave® system. Baselining against this tooling is the legacy RapidClave® tooling, as well as compression tooling, which would be the incumbent processing technique that the RapidClave® is displacing. Both an automotive and aerospace application were used to evaluate the tooling and process. Specifically, a four-piece Volkswagen hood structure was selected as an appropriate automotive geometry, while a wing structure representative of a current wing entering limited production was provided by Cornerstone Research Group for evaluating aerospace applications. In total, six tools were fabricated, and costs were compared against compression tooling quotes of the same tool geometry. A cost savings of over 80% was shown on all the tools. This demonstration also produced parts in the RapidClave® using the abovementioned tools to validate the low-cost tooling and the machine modifications. Solvay recommended two prepreg systems, CYCOM® EP2750 and SolvaLite™ 712, as appropriate for aerospace and automotive applications, respectively. Flat panels of each material were fabricated to verify cure parameters before proceeding with actual part fabrication. On-tool cycle times of approximately 30 minutes was achieved, with modifications identified to lower the time further.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

We report home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A fast and accurate domain decomposition nonlinear manifold reduced order model

Here, this paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM ROMs approximate the full order model (FOM) state in a nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for problems with slowly decaying Kolmogorov n-width. However, the number of NM-ROM parameters that need to be trained scales with the size of the FOM. Moreover, for “extreme-scale” problems, the storage of high-dimensional FOM snapshots alone can make ROM training expensive. To alleviate the training cost, this paper applies DD to the FOM, computes NM-ROMs on each subdomain, and couples them to obtain a global NM-ROM. This approach has several advantages: Subdomain NM-ROMs can be trained in parallel, involve fewer parameters to be trained than global NM-ROMs, require smaller subdomain FOM dimensional training data, and can be tailored to subdomain specific features of the FOM. The shallow, sparse architecture of the autoencoder used in each subdomain NM-ROM allows application of hyper-reduction (HR), reducing the complexity caused by nonlinearity and yielding computational speedup of the NM-ROM. This paper provides the first application of NM-ROM (with HR) to a DD problem. In particular, this paper details an algebraic DD reformulation of the FOM, training a NM-ROM with HR for each sub domain, and a sequential quadratic programming (SQP) solver to evaluate the coupled global NM-ROM. Theoretical convergence results for the SQP method and a priori and a posteriori error estimates for the DD NM-ROM with HR are provided. The proposed DD NM-ROM with HR approach is numerically compared to a DD LS-ROM with HR on the 2D steady-state Burgers’ equation, showing an order of magnitude improvement in accuracy of the proposed DD NM-ROM over the DD LS-ROM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Chemical-Free Lithium Separation from High-Salinity Brines Using Model-Informed and Machine Learning-Optimized Multi-Column Zwitterionic Chromatography

Direct Lithium Extraction (DLE) technologies often struggle to produce high-purity lithium salts from high-salinity brines, as current approaches require chemical-based elution, regeneration, and precipitation steps, resulting in significant environmental footprints. A novel salt fractionation approach using carboxybetaine resin, known as zwitterionic chromatography (ZIC), has demonstrated that lithium ions can be separated from divalent cations under high-salinity conditions using only water as eluent, with no regeneration required. To enable continuous and scalable deployment of this approach, we developed a chemical-free Multi-column Zwitterionic Chromatography (MZC) process and its theoretical and process models. To predict and optimize this nontraditional separation system, we introduced a novel anti-Langmuir isotherm, and the isotherm parameters were estimated through a machine learning-driven optimization based on artificial neural network ensembles with numerical feasibility assessment. Using machine learning-driven optimization, the MZC process achieved 98.0% lithium recovery, 99.5 % Li/(Li + Mg + Ca) purity, a 31.3% productivity increase, and a 33% reduction in water use compared to batch operation. The proposed MZC process enables lithium separation at $0.6-1.2 kg-1 Li, with costs dominated by resin manufacturing, while offering lower separation costs and carbon footprint compared with conventional carbonation. Overall, these findings position the MZC process as an effective polishing step within scalable and sustainable lithium production pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Interface and Thermophysical Properties of R 32 Refrigerant

Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH 2 F 2 ) has emerged as a promising candidate owing to its favorable thermophysical properties, including high heat transfer efficiency and low viscosity. While bulk properties such as density, viscosity, and thermal conductivity have been widely studied, interfacial properties, including surface tension and interfacial thickness, remain comparatively underexplored, despite their importance in phase-transition dynamics. Here, we perform molecular dynamics (MD) simulations from 180 to 300 K using an optimized transferable force field for fluoropropenes with enhanced electrostatics to assess both bulk and interfacial behavior of R32. Simulations reproduced density within ±2.1%, viscosity within 3.05%, and thermal conductivity within 7.41% of NIST reference data. Heat capacities (C p and C v ) were predicted within 5%. For interfacial properties, surface tension trends were reproduced within 13.58% deviation, and the vapor–liquid coexistence curve closely matched reference data, yielding a critical temperature of 345.7 K (1.6% deviation) and a critical density of 0.397 g/cm 3 (6.4% deviation). Importantly, the vapor–liquid interface exhibited pronounced temperature-dependent broadening across the 180–290 K range. This behavior correlates with increasing molecular kinetic energy, reduction in intermolecular cohesive interactions, and a progressive loss of preferential dipole alignment, which collectively enhance thermal fluctuation amplitudes at elevated temperatures. These validated results provide predictive molecular-level insights, particularly for interfacial properties that remain less characterized. By reducing property prediction errors in key parameters such as critical temperature, this work provides reliable inputs for heat-exchanger and system models. Such correlations can support optimized component sizing, improved performance, and reduced refrigerant charge. Beyond R32, the methodology offers a transferable framework for blended and next-generation low-GWP refrigerants, contributing to sustainable thermal management aligned with the 2027 EU F-Gas regulation and 2030 Kigali Amendment.

Fluids↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗
DOE OSTI2022

Mechanical Characterization of Loblolly Pine

Biorefineries are faced with various material flow challenges during feeding and handling, due to inherent variability and inhomogeneity of biomass feedstocks. This impacts the efficiency of refinery processes and unit operations. Proper characterization of bulk properties allows for optimal process design and reduction of overall costs. This work is aimed at evaluating the physical and mechanical properties that impact bulk flow of loblolly pine. Cyclic axial compression tests are performed to elucidate consolidation behavior under different stress conditions. The results are then used to develop correlations between material parameters (particle size, tissue type, moisture content) and Elastic Modulus of bulk samples.

09 BIOMASS FUELS↗
DOE OSTI2022

Model-Free Building Temperature Control and Power Allocation Under Measurement Time Delays

Taking a step towards a greener planet has created an increased need for a higher integration of renewable energy resources into the electric grid. Nonetheless, the intermittency and uncertainty associated with renewable generation have slowed down this integration. Demand response (DR) has been recently adopted to address this challenge by utilizing demand side flexibility and enabling the participation of many grid-interactive efficient buildings (GEBs). However, existing DR methods require significant modeling and/or training efforts and are computationally expensive. To address the aforementioned issues, we propose a model-free control (MFC)-based strategy that is robust to the time delays in the temperature measurements of the thermostatically controlled loads (TCLs). It assigns to each GEB a local controller to maintain the TCLs’ temperatures within desired comfort levels, while the load aggregator (LA) allocates the assigned reference power provided by the distribution system operator (DSO) to support a specific grid service, such as demand peak reduction, load shifting, balancing supply and demand, and consuming the solar photovoltaic power locally. We investigate the effects of such loss of information on the local control action as well as on meeting the power allocation constraint. We conclude that, for an appropriate choice of design parameters, the proposed MFC controller is satisfactorily robust to measurement time delays.

