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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
NASA NTRSDate not supplied

Thermo-Poro-Mechanical Modeling of RTV Intumescence

Room temperature vulcanizing (RTV) silicone is a high-temperature adhesive used as a gap-filler between heatshield tiles in numerous entry missions. Its propensity to intumesce, or swell upon exposure to heat, is a well-known effect that needs to be carefully quantified during design. At tile interfaces of charring ablators, intumescence, combined with differential recession, could cause the gap filler to protrude past the ablator outer mold line, forming a “fence”. Fencing can in turn cause transition to turbulence of the flow wetting the heat shield, leading to augmented surface heating. Recent experiments conducted at the Plasmatron X facility, the high enthalpy wind tunnel of the Center for Hypersonics and Entry Systems Studies, have shown prominent fencing of RTV gap fillers in PICA, under both nitrogen and air plasmas. Similar observations are well known in the arcjet literature. Further experiments under controlled environment, performed using in situ X-ray micro-computed tomography (micro-CT) at the Advanced Light Source (ALS), have shown heating rate-dependent swelling and shrinkage of RTV during pyrolysis. To simulate RTV intumescence, a novel model was introduced in the Porous Materials Analysis Toolbox based on OpenFOAM, PATO, to account for pore-pressure buildup within both closed- and open-pores. The governing equation for the thermo-poro-mechanical response were developed, assuming linear elasticity for the charring silicone. A new multi-pyrolysis model that tracks non-monotonic advancement of material properties with pyrolysis was proposed. This model addresses the limitations of state-of-the-art ablator models to capture the different stages of thermal degradation and coupled thermomechanics. Swelling of RTV was simulated using the new thermo-poro-mechanical model and compared against in situ micro-CT data. Results showed good agreement in intumescence height and temperature profiles at all heating rates, indicating that the key factor contributing to RTV swelling is the internal pressure build-up within closed- and open-pores. As RTV is cured into a soft (rubbery) compound with low-porosity and permeability, initial temperature increase and pyrolysis gas production cause a significant increase of internal pressure, causing a pronounced volume growth. As thermal degradation progresses, rigidization of the silicone occurs due to char hardening which counteract volume shrinkage after gas pressure relief. Overall, our model shows that accounting for changes in properties such porosity, permeability and key thermomechanical coefficients is crucial for capturing the RTV volume change during ablation and enable a predictive capability for heatshield tile interface response. A plan for future calibration of thermomechanical properties evolution during degradation is discussed, as a key next step to close the new model.

RTV↗
NASA NTRSDate not supplied

Thermo-Poro-Mechanical Modeling of RTV Intumescence

Room temperature vulcanizing (RTV) silicone is a high-temperature adhesive used as a gap-filler between heatshield tiles in numerous entry missions. Its propensity to intumesce, or swell upon exposure to heat, is a well-known effect that needs to be carefully quantified during design. At tile interfaces of charring ablators, intumescence, combined with differential recession, could cause the gap filler to protrude past the ablator outer mold line, forming a “fence”. Fencing can in turn cause transition to turbulence of the flow wetting the heat shield, leading to augmented surface heating. Recent experiments conducted at the Plasmatron X facility, the high enthalpy wind tunnel of the Center for Hypersonics and Entry Systems Studies, have shown prominent fencing of RTV gap fillers in PICA, under both nitrogen and air plasmas. Similar observations are well known in the arcjet literature. Further experiments under controlled environment, performed using in situ X-ray micro-computed tomography (micro-CT) at the Advanced Light Source (ALS), have shown heating rate-dependent swelling and shrinkage of RTV during pyrolysis. To simulate RTV intumescence, a novel model was introduced in the Porous Materials Analysis Toolbox based on OpenFOAM, PATO, to account for pore-pressure buildup within both closed- and open-pores. The governing equation for the thermo-poro-mechanical response were developed, assuming linear elasticity for the charring silicone. A new multi-pyrolysis model that tracks non-monotonic advancement of material properties with pyrolysis was proposed. This model addresses the limitations of state-of-the-art ablator models to capture the different stages of thermal degradation and coupled thermomechanics. Swelling of RTV was simulated using the new thermo-poro-mechanical model and compared against in situ micro-CT data. Results showed good agreement in intumescence height and temperature profiles at all heating rates, indicating that the key factor contributing to RTV swelling is the internal pressure build-up within closed- and open-pores. As RTV is cured into a soft (rubbery) compound with low-porosity and permeability, initial temperature increase and pyrolysis gas production cause a significant increase of internal pressure, causing a pronounced volume growth. As thermal degradation progresses, rigidization of the silicone occurs due to char hardening which counteract volume shrinkage after gas pressure relief. Overall, our model shows that accounting for changes in properties such porosity, permeability and key thermomechanical coefficients is crucial for capturing the RTV volume change during ablation and enable a predictive capability for heatshield tile interface response. A plan for future calibration of thermomechanical properties evolution during degradation is discussed, as a key next step to close the new model.

