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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Automated Finite Element Analysis of Elastically-Tailored Plates

A procedure for analyzing and designing elastically tailored composite laminates using the STAGS finite element solver has been presented. The methodology used to produce the elastic tailoring, namely computer-controlled steering of unidirectionally reinforced composite material tows, has been reduced to a handful of design parameters along with a selection of construction methods. The generality of the tow-steered ply definition provides the user a wide variety of options for laminate design, which can be automatically incorporated with any finite element model that is composed of STAGS shell elements. Furthermore, the variable stiffness parameterization is formulated so that manufacturability can be assessed during the design process, plus new ideas using tow steering concepts can be easily integrated within the general framework of the elastic tailoring definitions. Details for the necessary implementation of the tow-steering definitions within the STAGS hierarchy is provided, and the format of the ply definitions is discussed in detail to provide easy access to the elastic tailoring choices. Integration of the automated STAGS solver with laminate design software has been demonstrated, so that the large design space generated by the tow-steering options can be traversed effectively. Several design problems are presented which confirm the usefulness of the design tool as well as further establish the potential of tow-steered plies for laminate design.

Jegley, Dawn C.↗
DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗
DOE OSTI2023

BitGNN: Unlocking the Performance Potential of Binary Graph Neural Networks on GPUs

Graph Neural Networks (GNNs) have shown compelling results in many graph-based learning tasks. They are, however, time-consuming. Recent work has shown a promising direction in improving GNN speed and shrinking the size — network binarization, which binarizes network values and operations. Prior work, however, mainly focused on algorithm designs, leaving it open on how to fully materialize the performance potential. This work fills the gap by proposing techniques to best map binary GNNs and their computations to fit the nature of bit manipulations, optimizations and algorithms to maximize BSpMM kernel efficiency, and solutions to other factors influencing the end-to-end time on GPUs. Results on real-world graphs show that the proposed techniques outperform state of-the-art binary GNN implementations by 21-67× with little accuracy loss.

Chen, Jou-An↗
DOE OSTI2021

Accelerate Nuclear Research and Development by Reducing Time and Cost Spend in the Pre-conceptual Design Phase of Advanced Reactor Experiments

The design process of every new concept, such as advanced nuclear reactors or associated experiments, starts with the pre-conceptual design phase. In this phase, the viability of a wide range of design options needs to be assessed quickly, to understand the operating envelope and its feasibility. A variety of physics models (thermal-hydraulics, neutronics, mechanical design, etc.) has to be considered at this very first design stage and optimum component sizes and materials (e.g. heat exchangers, piping, turbomachinery, coolant type, etc.) have to be chosen for a given set of boundary conditions (e.g. heat source, heat sink, flow rate, etc.). Detailed solutions such as provided by high fidelity methods like computational fluid dynamics (CFD), Monte Carlo methods, etc. and even lower fidelity tools such as system or subchannel codes, etc. are usually not used during the pre-conceptual design due to the relatively long time needed to create input models, the computational time to obtain a solution and the lack of flexibility to quickly investigate different combinations of components, individual component sizes and material properties. High fidelity tools are usually only employed in the conceptual design and later phases once a base concept has been identified during the pre-conceptual design stage. The current practice during the pre-conceptual design stage is that analysts collect the needed equations, material properties, closure laws, etc. and create ad-hoc solutions form scratch for every new problem. There clearly is a lack of a flexible scoping tool that can be used during pre-conceptional design before higher fidelity tools (as described above) come into play. To reduce user errors in ad-hoc solutions and increase fidelity and efficiency, this project aims to investigate and develop a user-friendly scoping tool to address the thermal-hydraulic designing needs during preconceptual experiment design, i.e. Thermal-hydraulic Research Universal Scoping Tool (TRUST). The success of TRUST will provide the nuclear engineers with an easy-to-use and affordable calculator for early reactor system design and optimization.

