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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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Elastic Bayesian Model Calibration
Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.
Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization
Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.
Direct Utilization of Pure and Denatured Ethanol in Metal Supported Solid Oxide Fuel Cells
Metal supported solid oxide fuel cells (MS-SOFC) are integrated with internal reforming catalyst for direct utilization of ethanol to generate electricity. MS-SOFCs are operated up to 500 h at 700°C, with water-ethanol blend fuel using high-purity ethanol and denatured ethanol. Performance and durability with denatured ethanol varies dramatically with the composition of the denaturant. Cells operated with three denatured ethanol fuels containing small amount of methanol, isopropanol, and denatonium benzoate demonstrate similar and relative stable performance after stabilization, suggesting that these fuels are fit for MS-SOFC operation. No or minimum carbon deposits are observed on the high entropy alloy-based reforming catalyst layer. Denatured ethanol containing gasoline and toluene leads to fast degradation. Here, MS-SOFCs are promising for direct utilization of denatured ethanol, offering a path to rapid-start, carbon-neutral operation with widely-available fuels.
Density Functional Theory Study of the Initial Stages of Cl-Induced Degradation of α -Cr 2 O 3 Passive Film
The ion exchange and point defect models are two prominent models describing the role of anions, such as chlorides, in the degradation of passive oxide films. Here the thermodynamic feasibility of critical steps of Cl-induced degradation of a hydroxylated α-Cr 2 O 3 (0001) surface, as proposed by these two models, are studied. Both models begin with Cl substitution of surface OH and H 2 O, which becomes less favorable with increasing Cl coverage. The initial stages of Cl-induced breakdown of the α-Cr 2 O 3 depend on Cl coverage and the presence of O vacancy near the surface as follows: (1) neither Cl insertion (supporting the ion exchange model) nor Cr vacancy formation (supporting the point defect model) is feasible at low Cl coverages except in the presence of O vacancies near the surface, where Cl insertion is thermodynamically feasible even at low coverages, (2) in the absence of O vacancies, Cr vacancy formation becomes feasible from 10/12 ML onwards whereas Cl insertion by exchange with subsurface OH only becomes feasible at full coverage. This implies that at higher coverages Cl-induced degradation first initiates through a vacancy formation mechanism, but both insertion and vacancy formation would be feasible at full coverage.
Tape Casting of Thin Electrolyte and Thick Cathode for Halide-Based All-Solid-State Batteries
Most previous studies about halide solid-state electrolytes have used pellets prepared by uniaxial pressing, which is a good approach for materials development but is not suitable for commercialization. Thinner electrolyte layers that can be scaled up to large cell areas are required, and tape casting is a promising approach. It is challenging, however, as halide materials are reactive with most of the conventional solvents used in the process. In this study, solvents with low polarity, such as toluene, are found to be compatible with the Li 3 YBr 6 halide material. A wide variety of candidate binders that are soluble in toluene are studied. MSB1–13 binder is preferred, based on the ionic conductivity and mechanical properties of the tape. Electrolyte tapes (<70 μ m) are successfully cast on Al substrates, using 2 wt% binder. The resulting room temperature ionic conductivity is 2 × 10 −4 S cm −1 . Two composite cathodes including active material (LiFePO 4 or LiNi 0.82 Mn 0.07 Co 0.11 O 2 ) and 1 to 1.5 wt% MSB1–13 are tape cast as proof-of-concept for a scalable cell fabrication approach. A LiFePO 4 cell shows good retention at 25 °C. The performance of NMC cells with tape electrolyte or pellet electrolyte is similar. This study demonstrates the feasibility of tape casting halide-based electrolytes and cathodes.
Succinonitrile-Lithium Salt Complexes as Solid Catholytes for LLZO-Based Solid-State Batteries
The thermal and electrochemical properties of several succinonitrile (SN)-based organic ionic plastic crystals (OIPCs) containing lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), lithium bis(oxalate) borate (LiBOB) or mixtures of the two salts, were investigated with the goal of determining which is most promising for use as a catholyte in solid-state lithium batteries. The best combination of properties for this use was found for the mixture containing 3 mol% LiTFSI and 2 mol% LiBOB in SN. Based on these observations, several solid-state cells containing Al-substituted Li 7 La 3 Zr 2 O 12 separators with composite cathodes consisting of LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC111), carbon black, and the succinonitrile-salt mixtures were assembled and cycled at room temperature, without exogenous pressure, using a simple design.
Pioneering Microporous Layers for Proton-Exchange-Membrane Water Electrolyzers via Tape Casting
The imperative shift towards decarbonization necessitates the production of clean hydrogen through water electrolysis, powered by renewable energy sources. Among electrolyzer technologies, proton-exchange-membrane (PEM) systems emerge as a promising option for large-scale hydrogen generation due to their modular design and rapid response, aligning well with the intermittency of renewable energy. In this study, we employ a tape casting method to fabricate microporous layers (MPLs), both as a single layer and as a bilayer over commercial porous transport layers (PTLs), to further enhance performance of water electrolyzers. We demonstrate that microporous layers require adequate pore sizes to facilitate gas removal, preventing gas flooding and preserving electrolyzer performance. Our single layer microporous layers exhibit lower overpotentials compared to commercial sintered Ti PTLs by 142 mV at 4 A·cm ⁻2 . Moreover, we show that having an effective microporous layer enhances electrolyzer performance irrespective of the substrate used, offering avenues for cost reduction. We also investigate novel PTL structures with reduced tortuosity and integrated MPL fabricated via phase inversion tape casting, resulting in a performance enhancement of 92 mV. Our findings unravel the critical role of microporous layer structures and their impact on electrolyzer performance.
