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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analytical-based simulation approach for an anion exchange membrane fuel cell

An analytical and empirical-based 1-D, non-isothermal, steady-state model for anion exchange membrane fuel cell capable of capturing two-phase phenomena is presented in this study. Coupled multi-physics including mass and charge transport, electrochemical reactions, heat transfer, and two-phase water transport are considered in the model and the simulated results are compared to experimental data. To better represent actual material properties and localized conditions, the model applies multilayer discretization in the gas diffusion electrode to enhance prediction accuracy. The model successfully predicts the baseline performance at 70 °C, 131 kPa abs., 92% RH with pure H 2 /O 2 gas as well as the limiting current at 10% H 2 . The robust simulation approach allows for simplistic and accurate estimation of cell performance without the complications of applying two-phase parameters and expensive computational need for numerical models. In addition, the results from the sensitivity studies of material properties and operating conditions provide valuable insights on water management strategies and optimal component design for advancing anion exchange membrane fuel cell technology.

1-D model↗
DOE OSTI2020

The roles of yield strength mismatch, interface strength, and plastic strain gradients in fatigue crack growth across interfaces

An irreversible cohesive zone model was used to understand how plastic strain gradients and yield strength mismatches affect fatigue crack growth across bi-material and multilayer interfaces for a range of interface strengths. Parametric studies identified regimes of penetration through the interface, deflection into the interface, and a transition where cracks extend on both paths. Plastic strain gradients strongly influence the transition regime, and a yield strength mismatch is necessary to cause deflection. For crack growth across an interlayer, interactions between the interfaces occur when the crack-tip plastic zone spans the interlayer. Plastic strain gradients promote penetration through the interlayer.

42 ENGINEERING↗
DOE OSTI2022

Synergistic effects of chlorine substitution in sulfide electrolyte solid state batteries

All-solid-state battery is considered as one of the most promising competitors to Li ion batteries. Two widely known performance metrics for solid electrolytes, among others, are ionic conductivity and stability. In this study, it is found that both can be improved by the synergistic effects of chlorine substitution in sulfide-based solid electrolytes. Particularly, instabilities arising from both bulk decompositions and interfacial reactions to electrodes can be better inhibited in the chlorine substituted sulfide solid electrolytes through the increased susceptibility to the mechanical constriction induced enhancement of voltage stability. As a result, the stability window of some chlorine-rich Li-argyrodites can be systematically higher than some other chlorine-deficient or chlorine-free electrolytes, especially under the implementation of the mechanical constriction battery assembly and test conditions. Thus, a solid-state battery system of 4 V to 5 V-class cathodes paired with lithium metal anode is demonstrated using these chlorine-rich Li-argyrodites without additional coatings. Furthermore, since Cl composition modulates the stability and instability of Li-argyrodite at low voltages, it allows us to design a multilayer configuration with a hierarchy of Li metal stabilities to demonstrate the stable cycling at relatively high current densities for solid-state batteries. It is found that a moderate Cl composition in the electrolyte is the best to inhibit Li dendrite penetration as the central electrolyte layer, emphasizing a slightly increased “instability” as the hidden performance metric of relevance here, in addition to the two well-known metrics of stability and ionic conductivity. The understanding of the chlorine substitution effect in sulfide electrolytes provides an important design principle for all-solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2018

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Highlights: • A new hybrid approach is proposed to improve forecasts of hydro-meteorological time series. • Rank-Set Pair Analysis combined with wavelet de-noising markedly improves forecasting accuracy. • The performance of the proposed approach proves best among its present competitors even when the extreme value occurs. Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

58 GEOSCIENCES↗
DOE OSTI2018

Modeling and MANOVA studies on toxicity and endocrine potential of packaging materials exposed to different extraction schemes

