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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cell-Free Screening, Production and Animal Testing of a STI-Related Chlamydial Major Outer Membrane Protein Supported in Nanolipoproteins

Background: Vaccine development against Chlamydia, a prevalent sexually transmied infection (STI), is imperative due to its global public health impact. However, significant challenges arise in the production of effective subunit vaccines based on recombinant protein antigens, particularly with membrane proteins like the Major Outer Membrane Protein (MOMP). Methods: Cellfree protein synthesis (CFPS) technology is an aractive approach to address these challenges as a method of high-throughput membrane protein and protein complex production coupled with nanolipoprotein particles (NLPs). NLPs provide a supporting scaffold while allowing easy adjuvant addition during formulation. Over the last decade, we have been working toward the production and characterization of MOMP-NLP complexes for vaccine testing. Results: The work presented here highlights the expression and biophysical analyses, including transmission electron microscopy (TEM) and dynamic light scaering (DLS), which confirm the formation and functionality of MOMP-NLP complexes for use in animal studies. Moreover, immunization studies in preclinical models compare the past and present protective efficacy of MOMP-NLP formulations, particularly when co-adjuvanted with CpG and FSL1. Conclusion: Ex vivo assessments further highlight the immunomodulatory effects of MOMP-NLP vaccinations, emphasizing their potential to elicitrobust immune responses. However, further research is warranted to optimize vaccine formulations further, validate efficacy against Chlamydia trachomatis, and beer understand the underlying mechanisms of immune response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Minimized aging of isocyanurate-based rigid cellular foams for buildings through tailored barrier facers and optimized formulation

The thermal resistivity (h·ft 2 ·°F/Btu/in.) of closed-cell rigid foam insulation materials significantly decreases over time. Diffusion-tight facers are designed to significantly enhance initial thermal resistivity and long-term thermal performance by preventing gas diffusion into the foam cells and inhibiting the escape of low thermal conductivity blowing agents. In addition to the gas diffusion property of the facer film, adhesion between the foam and the facer is crucial for achieving diffusion-tight bonding. Here, this study addresses the challenge of thermal aging by investigating how facer film properties and foam formulation influence the durability of thermal performance. A systematic evaluation was conducted to understand the effects of polymeric barrier films, surface treatments, facer coverage, and foam matrix rigidity on thermal resistivity over time. Key findings reveal that diffusion-tight facers, particularly those with metallized layers and compatible heat seal layers, significantly reduce gas exchange and improve foam-facer adhesion. The optimized system, incorporating barrier facers and a tailored polyisocyanurate foam formulation, achieved initial and aged thermal resistivity values after 200 days of approximately 8.3 and 7.4 h ft 2 ·°F/Btu/in., respectively representing only a 10 % reduction compared to a 17 % reduction observed in control samples without facers. Notably, polyurethane spray foams with facers exhibited only a 4 % reduction in thermal resistivity, compared to a 23 % decrease in control samples, demonstrating nearly six times better retention of thermal performance. This innovative facer technology presents a promising solution for reducing energy costs and represents a significant advancement in optimizing energy management for building envelopes in future technologies. This technology can also be adapted for other applications that necessitate the preservation of long-term thermal performance.

Wanasinghe, Shiwanka Vidarshi [Oak Ridge National ↗
DOE OSTI2022

A better understanding of the mechanics of borehole breakout utilizing a finite strain gradient-enhanced micropolar continuum model

Borehole breakout denotes the failure in rock mass subjected to drilling, caused by stress concentrations exceeding the material strength. Depending on the material properties, the preexisting in situ stress state, and the borehole dimensions, different types of borehole breakout, such as spiral-shaped breakout or v-shaped breakout, are distinguished in the literature. In the present work, we address the influence of the material properties on the predicted borehole breakout mode in a comprehensive finite element study. To this end, herein we employ a gradient-enhanced micropolar damage-plasticity model based on the Mohr–Coulomb strength criterion, formulated in the finite strain regime, which is calibrated based on experimental results from plane strain compression tests on Red Wildmoor sandstone. In the numerical study, the influence of the in situ stress state, the material friction angle, plastic dilation, post peak residual strength, and the inherent material length scale parameters are investigated. Thereby, we demonstrate that depending on the material parameters, substantially different failure modes, characterized by strongly localized shear bands or diffuse failure zones, are predicted. It is shown that in particular the brittleness of the material in the post peak regime has a major influence on the predicted breakout type. Moreover, a statistical validation of the results is obtained by considering different random field distributions of the initial material strength.

