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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid

In this study, several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

High temperature design methods rely on constitutive models for inelastic deformation and failure typically calibrated against the mean of experimental data without considering the associated scatter. Variability may arise from the experimental data acquisition process, from heat-to-heat material property variations, or both and need to be accurately captured to predict parameter bounds leading to efficient component design. Applying the Bayesian Markov Chain Monte Carlo (MCMC) method to produce statistical models capturing the underlying uncertainty in the experimental data is an area of ongoing research interest. This work varies aspects of the Bayesian MCMC method and explores their effect on the posterior parameter distributions for a uniaxial elasto-viscoplastic damage model using synthetically generated reference data. From our analysis with the uniaxial inelastic model we determine that an informed prior distribution including different types of test conditions results in more accurate posterior parameter distributions. The parameter posterior distributions, however, do not improve when increasing the number of similar experimental data. Additionally, changing the amount of scatter in the data affects the quality of the posterior distributions, especially for the less sensitive model parameters. Moreover, we perform a sensitivity study of the model parameters against the likelihood function prior to the Bayesian analysis. The results of the sensitivity analysis help to determine the reliability of the posterior distributions and reduce the dimensionality of the problem by fixing the insensitive parameters. The comprehensive study described in this work demonstrates how to efficiently apply the Bayesian MCMC methodology to capture parameter uncertainties in high temperature inelastic material models. Quantifying these uncertainties in inelastic models will improve high temperature engineering design practices and lead to safer, more effective component designs.

42 ENGINEERING↗
DOE OSTI2021

Opacity Table Generation

Radiation opacity is a key property of material systems which describes the absorption and scattering of photons. Such processes are an important part of energy transport, diagnostics, and astrophysical phenomena. Recent experiments in iron opacity and continuum lowering have sparked renewed interest in and careful vetting of opacity models. Even within the local thermodynamic equilibrium approximation, opacity varies over a large parameter space: material, temperature, density, and energy. Opacity tables that span wide regimes of this parameter space are of prime importance to radiation-hydrodynamics codes. Accurate opacity calculations cannot be achieved inline during a radiation hydrodynamics simulation since they involve accounting for all possible electron transitions of all important atomic states of a thermal ensemble. Livermore’s current framework utilizes precalculated tables at many densities, temperatures, and energies. Last year, the opacity theory team completed an L2 milestone concerning the development of a new code, Opus, which served the dual purpose of creating a modern code infrastructure and version control as well as the basis for training new members of that team, a critical need as key members near or are passed retirement. For the current L2 milestone, we exercised and validated Opus on three elements: boron, carbon, and nitrogen, which have few enough electrons to allow a careful convergence study over many input parameters. Here, we report on the production of new tables, show their convergence with respect to important parameters, state some of their limitations, and document a mostly automated production process. In this section, we address the milestone completion criteria, found on the title page and the subsection headings. In section 2, we validate Opus against Tycho and compare with TabOp and Atomic. Section 3 details some of the physics implemented in Opus and relevant to this report. We then cover details of the density-temperature grid in section 4 and do a spectral comparison between different parameter settings and different codes in section 5. After summarizing these results in section 6, we give instructions how to run the opacity table scripts in appendix A and document the scripts and input/output files in appendix B.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Soliton Generation in Negative Thermal Expansion Materials

Strain solitons have been observed statically in several 2D materials and dynamically in substrate materials using ultrafast laser pulses. The latter case relies on lattice relaxation in response to ultrafast heating in a light-absorbing transducer material, a process which is sensitive to the thermal expansion coefficient. Here we consider an unusual case where the sign of the thermal expansion coefficient is negative, a scenario which is experimentally feasible in light of rapid and recent advances in the discovery of negative thermal expansion materials. We present numerical solutions to a nonlinear differential equation which has been repeatedly demonstrated to quantitatively model experimental data and discuss the salient results using realistic parameters for material linear and nonlinear elasticity. The solitons that emerge from the initial value problem with negative and positive thermal expansion are qualitatively different in several ways. The new case of negative thermal expansion gives rise to a nearly-periodic soliton train with chirped profile and free of an isolated shock front. We suggest this unanticipated result may be realized experimentally and assess the potential for certain applications of this generic effect.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of tank designs for hydrogen storage on heavy duty vehicles using metal hydrides