silicone intumescence↗
NASA NTRSDate not supplied

Time-History Statistics of Soot Formation in A Model Gas Turbine Combustor

Soot formation is a complex dynamic and intermittent process determined by properties of the fuel, combustor design, and combustor operation. Although the major steps in soot formation (i.e., formation of precursors, inception, growth and evolution) are similar for a variety of carbonaceous fuels, applications, and operating conditions, it remains unclear when the temporal transition between these steps occurs. An engineering prediction tool coupled with computational fluid physics (CFD), therefore needs to accurately model all these complex steps. To develop such a model, we propose the time-history concept for understanding the time dependency of soot formation as a function of local properties (i.e., temperature, velocity, local fuel air ratio, etc.). We continue our previous work with modeling the DLR aero-combustor [1] with our updated in-house CFD code, Open National Combustion Code (OpenNCC), that now includes a Multiple Time-Scale Flamelet Progress Variable approach and a the semi-empirical two-equation soot model. We injected massless tracer particles upstream of the injector region of the combustor to collect time-history statistics of the solution variables. The correlations between the collected statistics with respect to the experimental soot volume fraction data showed that time-history effect of certain flow variables, including turbulent kinetic energy (TKE), and multiple species is indeed important for soot formation. We then conducted a time-history based correlation analysis to determine the key species and the concentration ranges critical for soot formation (C6H5-based nucleation, acetylene-based surface growth, and oxidation with OH and O2). Based on the time-history correlation coefficient (THCC) analysis, we propose possible modifications to improve the current two-equation model.

LES↗
DOE OSTI2025

Development of a high fidelity CFD model for solvent evaporation and transport in porous structure during battery electrode drying

An efficient battery manufacturing process is the key to the mass production of Electric Vehicles (EV), in which drying is one of the most energy-intensive steps significantly influencing the battery cell performance. An accurate 3D CFD model for drying is essential for predicting the drying mechanism and optimizing its parameters. By optimizing the drying process, it is possible to reduce energy consumption and cost during battery manufacturing, minimize binder loading and maximize active material loading to achieve superior electrochemical performances and facilitate wider and faster public adoption of EV. This project aims to optimize the drying process during electrode manufacturing by leveraging high-fidelity, porous electrode simulations for solvent evaporation. By optimizing this process, we seek to reduce energy consumption during battery manufacturing, while minimizing binder loading and maximizing active material loading, with the overall goal of enhancing electrical vehicle performance.

Horner, Jeffrey Scott [Sandia National Laboratorie↗
NASA NTRSDate not supplied

Turbo-Design: Open-Source Radial Equilibrium Turbomachinery Solver: Part I - Turbines

Advances in 3D Geometrical Designs and Cooling have played a significant role in improving the efficiency of turbomachinery. However, these advancements must be effectively translated back to the modeler. Machine learning can facilitate this transition. Specifically, machine learning–based loss models can bridge the gap between 3D and 1D designs, enabling modelers not only to predict velocity triangles but also to extract additional geometric features. Currently, the design tools used at NASA have not been updated to support such integration—until now. TurboDesign is an open-source, Python-based framework that replaces TD2 (LEW-11029-1) and AXOD2 (LEW-16323-1), both of which are radial equilibrium solvers for axial turbines. The goal of this update is to enable the integration of machine learning loss models into radial equilibrium equations. Additionally, TurboDesign is designed to support radial machines. This paper presents the governing equations, the assumptions underlying the code, the integration of legacy loss models, an example of machine learning model integration, and a validation comparison with CFD. All code, tutorials, and documentation are available at: https://www.github.com/nasa/turbo-design

Radial Equilibrium↗
DOE OSTI2026

Development of an Amine Oxide Polyzwitterion Brush Martini Model with Polarizable Water and Ions