42 ENGINEERING↗
DOE OSTI2024

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective

Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Computer-aided analysis and design of the shape rolling process for producing turbine engine airfoils

Mild steel (AISI 1018) was selected as model cold rolling material and Ti-6A1-4V and Inconel 718 were selected as typical hot rolling and cold rolling alloys, respectively. The flow stress and workability of these alloys were characterized and friction factor at the roll/workpiece interface was determined at their respective working conditions by conducting ring tests. Computer-aided mathematical models for predicting metal flow and stresses, and for simulating the shape rolling process were developed. These models utilized the upper bound and the slab methods of analysis, and were capable of predicting the lateral spread, roll separating force, roll torque, and local stresses, strains and strain rates. This computer-aided design system was also capable of simulating the actual rolling process, and thereby designing the roll pass schedule in rolling of an airfoil or a similar shape.

Lahoti, G. D.↗
NASA NTRS1995

Shielding materials for highly penetrating space radiations

Interplanetary travel involves the transfer from an Earth orbit to a solar orbit. Once outside the Earth's magnetosphere, the major sources of particulate radiation are solar cosmic rays (SCR's) and galactic cosmic rays (GCR's). Intense fluxes of SCR's come from solar flares and consist primarily of protons with energies up to 1 GeV. The GCR consists of a low flux of nuclei with energies up to 10(exp 10) GeV. About 70 percent of the GCR are protons, but a small amount (0.6 percent) are nuclei with atomic numbers greater than 10. High energy charged particles (HZE) interact with matter by transferring energy to atomic electrons in a Coulomb process and by reacting with an atomic nucleus. Energy transferred in the first process increases with the square of the atomic number, so particles with high atomic numbers would be expected to lose large amounts of energy by this process. Nuclear reactions produced by (HZE) particles produce high-energy secondary particles which in turn lose energy to the material. The HZE nuclei are a major concern for radiation protection of humans during interplanetary missions because of the very high specific ionization of both primary and secondary particles. Computer codes have been developed to calculate the deposition of energy by very energetic charged particles in various materials. Calculations show that there is a significant buildup of secondary particles from nuclear fragmentation and Coulomb dissociation processes. A large portion of these particles are neutrons. Since neutrons carry no charge, they only lose energy by collision or reaction with a nucleus. Neutrons with high energies transfer large amounts of energy by inelastic collisions with nuclei. However, as the neutron energy decreases, elastic collisions become much more effective for energy loss. The lighter the nucleus, the greater the fraction of the neutron's kinetic energy that can be lost in an elastic collision. Thus, hydrogen-containing materials such as polymers are most effective in reducing the energy of neutrons. Once neutrons are reduced to very low energies, the probability for undergoing a reaction with a nucleus (the cross section) becomes very high. The product of such a reaction is often radioactive and can involve the release of a significant amount of energy. Thus, it is important to provide protection from low energy neutrons during a long duration space flight. Among the light elements, lithium and boron each have an isotope with a large thermal neutron capture cross section, Li-6 and B-10. However, B-10 is more abundant in the naturally-occurring element than Li-6, has a thermal neutron capture cross section four times that of Li-6, and produces the stable products, He-4 and Li-7 in the interaction while Li-6 produces radioactive tritium (H-3). Thus, boron is the best light-weight material for thermal neutron absorption in spacecraft. The work on this project was focused in two areas: computer design where existing computer codes were used, and in some cases modified, to calculate the propagation and interactions of high energy charged particles through various media, and materials development where boron was incorporated into high performance materials.

Kiefer, Richard L.↗
DOE OSTI2020

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
NASA NTRS2008

Simulating the Gradually Deteriorating Performance of an RTG

Degra (now in version 3) is a computer program that simulates the performance of a radioisotope thermoelectric generator (RTG) over its lifetime. Degra is provided with a graphical user interface that is used to edit input parameters that describe the initial state of the RTG and the time-varying loads and environment to which it will be exposed. Performance is computed by modeling the flows of heat from the radioactive source and through the thermocouples, also allowing for losses, to determine the temperature drop across the thermocouples. This temperature drop is used to determine the open-circuit voltage, electrical resistance, and thermal conductance of the thermocouples. Output power can then be computed by relating the open-circuit voltage and the electrical resistance of the thermocouples to a specified time-varying load voltage. Degra accounts for the gradual deterioration of performance attributable primarily to decay of the radioactive source and secondarily to gradual deterioration of the thermoelectric material. To provide guidance to an RTG designer, given a minimum of input, Degra computes the dimensions, masses, and thermal conductances of important internal structures as well as the overall external dimensions and total mass.