The objective of this project was to evaluate material-based hydrogen storage solutions as a replacement for high-pressure hydrogen gas or liquid hydrogen on Class 7 or 8 tractor fuel cell electric vehicles. The project focused on low-density main-group hydrides, a well-known class of materials for hydrogen storage. Prior research has considered metal amides as storage materials for light-duty vehicles but not for heavy-duty applications. The project established the basis for further development of storage systems of this type for heavy duty vehicles (HDV). Systems analysis of an HDV storage system comprised of a tank and associated balance of plant (piping, coolant tubes, burner) was performed to determine the usable hydrogen capacity. A composite storage material comprised of a metal hydride mixed with a high thermal-conductivity carbon is predicted to have a usable hydrogen volumetric capacity comparable to or exceeding that of 700 bar pressurized hydrogen gas. The gravimetric capacity of this material is also predicted to be competitive with pressurized gas, particularly if costly carbon fiber composite Type III or Type IV tanks are excluded. The storage system design parameters and material properties served as inputs to a second model that simulates fuel cell operation in conjunction with the storage system during an HDV drive cycle. The results show that sufficient hydrogen pressure can be produced to operate a Class 8 HDV, yielding a range of ~480 miles. These results are particularly relevant for high-impact regions, such as the South Coast Air Quality Management District, for which an economical vehicular hydrogen storage system with minimal impact on cargo capacity could accelerate adoption of heavy-duty fuel cell electric vehicles. An additional benefit is that knowledge generated by this project can assist in development of material-based storage for stationary applications such as microgrids and backup power for data centers.

08 HYDROGEN↗
DOE OSTI2023

TRINIDI (Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inference)

This software is an open-source Python library that provides tools for processing hyperspectral neutron time-of-flight radiography data. This type of data allows material decomposed reconstructions to be generated with the use of material characteristic spectral responses and the algorithms provided in this code library. The software library will contain tools for pre-processing the neutron measurement data, estimating measurement system parameters, reconstructing material decomposed radiographs, and computing material decomposed computed tomography (CT). Furthermore, it will have capability to generate and process simulated neutron time-of-flight data with the goal of benchmarking and demonstrating the tools that are provided. The software will include thorough documentation and application examples.

Balke, Thilo↗
DOE OSTI2020

SNL will be able to provide well-calibrated models to weapons systems analysts by integrating an engine for constitutive model calibration with the GRANTA materials database

Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing Spin‐Crossover Systems to Enhance Thermopower and Thermoelectric Figure‐of‐Merit in Paramagnetic Materials

Thermoelectric materials, capable of converting temperature gradients into electrical power, have been traditionally limited by a trade‐off between thermopower and electrical conductivity. This study introduces a novel, broadly applicable approach that enhances both the spin‐driven thermopower and the thermoelectric figure‐of‐merit (zT) without compromising electrical conductivity, using temperature‐driven spin crossover. Our approach, supported by both theoretical and experimental evidence, is demonstrated through a case study of chromium doped‐manganese telluride, but is not confined to this material and can be extended to other magnetic materials. By introducing dopants to create a high crystal field and exploiting the entropy changes associated with temperature‐driven spin crossover, we achieved a significant increase in thermopower, by approximately 136 μV K −1 , representing more than a 200% enhancement at elevated temperatures within the paramagnetic domain. Our exploration of the bipolar semiconducting nature of these materials reveals that suppressing bipolar magnon/paramagnon‐drag thermopower is key to understanding and utilizing spin crossover‐driven thermopower. These findings, validated by inelastic neutron scattering, X‐ray photoemission spectroscopy, thermal transport, and energy conversion measurements, shed light on crucial material design parameters. We provide a comprehensive framework that analyzes the interplay between spin entropy, hopping transport, and magnon/paramagnon lifetimes, paving the way for the development of high‐performance spin‐driven thermoelectric materials.