58 GEOSCIENCES↗
DOE OSTI2024

Microstructure effects on high velocity microparticle impacts of copper

Constitutive models can fail to predict high-rate deformation behavior due to their inability to account for microstructural effects. In part, this is because of a dearth of experimental benchmarking data in the high strain-rate, low pressure regime, since many high-rate experiments also probe a region of strong shockwaves, at which point microstructure effects no longer play a primary role. This work uses laser-induced particle impact testing to quantitatively study high velocity impacts of small, rigid alumina microspheres on flat copper substrates with varying amounts of initial cold work in the weak shock regime, but at very high strain rates up to ~10 7 s –1 . Through paired experiments and numerical simulations, this work shows that the initial microstructure condition can have significant influence on dynamical mechanical properties in this range. Specifically, prior work hardening of the copper substrate leads to increased rebounding of the microparticles (i.e., less plastic dissipation in the impact) as well as smaller craters. Each of these experimental measurables can be converted into a strength measure, i.e., the dynamic yield strength or dynamic hardness, respectively, neither of which is well predicted consistently by existing constitutive laws. The general trend of hardening can be captured by such models by incorporating an existing “pre-strain,” suggesting that future calibration of the materials parameters may yield a good fit over a broader range of conditions. Our results emphasize the importance of reporting the microstructural condition in dynamic studies, as well as the necessity of accounting for these factors when formulating and optimizing constitutive models.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗
DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗
DOE OSTI2026

Optimization of well design and CO 2 injection strategy for risk reduction in Class VI geological carbon sequestration wells

The safety and durability of Class VI wells are critical for geological carbon sequestration (GCS). However, current GCS operations face unique challenges: unlike traditional Class II wells, Class VI CO 2 injection wells operate at rates up to 100 times higher, dramatically increasing the risk of wellbore leakage and structural compromise due to severe temperature drops and associated mechanical stresses. Despite existing guidelines on material selection, there remains a substantial gap in understanding how rapid CO 2 injection rates, low surface temperatures, and variable reservoir conditions interact to threaten long-term well integrity. This study presents a comprehensive, original workflow integrating advanced analytical and numerical models for both well flow and well integrity analysis. By systematically simulating a wide range of field-relevant scenarios—including variations in injection rate, CO 2 temperature, and reservoir pressure—this work provides the first cross-validated assessment of cooling effects on wellbore. The results reveal that extreme temperature drops, up to 60 °C, can occur under high injection rates, particularly in depleted reservoirs, significantly increasing the risk of cement failure. Building on these insights, the study proposes innovative, practical well design and operational strategies, including ductile cement formulations, pre-stressing techniques, advanced insulation coatings, and proactive management of injection rates. The safety of Class VI well extends beyond simply using CO 2 resistant materials. Cement materials should possess optimal thermo-hydraulic-mechanical-chemical properties for effective performance. This work provides a scientific basis for optimizing Class VI well designs, with direct benefits for minimizing environmental risk, lowering operational costs, and enhancing the long-term reliability of GCS.

25 ENERGY STORAGE↗
DOE OSTI2023

Effect of Diphenyl Content on Viscoelasticity of Poly(dimethyl- co -diphenyl)siloxane Melt and Network

Polydimethylsiloxane (PDMS) is one of the most widely used polymeric materials for sealants, adhesives, lubricants, and thermal as well as electrical insulation. At low temperatures, however, PDMS is subject to crystallization that can cause deterioration in mechanical function. A common way to suppress such crystallization is through the incorporation of phenylsiloxane into the backbone of polysiloxane. Nevertheless, the introduction of phenyl components, even in small quantities, could potentially change the properties of the siloxane in a significant way. In this work, a series of mechanical tests and finite element simulations were performed to study the macroscale viscoelasticity of two poly(dimethyl-co-diphenyl)siloxane formulations in order to understand the effects of a few percent diphenyl contents on the viscoelasticity of the polysiloxane material. Here, we utilized the small-angle X-ray scattering to investigate the microscopic structures of the copolymers and broadband dielectric spectroscopy and rheology to probe the chain dynamics at the microscale. The results of these characterizations were used to inform the finite element simulations. We found that the degree of cross-linking does not significantly alter the microstructure but can profoundly affect the viscoelastic response of the copolymer networks. The corresponding hysteretic behavior is interpreted in terms of reptation-like motion and relaxation of the effective free chains in the cured polymer network. The relaxation of the copolymer chains is slowed significantly by even a small increase in the molar ratio of the diphenyl component.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of steady-state analytical wake models implemented in wind farm analysis software