Highlights: • Simultaneous assessment of the impact of treatment conditions on packages ecotoxicity. • Checking of the impact of temperature and extracting solvents on endocrine potential of packages. • Comparison of the package lining spectra before and after extraction. • Ecotoxicity parameters as a tool in packages lining stability assessment. The stability of the linings of packaging that is in contact with the goods stored has been of major concern during decades of the development of packaging materials. In this work, an attempt was undertaken to assess the applicability of using two bioassays (Microtox® and XenoScreen YES/YAS) in estimating the stability of packaging (cans, caps, multilayer material) and the impact of their degradation on the toxicity of some simulated media. The assessment of the impact of packaging storage conditions (temperature, disinfection, preservation, extracting and washing solvents) was planned and performed with i) regression modeling of the experimental effects on the ecotoxicity readings, ii) ANOVA and MANOVA estimation of the experimental conditions as significant factors affecting the toxicity results and iii) FTIR analysis of the packages. It is shown that the effects of temperature and extraction solvents could be quantitatively assessed by the agreement between all methods applied. It can be stated that temperature and acidity as well as the alcohol content in the sensitive media have the greatest impact on the toxicity of the extract and thus on the stability of the internal lining and the extractability of xenobiotics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model-free stabilization via Extremum Seeking using a cost neural estimator

In this paper, a fully model-free architecture for vertical stabilization of thermonuclear plasmas in tokamak experimental reactors is presented. For the first time, an Extremum Seeking control algorithm is combined with neural networks to estimate the Lyapunov function to be minimized, resulting in a fully data-driven control architecture. The performance of different neural networks are compared. Specifically, Multilayer Perceptrons and Extreme Learning Machines are considered. The proposed architecture is tested in simulation to show that it can counteract relevant plasma disturbances, resulting in a significant improvement in terms of the achievable operative space compared to the Extremum Seeking algorithm, which still relies on model-based cost estimator.

42 ENGINEERING↗
DOE OSTI2018

Molecular simulations of competitive adsorption of carbon dioxide – methane mixture on illitic clay surfaces

Molecular dynamics and Monte Carlo simulation studies were carried out here to investigate adsorption behavior of carbon dioxide and methane mixtures on illitic clay surfaces under dry conditions. Various compositions of the mixtures and distributions of isomorphic substitutions in clay layers were chosen to explore competitive adsorption depending on component concentration and charge localization. The simulations show that carbon dioxide is preferentially sorbed on the illitic surface and is capable to promote methane desorption. Density distributions of the molecular species in pore space reveal formation of multilayers on the clay surfaces at elevated pressures. Finally, mixed adsorption isotherms were compared with adsorption isotherms of pure compounds and thermodynamic quantities were reported to characterize the interaction of the carbon dioxide and methane with the clay surface.

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

Reexamining supercritical gas adsorption theories in nano-porous shales under geological conditions

Adsorption is an important phenomenon in surface chemistry, especially for nano-porous shales. In the shale gas-in-place, adsorbed gas could contribute up to 85%. However, adsorption is hard to quantitatively characterize due to different adsorption mechanisms, patterns, surfaces, and pore sizes. Moreover, key thermodynamic parameters, such as the enthalpy of adsorption, are challenging to determine due to uncertainties in adsorbed gas densities used in constructing absolute isotherms. In this paper, we revisit the Brunauer, Emmett, and Teller (BET) model, analytically simplify Ono-Kondo (OK) models for subsurface shales, and compare commonly used mono- and multilayer adsorption models (e.g., Langmuir, supercritical Dubinin-Radushkevich (SDR), supercritical BET (SBET), and simplified OK (OKs) models) with recently proposed pressure-dependent adsorption densities to develop a practical and reliable methodology that can be used in the supercritical state, typical for subsurface black shale conditions.Three independent data sets were used for nitrogen and methane adsorption isotherms at different temperatures. Furthermore, we demonstrate that adsorption predicted by the SDR model is comparable or lower than that predicted by the SBET model, but higher than the amounts predicted by Langmuir and OK models. The nitrogen BET method tends to underestimate the accessible SSA for methane. Measurement of isosteric heat of adsorption is suggested instead of the experimental fitting method, due to the significant difference between the calculated results by two commonly used methods. Experimental fitting and simulation methods are also briefly reviewed to guide future research on shale gas adsorption.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature

