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NASA NTRS1998

Combined Numerical/Analytical Perturbation Solutions of the Navier-Stokes Equations for Aerodynamic Ejector/Mixer Nozzle Flows

In spite of rapid advances in both scalar and parallel computational tools, the large number of variables involved in both design and inverse problems make the use of sophisticated fluid flow models impractical, With this restriction, it is concluded that an important family of methods for mathematical/computational development are reduced or approximate fluid flow models. In this study a combined perturbation/numerical modeling methodology is developed which provides a rigorously derived family of solutions. The mathematical model is computationally more efficient than classical boundary layer but provides important two-dimensional information not available using quasi-1-d approaches. An additional strength of the current methodology is its ability to locally predict static pressure fields in a manner analogous to more sophisticated parabolized Navier Stokes (PNS) formulations. To resolve singular behavior, the model utilizes classical analytical solution techniques. Hence, analytical methods have been combined with efficient numerical methods to yield an efficient hybrid fluid flow model. In particular, the main objective of this research has been to develop a system of analytical and numerical ejector/mixer nozzle models, which require minimal empirical input. A computer code, DREA Differential Reduced Ejector/mixer Analysis has been developed with the ability to run sufficiently fast so that it may be used either as a subroutine or called by an design optimization routine. Models are of direct use to the High Speed Civil Transport Program (a joint government/industry project seeking to develop an economically.viable U.S. commercial supersonic transport vehicle) and are currently being adopted by both NASA and industry. Experimental validation of these models is provided by comparison to results obtained from open literature and Limited Exclusive Right Distribution (LERD) sources, as well as dedicated experiments performed at Texas A&M. These experiments have been performed using a hydraulic/gas flow analog. Results of comparisons of DREA computations with experimental data, which include entrainment, thrust, and local profile information, are overall good. Computational time studies indicate that DREA provides considerably more information at a lower computational cost than contemporary ejector nozzle design models. Finally. physical limitations of the method, deviations from experimental data, potential improvements and alternative formulations are described. This report represents closure to the NASA Graduate Researchers Program. Versions of the DREA code and a user's guide may be obtained from the NASA Lewis Research Center.

DeChant, Lawrence Justin↗

DOE OSTI2025

Preliminary Kinetic Analysis of Non-Equilibrium Plasma- Assisted Methanol Pyrolysis and Oxidation Experiments

Efforts to enhance power generation efficiency and reduce emissions have driven interest in novel combustion techniques, including non-equilibrium plasma (NEP) ignitors. NEP ignitors show promise in improving energy conversion efficiency, fuel reforming, emission control, and lean-flammability limits. However, their adoption is hindered by a limited understanding of the interplay between plasma-enhanced combustion and thermal chemistry, particularly for complex fuels under engine-relevant conditions. Developing experimentally validated kinetic mechanisms is therefore critical. Additionally, the increasing interest in renewable biofuels like ethanol and methanol, coupled with the desirable qualities of NEP ignitors, presents a compelling opportunity for study. Therefore, this work acts as an extension of a previous work (Bopaiah et al., 2023) pertaining to the experimental results of NEP-assisted methanol pyrolysis and oxidation. Experiments were performed with a custom-built plasma flow reactor at 0.5 atm and temperatures from 523-1203 K. All reactive mixtures are extremely diluted to minimize exothermicity due to reactivity, allowing isothermal assumptions and the isolation of plasma chemistry from thermal chemistry. A dielectric barrier discharge plasma, at 14 kV and 15 ns full-width half maximum, was applied to the reactive mixture at varying frequencies to maintain the number of pulses with increasing temperature. Steady-state product speciation was performed downstream of the reactor with ex-situ GC/MS diagnostics. The attained experimental results were examined through in-depth analysis performed by means of an in-development plasma-coupled kinetic mechanism. As discussed in the previous work, the plasma significantly accelerates methanol pyrolysis, increasing stable intermediate production, including oxygenated and nitrile species. Plasma-assisted oxidation shows even faster fuel consumption compared to pyrolysis and a 200 K ignition shift compared to thermal oxidation. For plasma-assisted pyrolysis, the model demonstrates that accelerated fuel consumption stems from dissociative quenching of excited N2 states with fuel and H2, generating H radicals that react to rapidly form CH3 and CH2OH radicals. At low temperatures, these radicals recombine to produce oxygenates, while CH3 drives nitrile and hydrocarbon formation at higher temperatures. While the model captures pyrolysis trends well, discrepancies in methane, ethylene, and ethanol predictions are present. Similarly, the model faces challenges in accurately representing plasma-assisted oxidation, predicting a much steeper fuel gradient and ignition 100 K earlier than the experiment. While a similar scheme to pyrolysis is nested in the reaction pathway, the enhancement of the O and H radical fluxes and their initiation of the OH and HO2 radical pools dominate fuel and intermediate oxidation. The overestimation of these processes is shown to be responsible for the divergence of model from experiment. While the modelling predictions of this preliminary mechanism are not perfect, they serve as a valuable starting point. Primarily, they elicited new reaction pathways that are not otherwise possible in thermal chemistry induced reaction kinetics. The results also provide a basis for the future work that should be performed. For example, theoretical and experimental studies on excited nitrogen species and fuel/fuel radical interactions, quantification of the NOx production, and the kinetics behind the slow ignition observed in oxidation should be emphasized.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Overcoming Barriers in Electrochemical Toluene Hydrogenation for Efficient Hydrogen Storage by Pt 3 Au Alloy Catalysts