Wood, Eric G.↗
NASA NTRS2001

Higher-Order Theory for Functionally Graded Materials

Functionally graded materials (FGM's) are a new generation of engineered materials wherein the microstructural details are spatially varied through nonuniform distribution of the reinforcement phase(s). Engineers accomplish this by using reinforcements with different properties, sizes, and shapes, as well as by interchanging the roles of the reinforcement and matrix phases in a continuous manner (ref. 1). The result is a microstructure that produces continuously or discretely changing thermal and mechanical properties at the macroscopic or continuum scale. This new concept of engineering the material's microstructure marks the beginning of a revolution both in the materials science and mechanics of materials areas since it allows one, for the first time, to fully integrate the material and structural considerations into the final design of structural components. Functionally graded materials are ideal candidates for applications involving severe thermal gradients, ranging from thermal structures in advanced aircraft and aerospace engines to computer circuit boards. Owing to the many variables that control the design of functionally graded microstructures, full exploitation of the FGM's potential requires the development of appropriate modeling strategies for their response to combined thermomechanical loads. Previously, most computational strategies for the response of FGM's did not explicitly couple the material's heterogeneous microstructure with the structural global analysis. Rather, local effective or macroscopic properties at a given point within the FGM were first obtained through homogenization based on a chosen micromechanics scheme and then subsequently used in a global thermomechanical analysis.

Aboudi, J.↗
NASA NTRS1999

Evaluation of Spacecraft Shielding Effectiveness for Radiation Protection

The potential for serious health risks from solar particle events (SPE) and galactic cosmic rays (GCR) is a critical issue in the NASA strategic plan for the Human Exploration and Development of Space (HEDS). The excess cost to protect against the GCR and SPE due to current uncertainties in radiation transmission properties and cancer biology could be exceedingly large based on the excess launch costs to shield against uncertainties. The development of advanced shielding concepts is an important risk mitigation area with the potential to significantly reduce risk below conventional mission designs. A key issue in spacecraft material selection is the understanding of nuclear reactions on the transmission properties of materials. High-energy nuclear particles undergo nuclear reactions in passing through materials and tissue altering their composition and producing new radiation types. Spacecraft and planetary habitat designers can utilize radiation transport codes to identify optimal materials for lowering exposures and to optimize spacecraft design to reduce astronaut exposures. To reach these objectives will require providing design engineers with accurate data bases and computationally efficient software for describing the transmission properties of space radiation in materials. Our program will reduce the uncertainty in the transmission properties of space radiation by improving the theoretical description of nuclear reactions and radiation transport, and provide accurate physical descriptions of the track structure of microscopic energy deposition.

Cucinotta, Francis A.↗
NASA NTRSDate not supplied

On The Incorporation of Both Function-Driven Requirements and Topology Optimization in The Development of Lunar Launch & Landing Pads

In the design of additively manufactured structures, there can be a constant battle between focusing on function-driven design and topology optimization in an attempt to improve a products ability to meet all requirements. Function-driven optimization is defined as the process of identifying the best design parameters that satisfy project functionality requirements. Function-driven optimization is typically implemented by a designer(s) who, instead of computational limitations, has human bias for the input or inputs determined. On the other hand, topology optimization is defined as a mathematical method that optimizes material layout within a given design space, for a given set of loads, boundary conditions, and constraints with the goal of maximizing the performance of the system. In topology optimization, the work is defined by the limited mathematical inputs and computationally available goals/restraints. If not evaluated simultaneously, these two approaches can result in fundamentally different design concepts meant to address the same requirements. NASA’s Artemis program includes the development of a base camp near the lunar South Pole. After site selection is made, one of the first infrastructure elements needed will be a launch and landing pad (LLP). The construction of such a pad will prevent damage to nearby structures from plume-surface interaction (PSI) while also providing a stable platform for the lander. These benefits reduce the required distance between the established landing zones and other settlement infrastructure. In this paper, the authors evaluate the requirements associated with lunar LLP development, application of both functional and topological optimization techniques, methodologies to combine the two techniques, and potential impacts on final design of such a pad.