Abstract In order to accurately simulate the fouling process of proteins onto polyzwitterion brushes, models that accurately capture the hydration properties and chain conformations of such brushes must first be established. We developed a Martini coarse-grained (CG) model for amine oxide polyzwitterion (PNOMA) brushes, a promising class of antifouling materials, in polarizable water and ions by fitting to all-atom bond and angle distributions, monomer hydration free energy, monomer–monomer distance potential of mean force (PMF), and monomer–salt radial distribution functions (RDFs). Martini 2.2P was selected for compatibility with the established polarizable water and ion models. For comparison with PNOMA, we also constructed models for conventional sulfobetaine (PSBMA) and phosphorylcholine (PMPC) polyzwitterions and the polycation PMETAC using established nonbonded bead types from the literature and refitting bond and angle potentials. We simulated each polymer brush chemistry for varying grafting density and chain length, validating brush height scaling relations against experimental data. The CG models captured the relative hydration strengths among different polyzwitterion chemistries, and brush heights extrapolated to higher molecular weights are in agreement with experimental ellipsometry data. We find that chain swelling of the superhydrophilic PNOMA brushes lies between that of the traditional polyzwitterions PSBMA/PMPC and the polycation PMETAC. For PNOMA brushes in NaCl solution, simulated brush height decreases with salt concentration due to the selectively strong interactions between amine oxide and sodium ions.

Walker, Christopher C. [Oak Ridge National Laborat↗
DOE OSTI2026

Probabilistic Resource Adequacy Suite (PRAS) v0.8 Model Documentation

The Probabilistic Resource Adequacy Suite, or PRAS, is a software package for studying power system resource adequacy. It allows the user to simulate power system operations under a wide range of operating conditions, in order to study the system's risk of failing to meet demand due to a resource shortfall, and identify the time periods and regions in which that risk occurs. This reports documents version 0.8 of the tool.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1992

Ruled and holographic experiment (AO 138-5)

The AO 138-5 experiment has been designed, via the French Cooperative Payload (FRECOPA) experiment with the aim to study the optical behavior of different diffraction gratings submitted to space vacuum long exposure and solar radiation. Samples were rules and holographic gratings, masters or replica, and some additional control mirrors with various coatings. The experiment was located on the B3, trailing edge of the Long Duration Exposure Facility (LDEF) and has been protected against atomic oxygen flux. The experienced thermal cycling has been evaluated from -23 C to 66 C during the flight, 34,000 orbits. The analysis has been focused on the triple point characterization including light efficiency, wavefront flatness quality and stray light level. Tests were conducted on control mirrors and gratings loaded but not exposed to cosmic dust or solar irradiations. They did not show any significant variations. Solar exposure has damaged the coating reflectivity in the ultraviolet region, the degradation is higher with the gratings, in terms of efficiency. However, wavefront flatness quality and stray light level tests revealed no additional changes.

Francis Bonnemason↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2024

Spectroscopy in Nanoscopic Cavities: Models and Recent Experiments

The ability of nanophotonic cavities to confine and store light to nanoscale dimensions has important implications for enhancing molecular, excitonic, phononic, and plasmonic optical responses. Spectroscopic signatures of processes that are ordinarily exceedingly weak such as pure absorption and Raman scattering have been brought to the single-particle limit of detection, while new emergent polaritonic states of optical matter have been realized through coupling material and photonic cavity degrees of freedom across a wide range of experimentally accessible interaction strengths. In this review, we discuss both optical and electron beam spectroscopies of cavity-coupled material systems in weak, strong, and ultrastrong coupling regimes, providing a theoretical basis for understanding the physics inherent to each while highlighting recent experimental advances and exciting future directions.

Chemistry↗
NASA NTRS1993

The Flight Performance of the Galileo Orbiter USO

Results are presented from an analysis of radio metric data received by the DSN stations from the Galileo spacecraft using an Ultrastable Oscillator (USO) as a signal source. These results allow the health and performance of the Galileo USO to be evaluated, and are used to calibrate this Radio Science instrument and the data acquired for Radio Science experiments such as the Red-shift Observation, Solar Conjunction, and Jovian occultations. Estimates for the USO-referenced spacecraft-transmitted frequency and frequency stability were made for 82 data acquisition passes conducted between launch (October 1989) and November 1991. Analyses of the spacecraft-transmitted frequencies show that the USO is behaving as expected. The USO was powered off and then back on in August 1991 with no adverse effect on its performance. The frequency stabilities measured by Allan deviation are consistent with expected values due to thermal wideband noise and the USO itself at the appropriate time intervals. The Galileo USO appears to be healthy and functioning normally in a reasonable manner.

D D Morabito↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Design and Modeling of the Charge Readout of a SiMOS Quantum Dot with a Single Electron Transistor and CryoCMOS

Single electron spin qubits trapped in SiMOS quantum dots are a promising technology for scaling to thousand- or million-qubit systems due to their compatibility with mature CMOS manufacturing processes. A readout system that combines a single electron transistor with a custom cryogenic CMOS amplification and digitization chain offers key advantages by avoiding the use of bulky RF components or room-temperature interconnects. We present design techniques and simulation results for an optimized qubit-SET cryoCMOS interface, culminating in the design of the QNDR1 ASIC, the first cryogenic readout ASIC designed under the Quandarum project, which targets the development of a many-channel spin qubit based detector for use in high energy physics.

