Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “materials simulations”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗
DOE OSTI2021

Thermal gradient effect on helium and self-interstitial transport in tungsten

First-wall materials in a fusion reactor are expected to withstand harsh conditions, with high heat and particle fluxes that modify the materials microstructure. These fluxes will create strong gradients of temperature and concentration of diverse species. Besides the He ash and the hydrogenic species, neutron particles generated in the fusion reaction will collide with the material creating intrinsic defects, such as vacancies, self-interstitials atoms (SIAs), and clusters of such point defects. These defects and the He atoms will then migrate in the presence of the aforementioned gradients. In this study, we use nonequilibrium molecular dynamics to analyze the transport of He and SIAs in the presence of a thermal gradient in tungsten. We observe that, in all cases, the defects and impurity atoms tend to migrate toward the hot regions of the tungsten sample. The resulting species concentration profiles are exponential distributions, rising toward the hot regions of the sample, in agreement with irreversible thermodynamics analysis. For both He atoms and SIAs, we find that the resulting species flux is directed opposite to the heat flux, indicating that species transport is governed by a Soret effect (thermal-gradient-driven diffusion) characterized by a negative heat of transport that drives species diffusion uphill (from the cooler to the hot regions of the sample). Here, we demonstrate that the steady-state species profiles obtained accounting for the Soret effect vary significantly from those where temperature-gradient-driven transport is not considered and discuss the implications of such a Soret effect on the response to plasma exposure of plasma-facing tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An IIPG-based finite element framework in MOOSE for modeling fiber reinforced composite failure governed by extrinsic cohesive laws

Fiber reinforced composites (FRC) are widely applied to many important engineering structures such as silicon carbide ceramic matrix composite (SiC CMC) of nuclear fuel claddings. Many material and structure failures are initiated at the interface between two distinctive materials, such as the debonding of interfaces of matrix/fiber of composites or bulk matrix/inclusion in metals at micro-scales. Accurate and robust modeling of these interface failures is critical to the development of novel material and structure with improved functionality. This paper presents a discontinuous Galerkin (DG) finite element method based on the Incomplete Interior Penalty scheme and its implementation in MOOSE for modeling material interface behaviors. The proposed method naturally accommodates important extrinsic cohesive zone material models and is capable of modeling the failures of material interfaces across which material properties are highly distinctive. We demonstrate a good performance of the proposed method using single fiber and multiple fiber debonding problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Nanoparticle self-assemblies with modern complexity

Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemistry-mediated Ostwald ripening in carbon-rich C/O systems at extreme conditions

Abstract There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.

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

Conceptual Design and Analysis of In-Vessel Components for the Materials Plasma Exposure eXperiment (MPEX)

The materials plasma exposure experiment (MPEX) is a linear plasma divertor simulator currently undergoing conceptual design. The facility will expose material samples to steady-state plasma fluxes to examine plasma–material interactions (PMIs) that are expected in the next generation of fusion devices. The plasmas will be generated by a helicon source, with electron and ion heating sources of up to 800 kW possible. The peak heat fluxes of the target are expected to be up to 10 MW/m 2 . The facility will be capable of handling low-activation neutron-irradiated samples in order to examine the multivariate effects of neutron damage and plasma fluence. Neutron-irradiated samples are planned to be roughly of 10-mm diameter; however, plasma-facing components up to 60×600 mm can be accommodated. The steady-state nature of the device will require the magnetic confinement of the plasma to be achieved with superconducting magnets, with a maximum on-axis field of 2.5 T. In addition, since MPEX will be a steady-state device, in-vessel components need to be water cooled. The primary in-vessel components will be the target, the dump plate, the limiter, the skimmers, and the microwave absorber. The conceptual design of these components is presented here, including analyses that confirm that the designs are adequate to meet the requirements of MPEX operation.

Lumsdaine, Arnold↗
DOE OSTI2026

Dynamic compression and spallation behavior of NbTaTiVZr high-entropy alloy

High-entropy alloys are a new class of materials with promising properties for aerospace and defense applications. The unique behavior of these materials is driven by the complex interactions between dissimilar atoms in a crystal and requires combined theoretical and experimental efforts to unlock their full potential. Here, we evaluate the relationship between the microstructure and the dynamic response of the equiatomic NbTaTiVZr alloy. Specifically, the shock Hugoniot, or equation of state, was measured up to a particle velocity of 0.5 mm μs −1 using gas gun plate impacts. Shock wave profile and incipient spallation experiments were used to characterize wave propagation and damage formation with post-mortem recovery experiments. Molecular dynamics simulations confirm the experimental findings and extend them up to 2.1 mm μs −1 particle velocity. Computational thermodynamic calculation of phase diagram simulations explain details of the material microstructure, which explains the measured strength and experimental damage patterns. Overall, this work provides detailed high-strain-rate characterization of a refractory high-entropy alloy, and more importantly, a framework and demonstration of the utility and necessity of a combined theoretical and experimental approach, outlining the importance of considering processing and manufacturing conditions when evaluating the performance of new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Barriers to carrier transport in multiple quantum well nitride-based c -plane green light emitting diodes

The presence of alloy disorder in III-nitride materials has been demonstrated to play a significant role in device performance through effects such as carrier localization and carrier transport. Relative to blue light emitting diodes (LEDs), these effects become more severe at green wavelengths. Because of the potential fluctuations that arise due to alloy disorder, full three-dimensional (3D) simulations are necessary to accurately relate materials properties to device performance. We demonstrate experimentally and through simulation that increased quantum well (QW) number in c-plane green LEDs contributes to excess driving voltage, and therefore reduced electrical efficiency. Experimentally, we grew an LED series with the number of QWs varying from one to seven and observed a systematic increase in voltage with the addition of each QW. Trends in LED electrical properties obtained from 3D simulations, which account for the effects of random alloy fluctuations, are in agreement with experimental data. Agreement is achieved without the need for adjusting polarization parameters from their known values. Finally, from these results, we propose that the polarization induced barriers at the GaN/InGaN (lower barrier/QW) interfaces and the sequential filling of QWs both contribute significantly to the excess forward voltage in multiple QW c-plane green LEDs.