Hydrogen storage and transportation are essential for the hydrogen economy, and liquid organic hydrogen carriers (LOHCs), such as a toluene/methylcyclohexane (TOL/MCH) system, offer significant advantages in terms of safety and efficiency. However, the electrochemical reduction of TOL to MCH (TER) faces challenges from competing with the hydrogen evolution reaction (HER) and catalyst instability. Here, in this study, Pt 3 Au is introduced as a highly effective catalyst for TER. Through density functional theory screening, we identified distinctive properties of Pt 3 Au, including enhanced binding to the TER intermediates and effective HER suppression. Experimental validation confirmed these computational predictions, with Pt 3 Au achieving the highest reported Faradaic efficiency (98%) in proton exchange membrane systems. Moreover, long-term testing demonstrated that Pt 3 Au maintained Faradaic efficiencies of >90% over 9 h, highlighting its robustness and operational stability. By integrating computational modeling and experimental evaluation, this work addresses key limitations in LOHC catalysis. Pt 3 Au establishes a benchmark for selective and stable TER performance, paving the way for advanced hydrogen storage technologies. These findings emphasize the critical role of rational catalyst design in overcoming the challenges associated with scalable and efficient hydrogen storage solutions.

LOHC↗

DOE OSTI2024

Advancing Tritium Self-Sufficiency in Fusion Power Plants: Insights from the BABY Experiment

Abstract In the pursuit of fusion power, achieving tritium self-sufficiency stands as a pivotal challenge.Tritium breeding within molten salts is a critical aspect of next-generation fusion reactors, yet experimental measurements of \gls{tbr} have remained elusive.Here we present the results of the \gls{baby} experiment, which represents a pioneering effort in tritium research by utilizing high-energy (\SI{14}{\mega\electronvolt}) neutron irradiation of molten salts, a departure from conventional low-energy neutron approaches. Using a small-scale (\SI{100}{\milli\litre}) molten salt tritium breeding setup, we not only simulated, but also directly measured a \gls{tbr} \textcolor{red}{(\num{3.57e-4})}. This innovative approach provides crucial experimental validation, offering insights unattainable through simulation alone.Moreover, our findings reveal a surprising outcome: tritium was predominantly collected as HT, contrary to the expected TF. This underscores the complexity of tritium behavior in molten salts, highlighting the need for further investigation.This work lays the foundation for a more sophisticated experimental setup, including increasing the volume of the breeder, enhancing neutron detection, and refining tritium collection systems.Such improvements are crucial for advancing our understanding of fusion reactor feasibility and paving the way for future experiments.

Delaporte-Mathurin, Rémi (ORCID:0000000310648882)↗

NASA NTRSDate not supplied

A CFD Validation Study using Artemis 1 Orbital Slosh Test Data

Advancements in liquid propellant management science and technologies are key to increasing safety, decreasing cost, and increasing payload mass of NASA space missions. Liquid propellant usually comprises a large portion of the total mass of launch vehicles and spacecraft, so predicting and controlling the motion of it are important. Computational fluid dynamics (CFD) programs are critical to predicting slosh dynamics, but CFD programs require experimental validation with physically relevant test data before the results can be trusted. Low-gravity slosh test data is lacking, and most of what exists is inadequate for CFD validation. A potential slosh risk for Orion, and the lack of validated low-gravity slosh models, led to performing on-orbit slosh tests during the Artemis 1 mission to assess the impacts of slosh on the guidance, navigation, and control of Orion. This work details the validation of a coupled slosh-motion CFD model using the Orion orbital slosh test data set.