In-situ↗
DOE OSTI2021

CARPE DIEM: Coupled Algorithms for Robust Partitioning of Equations for the Dynamic Interactions of Evolving Materials

From aircraft design to non-proliferation, technical and policy decisions are becoming increasingly reliant on simulation of complex, real-world, multi-physics systems involving multiple interacting domains. As the power of computers has grown, deficiencies associated with traditional low-order-accurate mechanisms for inter-domain coupling have become increasingly apparent. The CARPE DIEM project addressed such deficiencies in the context of fluid-structure interaction (FSI) by developing new algorithms and simulation techniques that are based on a novel and rigorous mathematical approach and that are designed for efficiency on modern high-performance computing platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Automated Design of Polymer Precursors for Rational Design of Porous Carbon-Materials [Slides]

Overview: Developed capability enables easy building of realistic polymer structures and a straightforward evaluation of polymer properties using different computational approaches. Topics of relevance to LANL mission: fundamental understanding of gas, vapor, ion, and liquid transport in polymer materials with applications in carbon capture, clean energy, hydrogen production (e.g., fuel cells and electrolyzes); Toxic element removal; Design of polymer binders in explosives. Workforce Development: Project provided a valuable opportunity to for a PI to manage a LANL research effort, expand collaborations (e.g., MPA division), and establish research topic that aligns with the LANL mission; The capability built was highlighted in the proposal "Versatile Polyolefins-Based Ionomer Platform for Next Generation Polymer Electrolyte Membrane and Hydrogen Production" lead by InnoSense LLC in collaboration with MPA-1 and T-1 as a response to DOE's call DE-FOA-0003003: Science Foundations for Energy Earthshots.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Redox Defect Thermochemistry of FeAl 2 O 4 Hercynite in Water Splitting from First-Principles Methods

Solar thermochemical hydrogen (STCH) production is a promising route to produce fuels from sunlight via high-temperature water splitting. However, efficient and technologically viable implementations of this process only allow a narrow window of thermodynamic boundary conditions that can be used to cycle the system, thus limiting the design space for suitable metal oxide redox mediators. An oxygen defect redox mechanism can contribute a favorable reduction entropy to expand this window, and computational evaluation of materials with high oxygen defect entropies could play a pivotal role in guiding the discovery and design of suitable oxides. This study employs first-principles calculations to investigate the redox mediating defect mechanism of the STCH candidate material, hercynite (FeAl 2 O 4 ). We compare the results of total energy calculations from density functional theory (DFT) with beyond-DFT approaches, including hybrid functionals and the random phase approximation, which are among the most advanced methods currently feasible for supercell defect calculations. Using the predicted formation energies, we perform thermodynamic modeling of FeAl2O4 reduction and oxidation via free energy minimization that incorporates ideal gas, configurational, and vibrational entropy contributions evaluated within the quasi-harmonic approximation. Special attention is devoted to understanding interactions among co-existing defects, such as the association of pairs and complexes of O vacancies and cation antisite defects, and the effect of mutually compensating defect charges. Our results corroborate the notion that the details of defect interactions can be decisive for the viability of hydrogen production within the desirable STCH process window.

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

Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry

We report numerical homogenization enables efficient computational analysis and design of multiscale structures made of micro-architected materials including lattice unit cells. However, predicting yield is nontrivial because it requires accurate and efficient predictions of the maximum stress inside the homogenized unit cells. To address this challenge, we develop a macroscale anisotropic yield function for micro-architected materials. The yield function depends on the three-dimensional macroscale stress state and the parameters describing a family of micro-architectures, such as the radii of the struts in a lattice unit cell. To ensure accuracy, we determine the maximum stress using three-dimensional continuum finite-element analysis. To ensure efficiency, we construct surrogate models from the aforementioned high-fidelity results for yield prediction. To reduce simulation costs and surrogate modeling complexity, we leverage orthotropic symmetry commonly found in lattice unit cells. In this paper, we provide a thorough presentation of group representations and the systematic procedure to exploit orthotropic domain symmetry in homogenization and surrogate modeling. We illustrate the use of linear homogenization results to predict yield in specific unit cells without further simulations. We furthermore show that surrogate modeling presents a viable option for anisotropic yield prediction in a continuously parametrized family of micro-architectures. More specifically, despite the dimensionality and degree of nonlinearity in the maximum micro von Mises stress within the unit cell, the surrogate models can predict it with less than 5% error at least 90% of the time. Moreover, the largest under-prediction error, which is more critical than the over-prediction error, is typically less than 10%.

42 ENGINEERING↗