CO 2 Electrolysis Using Metal-Supported Solid Oxide Cells with Infiltrated Pr 0.5 Sr 0.4 Mn 0.2 Fe 0.8 O 3-$δ$ Catalyst
Electrochemical conversion of CO 2 to CO is demonstrated with symmetric-structured metal supported solid oxide cells (MS-SOC). Perovskite Pr 0.5 Sr 0.4 Mn 0.2 Fe 0.8 O 3-δ (PSMF) and Pr 6 O 11 catalysts were infiltrated into the MS-SOC cathode and anode, using 3 cycles with firing at 850 °C and 8 cycles with firing at 800 °C, respectively. Upon reduction during operation, the perovskite PSMF was transformed to Ruddlesden–Popper structure with a highly efficient electrocatalytic activity. The impact of operating temperature (600–800 °C) and overpotential (0–1.8 V) on the CO 2 conversion was investigated. The highest CO 2 conversion of 57.2% was achieved at 750 °C and 1.8 V. During extended operation for 150 h at 750 °C and 1.2 V, a cell demonstrated relatively stable performance, with initial current density of 535 mA cm -2 and CO 2 conversion of 23%. Degradation mechanisms were studied by posttest characterization.
Optimization of Lithium Content and Sintering Additives in Tape Cast Lithium Garnet Electrolyte Sheets
The lithium garnet (LLZO) remains one of the most promising electrolyte materials for producing solid state batteries. Tape casting is one method for producing the thin ceramic electrolyte layer required for a high-performance solid state battery. Processing of LLZO is complicated by several factors including lithium volatilization, abnormal grain growth and phase instability. Many of these issues are exacerbated in tape cast sheets by the sheets' higher surface area to volume ratio, when compared to pellet processing methods. Here, we report on an environmentally friendly aqueous tape casting process for LLZO using methylcellulose as a binder and compare final sheet properties to other, solvent-based, options. Solids loading, binder content, dispersant composition, and wetting agent type are varied to improve ease of casting and enhance properties of the as-cast tape. Lithium content in sintered LLZO sheets is controlled by the inclusion of excess Li 2 CO 3 as lithium source in the ceramic tape itself and by a novel method of producing a lithium-saturated furnace atmosphere. Sintering time and temperature are optimized, and rapid thermal processing is investigated as a method of reducing lithium loss. Al 2 O 3 and MgO are examined for use as sintering additives. Characterization of LLZO sheets is performed with XRD, EIS and SEM. Li 2 CO 3 and sintering additive levels are optimized for ionic conductivity and density. The optimization effort improved the conductivity and density of 100µm thick sintered sheets to greater than 3x10 -4 S/cm conductivity and 90% density.
Scalable Object Store v. 1.0.0
SOS is software that stores data values in a set of structured flat files. The data is organized in the files based upon functions specified by the user in order to enable fast insertion and retrieval of data.
BCS (CSP Beam Characterization System) [SWR-23-13]
The Beam Characterization System (BCS) is an image-processing tool for estimating heliostat tracking errors. A large mirror, or heliostat, reflects the sun onto a distant target. A carefully calibrated camera then takes an image of this target to capture the shape of the reflected beam. The software kit hosts a series of scripts to identify key features and run analysis on the captured images.
VEESA R package
SAND2024-04584O R package for applying the VEESA pipeline method is a technique used for explainable machine learning with functional data. The VEESA pipeline makes use of the elastic-shape analysis framework for functional data. It also implements functional principal component analysis and permutation feature importance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
STPPHAWKES
SAND2024-08541O STTPHAWKES software solution is a Bayesian estimation approach to a Hawkes process model that treats missing data as a latent variable in the estimation. The open-source software will be released to the R CRAN repository and Sandia National Labs’ Github. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Impala
SAND2025-07450O Impala is a software tool that performs Bayesian model calibration for emulator-based models. The program uses R code to perform the calculations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
mvBayesR
SAND2025-11559O The mvBayesR tool performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis, and visualization. The tool compiles state-of-the-art methodology into one easy-to-use package. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
mvBayesPy
SAND2025-11476O The mvBayesPy tool is a Python package that performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis and visualization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
mvBayes
SAND2026-16980O mvBayes implements multivariate Bayesian regression using MATLAB and decomposes a multivariate or functional response into components based on a user-specified orthogonal basis. This allows for independent modeling of each component with any chosen univariate Bayesian regression model. This tool includes methods for prediction and visualization, facilitating the evaluation of Bayesian surrogate models through the application of Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling techniques. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.