A common set of mathematical wind turbine wake models are implemented in a few, well-adopted computational tools for wind farm wake modelling. Although the referenced mathematical formulations are common, implementation details may lead to differences in results. This study presents a systematic comparison of the implementation of mathematical wake models in open source, Python-based wind turbine wake modelling software, and a set of the models are directly compared. Despite aligning only the mathematical model parameters and retaining the default computational model parameters, good agreement is found across most of the model implementations, and additional agreement is expected upon further parameters alignment.

17 WIND ENERGY↗
DOE OSTI2021

Low friction bio-inspired polydopamine/polytetrafluoroethylene coating performance in hydrodynamic bearings

Purpose Low friction polymer coatings able to withstand high loadings and many years of continuous operation are difficult to formulate at low cost, but could find many applications in industry. This study aims to analyze and compare friction and wear performance of novel polydopamine/polytetrafluoroethylene (PDA/PTFE) and traditional tin Babbitt coatings applied to an industrial journal bearing. Design/methodology/approach This paper tested PTFE based coating, co-deposited with PDA, a biopolymer allowing sea mussels to adhere to ocean rocks. This coating was deposited on flat steel substrates and on a curved cast iron hydrodynamic journal bearing surface. The flat substrates were analyzed with a tribometer and an optical microscope, while the coated bearing liners were tested in an industrial laboratory setting at different speeds and different radial loads. Findings PDA/PTFE coating showed 2-3 times lower friction compared to traditional tin Babbitt for flat substrates, but higher friction in the bearing liners. PDA/PTFE also showed considerable wear through coating delamination and abrasion in the bearing liners. Research limitations/implications Five future modifications to mitigate coating flaws are provided, which include modifications to coating thickness and its surface finish. Originality/value While the novel coating showed excellent results on flat substrates, coating performance in a large scale bearing was found to be poor. This study shows that coating preparation needs to be improved to avoid frictional losses and unwanted damage to bearings. We provide several routes that could improve coating performance in industrial applications.

Engineering↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2025

Impact of the LiPF6 Concentration on the Interfacial Charge Transfer and Fast-charging Capabilities of Lithium-Ion Batteries

Fast-charging lithium-ion batteries (LIB) demand optimized electrolyte formulations to balance ionic conductivity, viscosity, and interfacial charge transfer kinetics. This study examines how LiPF 6 concentration shapes solvation structure, desolvation energy, charge transfer activation energy, and solid electrolyte interphase (SEI) properties, which are critical for fast-charging performance. Using Raman spectroscopy, electrochemical cycling, X-ray photoelectron spectroscopy, and atomistic modeling, we analyze how varying LiPF 6 concentrations impact interfacial and bulk transport properties. Our findings show that increasing LiPF 6 concentration alters lithium solvation structures, reduces desolvation energy, and accelerates charge transfer at the electrode interface. Higher concentrations lower the activation energy for charge transfer and suppress excessive SEI growth, improving interfacial kinetics. However, concentrations above a certain threshold increase viscosity and reduce ionic conductivity, limiting transport efficiency. These results offer insights into electrolyte solvation and interfacial charge transfer mechanisms, providing guidelines for designing next-generation fast-charging LIB electrolytes with enhanced efficiency, stability, and longevity.