Telsang, Bhagyashri↗
DOE OSTI2021

Genome Analysis of a Verrucomicrobial Endosymbiont With a Tiny Genome Discovered in an Antarctic Lake

Organic Lake in Antarctica is a marine-derived, cold (–13°C), stratified (oxic-anoxic), hypersaline (>200 gl –1 ) system with unusual chemistry (very high levels of dimethylsulfide) that supports the growth of phylogenetically and metabolically diverse microorganisms. Symbionts are not well characterized in Antarctica. However, unicellular eukaryotes are often present in Antarctic lakes and theoretically could harbor endosymbionts. Here, we describe Candidatus Organicella extenuata, a member of the Verrucomicrobia with a highly reduced genome, recovered as a metagenome-assembled genome with genetic code 4 (UGA-to-Trp recoding) from Organic Lake. It is closely related to Candidatus Pinguicocccus supinus (163,218 bp, 205 genes), a newly described cytoplasmic endosymbiont of the freshwater ciliate Euplotes vanleeuwenhoeki (Serra et al., 2020). At 158,228 bp (encoding 194 genes), the genome of Ca. Organicella extenuata is among the smallest known bacterial genomes and similar to the genome of Ca. Pinguicoccus supinus (163,218 bp, 205 genes). Ca. Organicella extenuata retains a capacity for replication, transcription, translation, and protein-folding while lacking any capacity for the biosynthesis of amino acids or vitamins. Notably, the endosymbiont retains a capacity for fatty acid synthesis (type II) and iron–sulfur (Fe-S) cluster assembly. Metagenomic analysis of 150 new metagenomes from Organic Lake and more than 70 other Antarctic aquatic locations revealed a strong correlation in abundance between Ca. Organicella extenuata and a novel ciliate of the genus Euplotes. Like Ca. Pinguicoccus supinus, we infer that Ca. Organicella extenuata is an endosymbiont of Euplotes and hypothesize that both Ca. Organicella extenuata and Ca. Pinguicocccus supinus provide fatty acids and Fe-S clusters to their Euplotes host as the foundation of a mutualistic symbiosis. The discovery of Ca. Organicella extenuata as possessing genetic code 4 illustrates that in addition to identifying endosymbionts by sequencing known symbiotic communities and searching metagenome data using reference endosymbiont genomes, the potential exists to identify novel endosymbionts by searching for unusual coding parameters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Thermodynamics and kinetics of solution combustion synthesis: Ni(NO 3 ) 2 + fuels systems

Solution combustion synthesis (SCS) utilizes exothermic self-propagating reactions to prepare nanoscale materials that can be used widely in energy, electronics, and biomedical technologies and other applications. SCS is a specific variety of a more general combustion synthesis (CS) method. Investigations of the thermodynamics, kinetics, and the mechanisms of SCS reactions, are not as well studied as the other CS processes. This work reports on a systematic study of the thermodynamics and kinetics of SCS reactions involving Ni(NO 3 ) 2 , an oxidizer, and either glycine (C 2 H 5 NO 2 ) or hexamethylenetetramine (HMT, C 6 H 12 N 4 ) as fuels. A thermodynamic modeling approach, based on the Gibbs free energy minimization principle, is applied to the simultaneous calculations of the adiabatic temperatures and compositions of the equilibrium products. Our calculations reveal the influence of fuel-to-oxidizer ratio, amount of water, and the oxygen in air on the combustion temperature under adiabatic conditions and the composition of the resulting products. We have, in turn, measured the combustion temperature and phase composition of products and compared them with the calculations. Variations of drying times for the solutions yield precursor gels with varying water contents. This approach enables the manipulation of combustion parameters and confirms the use of calculated activation energies for reactions using the Merzhanov-Khaikin method. Here, the results show that SCS reactions in fuel-lean solutions producing NiO have higher activation energy in contrast to reactions with fuel-rich solutions that form Ni. Reduction of activation energies due to the increase in the fuel-to-oxidizer ratio could be related to the observed change of the rate-limiting stages of the endothermic decomposition of the individual reactants to the exothermic decomposition of coordinate compounds formed between the reactants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Integrating fiber optic sensors into metallic components for sensing in harsh environments