Porous carbon (PC) has been widely regarded as one of the most promising absorbents for methane storage. Studies show that its uptake capacity and selectivity highly depend on textural structures. Although much effort has been made, unveiling their detailed structure-performance relationship remains a challenge. Here, we propose an innovative study where, with the assistance of machine learning, the hidden relationship of the textural structures of PC with the methane uptake and separation can be derived from existing data in material literature. Machine learning models were trained by the data, including specific surface area, micropore volume, mesopore volume, temperature, and pressure as the input variables and methane uptake as the output variable for prediction. Among the tested models, the multilayer perceptron (MLP) shows the highest accuracy in predicting the methane uptake. In addition, the model enables to automatically construct a uptake performance map in terms of micropore volume and mesopore volume. The obtained MLP model was also extended to explore the CO 2 /CH 4 selectivity by retraining it with the data collected from literature of PC for the CO 2 uptake. Finally, the constructed 2D selectivity map shows that the high selectivity can be achieved in the low CH 4 uptake region.

42 ENGINEERING↗
DOE OSTI2025

Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials

Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single build to create a composite with tunable mechanical properties. Here, this work presents an integrated experimental and computational investigation into the composition-dependent mechanical behavior of 3D printed digital materials. We experimentally characterize five formulations, combining soft and rigid UV-cured polymers under uniaxial tension and torsion across three strain and twist rates. The results reveal nonlinear and rate-dependent responses that strongly depend on composition. To model this behavior, we develop a physics-augmented neural network (PANN) that combines a partially input convex neural network (pICNN) for learning the composition-dependent hyperelastic strain energy function with a quasi-linear viscoelastic (QLV) formulation for time-dependent response. The pICNN ensures convexity with respect to strain invariants while allowing non-convex dependence on composition. To enhance interpretability, we apply $L_0$ sparsification. For the time-dependent response, we introduce a multilayer perceptron (MLP) to predict viscoelastic relaxation parameters from composition. The proposed model accurately captures the nonlinear, rate-dependent behavior of 3D printed digital materials in both uniaxial tension and torsion, achieving high predictive accuracy for interpolated material compositions. This approach provides a scalable framework for automated, composition-aware constitutive model discovery for multi-material 3D printing.

Constitutive modeling↗
DOE OSTI2019

Near-field radiative heat transfer between rough surfaces modeled using effective media with gradient distribution of dielectric function

Near-field radiative heat transfer (NFRHT) between rough surfaces, due to its widespread presence in engineering practice of near-field energy utilization, requires indepth studies, especially from the perspective of physical mechanism. In this paper, an effective multilayer model is built to approach the NFRHT between random rough surfaces of silicon carbide (SiC). Using the effective medium theory (EMT), the effective dielectric function of each layer is obtained, which forms a gradient distribution of dielectric function along the depth of the medium. The influence of the effective dielectric function on surface phonon polaritons (SPhPs) is analyzed, showing that the effective layers with small filling fraction of SiC feature lower SPhP resonance frequencies than SiC bulk. The coupling of SPhPs from the gradient distribution of dielectric function produces new surface modes that dominates the NFRHT. Investigation on the effect of root mean square height (RMS height, σ) reveals that the peaks of local density of states (LDOS) and spectral heat flux are red-shifted as σ increases, while the spectral heat flux below the peak frequency gets larger. This can be attributed to the coupling of SPhPs inside the rough layer. We also found the total net heat flux between rough surfaces separated by an average distance exceeds that between smooth plates and increases with increasing σ, which offer a new way to enhance NFRHT. Finally, this work provides a reference for the simulation and understanding of the NFRHT between rough surfaces.