42 ENGINEERING↗
DOE OSTI2023

Heat pump water heater enhanced with phase change materials thermal energy storage: Modeling study

A promising solution to improve the first hour rating (FHR) of a heat pump water heater (HPWH) involves employing a secondary tank which contains phase change material (PCM) capsules. To better understand the influence of PCM thermal storage on the HPWH operational performance, a dynamic model was developed to simulate and analyze the behavior of a newly developed HPWH technology that incorporates PCM storage into a standard HPWH to optimize key parameters such as the uniform energy factor and FHR. Mathematical models of several key components of the proposed HPWH-PCM integrated thermal energy storage (TES) system, e.g., water heater tank, PCM TES tank, evaporator, compressor, and expansion valve, have been elaborated. Also, a model-based control co-simulation platform was developed to integrate a embed PCM storage HPWH dynamic model with a control model for better supporting control design, analysis, verification, and validation. The model accuracy has been validated through comparing simulation results with lab test results, with a mean average percentage error of <5.5% for most of the selected performance variables. In addition, using the developed co-simulation platform, the demand response control strategy was studied to evaluate the load flexibility of the combined HPWH-PCM storage system by shifting system power usage to outside of the 3.5 h peak load period.

25 ENERGY STORAGE↗
DOE OSTI2023

Application of Machine Learning to Assist a Moisture Durability Tool

The design of moisture-durable building enclosures is complicated by the number of materials, exposure conditions, and performance requirements. Hygrothermal simulations are used to assess moisture durability, but these require in-depth knowledge to be properly implemented. Machine learning (ML) offers the opportunity to simplify the design process by eliminating the need to carry out hygrothermal simulations. ML was used to assess the moisture durability of a building enclosure design and simplify the design process. This work used ML to predict the mold index and maximum moisture content of layers in typical residential wall constructions. Results show that ML, within the constraints of the construction, including exposure conditions, does an excellent job in predicting performance compared to hygrothermal simulations with a coefficient of determination, R2, over 0.90. Furthermore, the results indicate that the material properties of the vapor barrier and continuous insulation layer are strongly correlated to performance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide ab initio semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Using molecular dynamics simulations to validate a new approach for determining the melting curves of materials

The Los Alamos National Laboratory (LANL), located in the state of New Mexico (United States), is one of the most iconic research centers in the world. Founded in 1943 as part of the Manhattan Project, it emerged from a global conflict and an unprecedented scientific emergency. At that time, the United States feared that Nazi Germany might develop an atomic weapon first. Under the direction of physicist J. Robert Oppenheimer, the U.S. government established a secret laboratory in an isolated region of the Los Alamos plateau, bringing together some of the greatest scientific minds of the era. This site, then known as Project Y, became the birthplace of the first atomic bomb.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Innovative Nuclear Materials Outboard-A Project Specimen Preparation Guide

The Innovative Nuclear Materials (INM) Program was recently established by the U.S. Department of Energy (DOE) to develop advanced material technologies for use in nuclear reactors. The INM program is presently focused on researching in-core non-fueled materials for application in fast spectrum nuclear reactors. The widespread deployment of fast reactors continues to be a prominent aspiration for advanced nuclear technology developers. However, companies working to license these reactors have no choice but to rely on historic material technologies since further optimization and advancement of these materials is impeded by the lack of fast neutron irradiation test facilities. INM-OA is a non-fueled drop-in experiment which will irradiate material specimens of interest to fast reactor applications. This experiment will be irradiated at Idaho National Laboratory (INL) in the Advanced Test Reactor (ATR) outboard-A (OA) position during normal and high temperature steady state (HTSS) cycles. It will include material specimens supplied by members of the INM program and will utilize a cadmium-lined basket to filter out incident thermal neutrons, thus simulating a faster neutron energy spectrum. Material specimens will undergo post-irradiation examination including microscopy and mechanical testing. In addition to absorption reactions, fast neutrons cause microstructural damage in materials by atom displacement, which can cause exacerbated changes in physical properties and behavior. Thus, the data obtained from the INM-OA experiment will be crucial for understanding the engineering-scale behavior of reactor materials. This document is intended for the Principal Investigators providing samples for this project. Topics included are a general description of the experiment, the irradiation experiment/capsule design, sample geometries, number of samples to be provided, documentation to be provided, a brief list potentially useful characterization methods that can be leveraged at INL, and other miscellaneous requirements specific to this project. This document is intended for informational use only.

innovative nuclear materials↗
DOE OSTI2022

Development of the MCNP-ORIGEN activation automation tool

This paper introduces the MCNP-ORIGEN activation automation tool for streamlining the calculation of experiment source terms. This tool couples the Monte-Carlo radiation-transport solver, MCNP to the depletion tool, ORIGEN-S. To showcase its current capabilities, this paper presents an activation analysis exercise, which is conducted with Serpent, and the results are com- pared. The experiment consists in a 90%-enriched uranium sphere surrounded by light water. A non-fissile cylinder is placed in the water, representing an irradiation experiment. We conducted simulations for 2 different cylinder materials: iron and cobalt at two different temperatures: 300 K and 900 K. The exercise consists of an irradiation of 50 days at a constant power of 5 MW, followed by a decay of 50 days. These studies highlight the discrepancies between how the different tools handle nuclear data. Finally, the results underscore multiple areas of possible improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

36 MATERIALS SCIENCE↗