magnons↗
DOE OSTI2020

Wide Band Gap Chalcogenide Semiconductors

Wide band gap semiconductors are essential for today’s electronic devices and energy applications because of their high optical transparency, controllable carrier concentration, and tunable electrical conductivity. The most intensively investigated wide band gap semiconductors are transparent conductive oxides (TCOs), such as tin-doped indium oxide (ITO) and amorphous In–Ga–Zn–O (IGZO), used in displays and solar cells, carbides (e.g., SiC) and nitrides (e.g., GaN) used in power electronics, and emerging halides (e.g., γ-CuI) and 2D electronic materials (e.g., graphene) used in various optoelectronic devices. Compared to these prominent materials families, chalcogen-based (Ch = S, Se, Te) wide band gap semiconductors are less heavily investigated but stand out because of their propensity for p-type doping, high mobilities, high valence band positions (i.e., low ionization potentials), and broad applications in electronic devices such as CdTe solar cells. This manuscript provides a review of wide band gap chalcogenide semiconductors. First, we outline general materials design parameters of high performing transparent semiconductors, as well as the theoretical and experimental underpinnings of the corresponding research methods. We proceed to summarize progress in wide band gap (EG > 2 eV) chalcogenide materials—namely, II–VI MCh binaries, CuMCh 2 chalcopyrites, Cu 3 MCh 4 sulvanites, mixed-anion layered CuMCh(O,F), and 2D materials—and discuss computational predictions of potential new candidates in this family, highlighting their optical and electrical properties. Then, we finally review applications—for example, photovoltaic and photoelectrochemical solar cells, transistors, and light emitting diodes—that employ wide band gap chalcogenides as either an active or passive layer. Overall, by examining, categorizing, and discussing prospective directions in wide band gap chalcogenides, this Review aims to inspire continued research on this emerging class of transparent semiconductors and thereby enable future innovations for optoelectronic devices.

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

Neutron diffraction and DFT studies of oxygen defect and transport in higher-order Ruddlesden–Popper phase materials

A series of higher-order Ruddlesden–Popper phase materials – La 3 PrNi 3 O 10-δ , La 2 Pr 2 Ni 3 O 10-δ and LaPr 3 Ni 3 O 10-δ – were synthesised and investigated by neutron powder diffraction to understand the oxygen defect structure and propose possible pathways for oxygen transport in these materials. Further complimentary DFT calculations of the materials were performed to support the experimental analysis. All of the phases were hypostoichiometric and it was observed that the majority of the oxygen vacancies were confined to the perovskite layers, with a preference for equatorial oxygen sites. A particular preference for vacancies in O(1) and O(5) sites at high temperatures was observed from neutron diffraction measurements which were further complimented by DFT calculations wherein the vacancy formation energy was found to be lowest at the O(1) site. Also, a preference for a curved oxygen transport pathway around the NiO 6 octahedra was observed which agrees with the published literature for Ruddlesden–Popper phase materials. Lattice parameters for all three compositions showed a linear increase with increasing temperature, but the increase was greatest in the c parameter while the b parameter showed only a slight increase when compared to the a parameter. The thermal expansion coefficient was calculated for all compositions and was found to be in the range 13.0–13.4 × 10 -6 °C -1 , which is compatible with the commonly used electrolyte materials for solid oxide fuel cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Comprehensive uncertainty quantification (UQ) for full engineering models by solving probability density function (PDF) equation

This report details a new method for propagating parameter uncertainty (forward uncertainty quantification) in partial differential equations (PDE) based computational mechanics applications. The method provides full-field quantities of interest by solving for the joint probability density function (PDF) equations which are implied by the PDEs with uncertain parameters. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known apriori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the driving (dynamics) term of the PDEs based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the dynamics term of the governing PDEs. The efficacy of the method is demonstrated by applying it to two target problems: solid mechanics explicit dynamics with uncertain material model parameters, and reacting hypersonic fluid mechanics with uncertain chemical kinetic rate parameters. A minimally invasive implementation of the method for representative codes SPARC (reacting hypersonics) and NimbleSM (finite- element solid mechanics) and associated software details are described. For solid mechanics demonstration problems the method shows order of magnitudes improvement in accuracy over traditional stochastic collocation. For the reacting hypersonics problem, the method is implemented as a streamline integration and results show very good accuracy for the approximate sample solutions of re-entry flow past the Apollo capsule geometry at Mach 30.