The integration of fiber optic sensors into high-temperature materials is critical for real-time monitoring and autonomous operation of engineering systems. This study demonstrated a spark plasma sintering (SPS)-assisted embedding process for integrating sapphire fiber optic sensors into stainless steel components during part fabrication. Optical fibers were encapsulated in stainless steel 316L powders which were sintered at different fabrication conditions using SPS to investigate the effects of sintering parameters on the embedment. Measurements of optical transmittance, combined with microstructural analysis (X-ray computed tomography and scanning electron microscopy) and mechanical testing (tensile and microhardness), were conducted to examine the fiber functionality, fiber–matrix bonding quality, and properties of the sintered materials. Here, the results show that under suitable fabrication conditions, intact optical fibers can be encapsulated in highly-densified (>98 % relative density) stainless steel components. These conditions also led to a good bond at the fiber–matrix interface with micron-sized material interdiffusion across the interface. The sintering parameters were observed to affect fiber optical attenuation, where high temperature, pressure, and hold time during SPS enhanced fiber–matrix bonding and adversely affected optical transmission. Tensile testing confirmed the superior tensile strength and ductility of the matrix fabricated by SPS. Furthermore, the materials exhibited limited strength reduction (~70 MPa) upon the integration of fibers. This study demonstrates the effectiveness of SPS for fiber-material integration for high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Active Site Engineering via Optimizing the Heterogeneous Support Structure for Single-Atom Catalysis

Supported single-atom catalysts show a large range of activities and selectivities that depend on the local environment of the catalytic sites. Here a theory-based optimization strategy is presented that is based on a density functional theory determination of the transition states and intermediates for a low-dimensional coordinate representation of the heterogeneity of the active sites. The approach is applied to a vanadium catalyst on an amorphous SiO 2 support that involves a large kinetic network described using a full chemistry model. Without assuming a priori scaling relations or mechanism reduction, the optimal state of heterogeneity is found to lie at atomic configurations where the activation energies for two distinct key chemical processes are equal. It is found a posteriori that the behavior of the system is consistent with linear free energy scaling relations in the randomness parameters. The energetic span theory proves quite useful in reducing the full chemistry model to a small number of key reactions. The use of a nonlinear optimization algorithm in combination with energetic span theory provides significant simplification in treating disordered systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

All-Atom Molecular Dynamics Simulations of the Temperature Response of Densely Grafted Polyelectrolyte Brushes

Water acts a “good” solvent for most polyelectrolyte (PE) systems due to its high dielectric strength. Theoretical and experimental studies have highlighted some remarkably unconventional properties of strongly water-swollen PE brushes such as a reduction in brush height with increase in temperature. However, an understanding of the atomistic-scale response of the grafted PE chains as well as the local arrangement and organization of the brush-supported counterions and water molecules to changes in temperature is missing. Here, we conduct an all-atom molecular dynamics (MD) study to probe the influence of temperature on the “microstructure” of densely grafted, fully ionized polyacrylic acid (PAA) brushes. The atomistic insights obtained from our study shed light on prior experimental observations and elucidate the key role played by changes in hydrogen bonding network in dictating the hydrophilicity of the brushes. In addition to the temperature-mediated reduction in height of the PE brush layer, we study the influence of temperature on several properties of the brush-supported water molecules such as the number distribution and kinetics of water-water and water-PE hydrogen bonds (HBs), mass density, orientational tetrahedral order parameter, and the activation energy associated with the self-diffusion of water. The effect of temperature on the properties of the brush-trapped counterions is also quantified via changes in their solvation structure, counterion-water radial distribution function (RDF), and the translational mobility. Our findings unravel several hitherto unknown phenomena such as the enhanced propensity for attaining PE brush-induced “water-in-salt”-like scenarios at elevated temperatures, and an increase in the activation energy for the self-diffusion of water with increase in the degree of PE brush-induced nanoconfinement (from ‘bulk water’ to ‘sparsely grafted PE brushes’ to ‘densely grafted PE brushes’). Further, we anticipate that this work will be helpful in improving our predictive capabilities of the temperature response of water-swollen PE brushes, which would be instrumental for better design of several PE brush systems used in a multitude of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Controlled plasma–droplet interactions: a quantitative study of OH transfer in plasma–liquid interaction