CFD↗

NASA NTRSDate not supplied

A CFD Validation Study Using Artemis 1 Orbital Slosh Test Data

CFD↗

DOE OSTI2024

Novel ceramic capacitors with ultrahigh energy density and efficiency (Final Technical Report)

Antiferroelectric ceramics are a special class of material that have shown great potential as the dielectric in electrical capacitors due to their high energy- and power-density. During each charge-discharge cycle, the ceramic undergoes transformation to a ferroelectric phase and resumes its antiferroelectric phase. The hysteresis associated with the transitions leads to a mediocre energy efficiency and service lifetime of antiferroelectric capacitors and, hence, their almost absence in commercial products. Under the support of this research project, we first formulated a universal lattice-compatibility theory that included electrostatic polarization energy along with elastic energy and thermal energy to understand the origin of the hysteresis in antiferroelectric oxides. Guided by this compatibility theory, we conducted high-throughput density functional theory (DFT) calculations to assess chemical modifiers and their effect on crystal structures of 400+ PbZrO 3 -based compositions. Down-selected compositions were experimentally validated for their suppressed hysteresis and higher energy efficiency. The verified low-hysteresis compositions were then expanded to an antiferroelectric ceramic library with nearly 500 new compositions (more than 1,500 samples) using high-throughput experiments involving ceramic synthesis and property screening. The large quantity of data generated (theory and experimental) in these tasks were processed by machine-learning techniques and identified trends were fed to the next iteration. In the end, we successfully discovered four compositions with near-zero hysteresis, yielding a world-record energy efficiency of 98.2% at an energy density of 3.0 J/cm 3 . Furthermore, our antiferroelectric ceramic capacitor reaches 79.5 million charge-discharge cycles lifetime, a factor of 80 enhancement over previous antiferroelectric ceramics with large hysteresis. These research accomplishments have not only met the milestones set in the SOPO, but also led to two patent filings, three journal publications (one of them was in Advanced Materials, impact factor 29.4), and nine oral presentations at various venues. Through the course of the project, three postdocs, four Ph.D. students, and one M.S. student were trained. In short, our project established a new methodology in searching next-generation functional ceramics on the fundamental side and discovered several high-efficiency antiferroelectric compositions for capacitors on the applied side. Once fabricated into the multilayer form for commercial applications, these ceramic capacitors can potentially enable the high temperature high power density DC-link capacitors that are critical for the next generation inverters in electric vehicles. The project also significantly contributed to the nation’s workforce development in the STEM fields.

36 MATERIALS SCIENCE↗

NASA NTRS2023

NASA GRC ICME Schema for Materials Data Management: An Executive Summary

Integrated Computational Materials Engineering (ICME) has received a growing emphasis in attention due its potential impact on rapid material design, reduction in cost and time to market for new applications, and the promise of ‘fit-for-purpose’ materials coupled with recent advances in high performance computing and material characterization tools. However, for an organization to implement ICME practices for material discovery and design, a series of both technical and cultural challenges must be overcome to foster an environment that enables efficient, traceable, and predictive multiscale simulations of material behavior to enable virtual design of materials. In 2016, NASA sponsored a 2040 Vision study to define the potential 25-year future state required for integrated multiscale modeling of materials and systems to improve both the associated time and cost for aerospace and aeronautical innovation. The study envisions a cyber-physical-social ecosystem of experimentally validated computational models, tools, and techniques, along with the associated digital tapestry, that can enable rapid, optimized, ‘fit-for-purpose’ design of materials, components, and systems. A key requirement for such an ecosystem is the development of a robust information management system for materials across their full lifecycle, including material pedigree, experimental (real) and virtual (simulation) data, developed material models, and the implementation of models in engineering applications, such that process-structure-property-performance relationships can be established, thereby enabling the virtual design and optimization of materials. Such an information management system must be able to effectively capture: i) material information at each length scale; ii) test data and analysis; iii) associated material models; and iv) material and model deployment in engineering applications. These systems must also provide traceability between experimental and virtual representations of the material to ensure, when appropriate, the material digital twin is maintained. Additionally, this robust material information management system must be able to seamlessly connect with both commercial and an organization’s in-house software tools, be they analysis tools, other material databases, product lifecycle management (PLM) or simulation data management (SDM) tools, etc., such that automation of the design and analysis of a material across multiple length scales is possible. In this paper, an executive summary of the NASA GRC ICME Schema for materials information management is presented. The database best practices and schema design philosophy specifically for ICME materials data management and an overview description of each element in the schema is given, along with its associated role in an ICME workflow. Additionally, auxiliary tools that interact with the database and provide judicious automation with regards to importing, exporting, and analyzing materials data are presented. Such tools are critical to an ICME ecosystem, not only for their role in enabling optimization, but also in relieving users of tedious manual tasks, thus helping to promote adoption and combat the cultural challenges organizations face in enabling ICME.