42 ENGINEERING↗
DOE OSTI2024

Performance of cross-flow turbines with varying blade materials and unsupported blade span

Cross-flow turbines could play a larger role in the diversification of the global energy supply if the impact of more cost-competitive design choices on performance and rotor dynamics was better understood. This study focuses on rotor performance and blade strain measurements while varying the following parameters: blade materials and blade free end length by changing strut support position. Towing tank experiments were performed with a modular 1-meter diameter cross-flow turbine consisting of three NACA 0018 blades with two support struts. One strut was fixed at the lower end of the turbine, while the second strut was adjustable, thereby changing the length of the free end. The blade materials tested were carbon, E-glass, and hollow E-glass fiber composites, in decreasing order of stiffness and cost. High-resolution distributed fiber optic sensors were embedded in two of the three rotor blades for each material and provided hundreds of strain measurements per blade. Turbine performance and blade strain were measured while varying tow speed and tip speed ratio. Performance tests were conducted at towing speeds sufficiently high for the performance to be independent of Reynolds number. E-glass blades and carbon blades performed similarly for the most rigid strut configurations. Higher strain was measured on the E-glass blades, and their performance was reduced for less rigid configurations compared to the carbon fiber blades. The performance of the highly deflective hollow E-glass blades was lower overall and became even more degraded for longer unsupported blade span. Furthermore, the results provide insight into the use of various blade materials in cross-flow turbines and guidance on allowable free end length for each material type.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Topological valley Hall polariton condensation

A photonic topological insulator features robust directional propagation and immunity to defect perturbations of the edge/surface state. Exciton-polaritons, that is, the hybrid quasiparticles of excitons and photons in semiconductor microcavities, have been proposed as a tunable nonlinear platform for emulating topological phenomena. However, mainly due to excitonic material limitations, experimental observations so far have not been able to enter the nonlinear condensation regime or only show localized condensation in one dimension. Here, in this study, we show a topological propagating edge state with polariton condensation at room temperature and without any external magnetic field. We overcome material limitations by using excitonic CsPbCl 3 halide perovskites with a valley Hall lattice design. The polariton lattice features a large bandgap of 18.8 meV and exhibits strong nonlinear polariton condensation with clear long-range spatial coherence across the critical pumping density. The geometric parameters and material composition of our nonlinear many-body photonic system platform can in principle be tailored to study topological phenomena of other interquasiparticle interactions. Polariton condensation of topological propagating edge states is demonstrated with halide perovskite microcavities.

42 ENGINEERING↗
DOE OSTI2022

Comprehensive view of microscopic interactions between DNA-coated colloids

Abstract The self-assembly of DNA-coated colloids into highly-ordered structures offers great promise for advanced optical materials. However, control of disorder, defects, melting, and crystal growth is hindered by the lack of a microscopic understanding of DNA-mediated colloidal interactions. Here we use total internal reflection microscopy to measure in situ the interaction potential between DNA-coated colloids with nanometer resolution and the macroscopic melting behavior. The range and strength of the interaction are measured and linked to key material design parameters, including DNA sequence, polymer length, grafting density, and complementary fraction. We present a first-principles model that screens and combines existing theories into one coherent framework and quantitatively reproduces our experimental data without fitting parameters over a wide range of DNA ligand designs. Our theory identifies a subtle competition between DNA binding and steric repulsion and accurately predicts adhesion and melting at a molecular level. Combining experimental and theoretical results, our work provides a quantitative and predictive approach for guiding material design with DNA-nanotechnology and can be further extended to a diversity of colloidal and biological systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Session 4: ASME Code Rules

General review of code rules What do they cover. What they don’t. Underlying assumptions Probability of failure Material property parameters Stresses and loads Degradation What is NonmetallicsWork Group (NWG) doing? Failure in components Redefining failure Degradation rules Oxidation degradation Irradiation degradation Molten Salt Issues Abrasion/Erosion

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