Plasmas in contact with liquids are a rich source of OH radicals and have been extensively studied in the last decade to leverage the ability to generate chemically reactive species in gas phase plasmas to decompose organics. Multiphase transfer of OH radicals is highly transport limited and to overcome transport limits, the plasma activation of aerosols, small liquid droplets, interspersed in the plasma has been proposed. In this work, we report a combined experimental and modeling study of a controlled plasma–droplet interaction experiment using a diffuse RF glow discharge in He + 0.2% H 2 O with detailed plasma diagnostics, ex situ analysis of the plasma-induced chemistry in the droplet containing formate, droplet trajectory and size measurements. This enables a quantitative study of the reactivity transfer of OH from the gas phase plasma to the liquid phase and how its diffusion limitations impact formate decomposition in the water droplet. For a droplet with a diameter of 36 μm, we observed 50% reduction in formate concentration in the droplets after plasma treatment for droplet residence times in the plasma of ~10 ms. These short droplet residence times in the plasma allow in some cases for droplet size reductions of ~5% in spite gas temperatures of 360 K. A one-dimensional reaction–diffusion model was used to calculate the OH transport and formate oxidation inside the droplet and was able to predict the conversion of formate by plasma in a droplet without any fitting parameters. The model further shows that formate conversion is dominated by near-interfacial reactions with OH radicals and is limited by diffusion of formate in the droplet. Here, the results show that a controlled plasma–micro-droplet reactor as reported in this study might be an excellent tool for detailed quantitative plasma–liquid interaction studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Dry-processed electrodes enabled by polytetrafluoroethylene fibrillation for high-performance lithium-ion batteries

The dry processing technique of polytetrafluoroethylene (PTFE) fibrillation offers significant advancements in cost reduction, environmental impact, and electrochemical performance. Dry processing alone allows for ∼ 20–60% cost reduction and increases of up to 40 mg cm⁻2 of areal loading – it typically comes at a reduction of rate capability. By leveraging the network structure of fibers, this method enhances rate capability and cycling performance, providing a compelling alternative to the conventional and currently predominant wet processing. The review delves into PTFE fibrillation mechanisms and examines critical influencing factors including material properties and processing parameters. It also discusses challenges associated with the electrodes fabricated by PTFE fibrillation, including structural instability due to insufficient PTFE fibrillization, compromised electrical conductivity from an insufficient conductive network, inhomogeneous dispersion resulting from the absence of solvents, restricted ionic transport due to increased electrode thickness, inadequate adhesion to current collector because of low surface energy of PTFE, electrochemical degradation due to low lowest unoccupied molecular orbital (LUMO) level of PTFE, particle damage during processing and environmental concerns related PTFE being a perfluoroalkyl and polyfluoroalkyl substances (PFAS). Recent research innovations aimed at mitigating these issues, their application in beyond-lithium batteries, and future research directions are thoroughly discussed.

Park, Hyunji↗
DOE OSTI2024

Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing

The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Online MCMC Thinning with Kernelized Stein Discrepancy

A fundamental challenge in Bayesian inference is efficient representation of a target distribution. Many nonparametric approaches do so by sampling a large number of points using variants of Markov chain Monte Carlo (MCMC). Here, we propose an MCMC variant that retains only those posterior samples which exceed a kernelized Stein discrepancy (KSD) threshold, which we call KSD thinning. We establish the convergence and complexity trade-offs for several settings of KSD thinning as a function of the KSD threshold parameter, sample size, and other problem parameters. We provide experimental comparisons against other online nonparametric Bayesian methods that generate low-complexity posterior representations. We observe superior consistency/complexity trade-offs across a range of settings including MCMC sampling on two Bayesian inference problems from the biological sciences, and 10 × inference speedup and storage reduction for Bayesian neural networks with no loss of accuracy and no increase in training time. Our code is available at https://github.com/colehawkins/KSD-Thinning.

Bayesian inference↗