Son, Seoung-Bum [Argonne National Laboratory (ANL)↗
DOE OSTI2025

A Finite Element Method for Compressible and Turbulent Multiphase Flow Instabilities with Heat Transfer

We present a new finite element framework for modeling compressible, turbulent multiphase flows with heat transfer. For two-fluid systems with a free surface, the Volume of Fluid (VOF) method is implemented without the need for interface reconstruction, while turbulence is resolved using a dynamic Vreman large eddy simulation (LES) model. Unlike most two-phase VOF studies, which neglect heat transfer, the present approach incorporates energy transport equations within the VOF formulation to account for heat exchange, an effect particularly important in turbulent flows. Conjugate heat transfer is often challenging in finite volume methods, which require explicit specification of heat fluxes at the solid–fluid interface, limiting accuracy and predictive capability. By contrast, the finite element formulation does not require heat flux inputs, allowing more accurate and robust simulation of heat transfer between solids and fluids. The method is demonstrated through three representative cases. First, a two-fluid instability with a single-mode perturbation is simulated and validated against analytical growth rates. Second, conjugate heat transfer is examined in a high-temperature flow over a cold metal cylinder, with validation performed both quantitatively—via pressure coefficient comparisons with experimental data—and qualitatively using vector field topology. Finally, compressible spray injection and breakup are modeled, demonstrating the ability of the framework to capture interfacial dynamics and atomization under turbulent, high-speed conditions. In the compressible spray injection and breakup case, the results indicate that the finite element formulation achieved higher predictive accuracy and robustness than the finite-volume method. With the same mesh resolution, the FEM reduced the root mean square error (RMSE) and mean absolute percentage error (MAPE) from 6.96 mm and 26.0% (for the FVM) to 4.85 mm and 12.7%, respectively, demonstrating improved accuracy and robustness in capturing interfacial dynamics and heat transfer. The study also introduced vector field topology to visualize and interpret coherent flow structures and instabilities, offering insights beyond conventional scalar-field analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Higgs Factory Considerations

We discuss considerations that can be used to formulate recommendations for initiating a lepton collider project that would provide precision studies of the Higgs boson and related electroweak phenomena.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Verification of a Fluid-Based Plasma-Edge Model Within the Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework

As the goal of achieving fusion power on the grid comes closer to fruition, fully coupled multiphysics models of fusion devices will be crucial. These models must incorporate the interconnected phenomena of these devices, including plasma physics, neutronics, first wall interactions, and tritium transport. Currently, there are two main approaches to developing these platforms: (1) loosely coupled, where one couples existing codes and solvers together through input and output parameters and data, and (2) tightly coupled, where one develops the necessary models within a singular, integrated framework. This work focuses on the latter approach for magnetically confined fusion devices by developing a fluid-based plasma-edge model within the Multiphysics Object Oriented Simulation Environment (MOOSE) Framework. This effort is coordinated with other efforts to develop, test, demonstrate, and deploy fusion relevant multiphysics capabilities including electromagnetics, particle-in-cell plasma, tritium transport, and fusion blanket design. This new model is an expansion of the MOOSE-based plasma application, Zapdos, which was originally formulated to model low-temperature, non-magnetized plasma processes. Verification studies have been conducted using newly developed magnetic plasma capabilities. These involved convergence analyses utilizing the method of manufactured solutions to verify new operators and case studies. A modular approach was taken here to demonstrate increasingly complicated simulation scenarios, which included a singular fluid with uniform magnetic field case, a singular fluid with spatially varying magnetic field case, and a coupled multifluid case.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

We present a benchmarking study of active learning (AL) schemes for designing planar multilayer nanophotonic metamaterials, where the design tasks are formulated as binary optimization problems. Different surrogate models, including factorization machine (FM), Gaussian process regression (GPR), and convolutional neural network (CNN), combined with different optimization methods, including exhaustive enumeration, discrete particle swarm optimization (DPSO), quantum annealing (QA), hybrid QA, and simulated annealing are studied. The benchmark cases investigated range from small problems with short binary lengths (N = 25) to large problems with N up to 100, focusing on the design of two classes of photonic structures, including antireflective coatings for the long-wavelength infrared region and transparent radiative coolers. For small problems, CNN coupled with DPSO in AL achieves the best performance. As N increases, FM with QA outperforms GPR and CNN. For FM-based AL, hybrid QA yields the best optimization results, particularly in high-dimensional cases (N = 100). These results demonstrate that the optimization method can significantly affect in AL performance as N increases, and that QA-based optimization can provide practical routes for mitigating the optimization bottleneck in high-dimensional problems.

Jung, Serang [Kyung Hee University, Korea]↗