42 ENGINEERING↗
DOE OSTI2021

Discrete Green’s functions and spectral graph theory for computationally efficient thermal modeling

Here, this work concerns solutions of the heat equation with the spectral graph method, for which the temperature is defined at discrete points in the domain and the spatial relationship among the points is described by a graph. The heat equation on the graph is solved using matrix techniques involving the eigenvectors and eigenvalues of the Laplacian matrix. The spectral graph approach precludes the computationally intensive meshing and numerous time-integration steps of the finite element method. In the present work, the spectral graph method is extended to include heat loss at the boundaries with a generalized boundary condition, and physics-based edge weights are introduced which simplify the calibration process. From this approach a discrete Green’s function is defined which allows for solutions under a variety of heating conditions including: space-varying initial conditions; time-and-space varying internal heating; and, time-and-space-varying heating at boundaries of type 1 (Dirichlet), type 2 (Neumann) and type 3 (Robin). Results are provided for benchmark heat transfer problems in one spatial dimension and in three spatial dimensions, and verification is provided by comparison with exact analytical solutions and finite difference solutions. The spectral graph method converges within 0.4% error of the analytical solution. The practical utility of the approach is demonstrated by thermal simulation of a multilayer additive manufacturing process. The spectral graph results are compared to experimentally-obtained temperature data for two metal parts, with error less than 5% of the experimental measurements, with computation time less than one minute on a desktop computer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effective thermal conductivity of insulation materials for cryogenic LH 2 storage tanks: A review

An accurate estimation of the effective thermal conductivity of various insulation materials is essential in the evaluation of heat leak and boil-off rate from liquid hydrogen storage tanks. In this work, we review the existing experimental data and various proposed correlations for predicting the effective conductivity of insulation systems consisting of powders, foams, fibrous materials, and multilayer systems. We also propose a first principles-based correlation that may be used to estimate the dependence of the effective conductivity as a function of temperature, interstitial gas composition, pressure, and structural properties of the material. We validate the proposed correlation using available experimental data for some common insulation materials. Further improvements and testing of the proposed correlation using laboratory scale data obtained using potential LH 2 tank insulation materials are also discussed.

08 HYDROGEN↗
DOE OSTI2025

Foam-insulated cryo-compressed hydrogen storage vessels for buses

Here, large reductions (40 % vs. compressed gaseous hydrogen vessels and 17 % vs. vacuum insulated cryo-compressed vessels) in hydrogen storage system cost are possible by operating buses with foam-insulated cryo-compressed hydrogen (CcH 2 ) vessels. Detailed thermodynamic analysis reveals that large size and rapid H 2 consumption in buses compensate for the poor performance of foam insulation (∼100 times more conductive than vacuum multilayer insulation), resulting in compact and inexpensive H 2 storage systems with sufficient H 2 capacity for the longest routes (500 km) while maintaining previously identified advantages of CcH 2 systems: increased autonomy, rapid high density refueling, and improved safety, while simultaneously avoiding vacuum insulation stability problems resulting from composite resin outgassing.

Cryo-compressed↗
DOE OSTI2023

Performance analysis of functionally graded multifunctional piezoelectric energy harvesting microgyroscopes

Functionally graded materials (FGMs) are composite materials with varying material properties in one or more directions. These materials possess distinct characteristics compared to their constituent components. The ability to control material distribution and compositions in FGMs offers improved constraints over the natural frequencies of the system, making them highly suitable for energy harvesting applications. In this study, we focus on a piezoelectric FGM composed of Platinum (Pt) and Lead Titanate Zirconate (PZT). By utilizing FGMs for energy harvesting, we simplify the system from a multilayer structure to a single-layer system, thereby increasing power density by reducing volume. In this work, a power law distribution is used to model the material variation throughout the thickness of the single-layered beam. The governing equations of motion and boundary conditions for FG energy harvesting microgyroscope are derived using Euler-Bernoulli beam theory, the constitutive piezoelectric principle, and the extended Hamilton's principle. To simulate the nonlinear motion of the FG energy harvester, we employ the differential quadrature method (DQM). Subsequently, an investigation is carried out to examine the effects of various factors including material distribution, platinum percentage, base rotation, DC voltage, and electrical load resistance on the energy harvesting microgyroscope with functionally graded materials. Here, the findings suggest that functionally graded energy harvesting gyroscopes can be adjusted to obtain effective energy harvesting and sensing capabilities by carefully selecting the material distribution, input voltage, and electrical load resistance.

42 ENGINEERING↗
DOE OSTI2022

Exploring drought-responsive crucial genes in Sorghum

Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.

60 APPLIED LIFE SCIENCES↗