Quinn, Adam [Fermilab] (ORCID:0009000605056371)↗
DOE OSTI2025

Advanced Model Development for Large Eddy Simulation of Oxy-Combustion and Supercritical Carbon Dioxide Power Cycles

A joint experimental and numerical study is performed to observe the characteristics of a supercritical carbon dioxide turbulent mixing layer in the presence of strong nonlinearities in the thermodynamic and transport properties. A bespoke experimental setup is designed and employed for this purpose and provides insight into macroscopic mixing behavior. The mixing is experimentally observed using two techniques: shadowgraphy and spontaneous Raman scattering. Qualitative and quantitative intensity fields obtained via these techniques yield instantaneous and mean density data. Spanwise temperature data is also collected using analogue resistance temperature detectors. These measurements are used to quantify the level of mixed material within the field. The experimental data are supplemented by a companion high-fidelity numerical study. The numerical results are obtained through fully resolved, three-dimensional direct numerical simulation. The numerical dataset permits observation of the near-field mixing characteristics, which are difficult to measure experimentally due to the rapid dynamics and sharp thermophysical gradients in this area. Qualitative field visualizations are presented, followed by quantitative mixed material results and observations regarding thermodynamic property trends at select locations within the field. One-dimensional spectra of the turbulent kinetic energy and solenoidal dissipation are provided to observe the spectral characteristics of the flow. Reynolds stress anisotropy is analyzed graphically through anisotropy invariance maps (Lumley triangles). The mixing quantification, spectral data and anisotropy analysis of a flow at these thermodynamic conditions represent the main outcomes of the work.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2026

Characterizing the subglacial environment of lower Thwaites Glacier using radar modeling

Understanding the spatial heterogeneity beneath Thwaites Glacier, West Antarctica, is vital to projecting its impact on future sea levels. Radar-echo sounding (RES) is commonly used to infer subglacial conditions, but these data can be challenging to interpret. We assess basal heterogeneity across Thwaites Glacier by comparing RES returns to a radar backscattering simulator for over 400 km of RES data. The modeled variations in bed returned power exhibited a strong correlation with actual RES data in 40% of our simulated flight segments, which we consider evidence for a relatively homogeneous glacier bed. Other sites (40%) demonstrated improved fit quality when hydrology or substrate transitions were introduced in the bed material model. The remaining simulated segments (20%) were diagnosed as having more complex basal heterogeneity. The spatial distribution of complex heterogeneity appears to coincide with asymmetric patterns in the RES specularity content, which has been interpreted in previous studies as a signature for channelized hydrology. Conversely, the homogeneous substrate locations coincide with areas of fast-moving ice in western Thwaites. Our simulation method can isolate power variations induced by material heterogeneity vs topography, which is an important limitation of existing RES analysis methods.

Antarctic glaciology↗
DOE OSTI2026

Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN

We demonstrate an iterative approach to modeling a sparsely measured magnetic field in a large-bore solenoid. This approach uses a hybrid of traditional and machine learning techniques. The traditional technique is a linear least-squares fit using a series solution to Laplace's equation, while the machine learning technique involves the training of a physics-informed neural network (PINN) on the least-squares fit residuals. We use a newly defined activation function "DELTAsnake," a modification to the snake activation function proposed by Ziyin et al. that allows for stronger curvature and non-monotonicity. The combined model approximately obeys Maxwell's equations to a level sufficient for producing high quality physics simulations and analysis. Our approach is applied to a highly realistic calculation of the expected magnetic field in the Mu2e experiment's Detector Solenoid which includes a simple model for the expected statistical measurement uncertainties. Using ten toy measurement simulations, we demonstrate the capabilities of our model in comparison to the least-squares method alone; the least-squares method alone results in a reduced chi-squared statistic of ${2.15 \pm 0.01}$, while our approach improves the reduced chi-square to ${1.034 \pm 0.005}$. Furthermore, for an average toy simulation, we show that the range of the RMS of the three field component residuals reduces from ${0.07-0.37}$ Gauss to ${0.05-0.07}$ Gauss. We find that this novel method is robust against a realistic systematic uncertainty deriving from Hall probe calibration bias and can be used to significantly reduce the number of measurements required to achieve an accurate model.

Kampa, Cole [Caltech] (ORCID:0000000192972920)↗