Materials↗

NASA NTRS2006

Experimental and Numerical Characterization of Transient Insertion of Heat Flux Gages in a Cylindrical Black Body Cavity at 1100 C

Initial transient thermal models have been developed to simulate a heat flux gage calibration process capable of generating high heat flux levels of interest to reciprocating and gas turbine engine industries as well as the aerospace industry. These transient models are based on existing, experimentally validated, steady state models of the cylindrical blackbody calibration system. The steady state models were modified to include insertion of a heat flux gage into the hot zone of the calibration system and time varying electrical current passing through the resistance heated blackbody. Heat fluxes computed using the initial transient models were compared to experimental measurements. The calculated and measured transient heat fluxes were within 5% indicating that the major physical phenomena in the transient calibration had been captured by the models. The predicted and measured transient heat fluxes were also compared at two different gage insertion depths. These results indicated that there is an optimum insertion position which maximizes heat flux and minimizes cavity disturbance.

Abdelmessih, A. N.↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗

NASA NTRS2019

Lipid Decontamination Procedures for Life Detection Missions

Molecular biosignatures are key targets for current, proposed, and future life detection missions. With the high accuracy and low limit of detection (LOD) that new and future instruments will require, decontamination of life detection hardware is necessary to prevent false positives. Lipids are a molecular biosignature of interest, as they are ubiquitous to all life as we know it, can survive unaltered in the geologic record for longer than any other biomolecule (i.e. billions of years), and form through both biotic and abiotic processes. Lipids display origin-diagnostic molecular patterns that can reveal biotic or abiotic synthesis, so finding them and ascertaining their molecular features is important for potentially detecting evidence of life elsewhere. Traditional methods of decontamination, or contamination control (CC), primarily clean hardware through fabrication in sterile (cleanroom) environments, killing microbes, and removing/flushing contaminants off instrument and spacecraft components. However, research suggests that some standard cleaning methods are either unlikely to remove lipid contaminants or are incompatible with life detection instrument materials. To solve this problem, I propose to find, test, and verify a decontamination method that thoroughly cleans instruments by destroying lipid molecules, but is simultaneously compatible with major materials used in these instruments. I will study the effects of traditional CC methods (including Dry Heat Microbial Reduction and Vapor phase Hydrogen Peroxide) and experimental CC methods (Electron Beam Irradiation) on lipid molecules for application to life detection instrumentation. I will then develop a CC plan for a novel lipid detector (ExCALiBR, Extractor for Chemical Analysis of Lipid Biomarkers in Regolith) searching for lipids in either soil or icy world scenarios. This plan will uphold planetary protection regulation requirements and validate experimental analyses of in-situ life detection tests.

Decontamination↗

DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗

DOE OSTI2025

Investigation of divertor detachment induced through neon seeding and density ramp on HL-3

A new self-consistent 1D scrape-off layer model has been recently developed in BOUT++ framework, named SD1D, which includes equations for various particle species (e.g. main plasma, neutrals and impurities) and couples open databases like ADAS and AMJUEL. It is able to quickly and effectively simulate divertor detachment experiments. In this work, a typical detachment experiment (shot #6270) on HL-3 with neon seeding is simulated using the SD1D code. It is found that the target electron temperature and the target ion saturation current in the simulations are consistent with experimental results measured by Langmuir probes on the target plate. The variation of D α radiation intensity in the divertor is qualitatively similar to the measured D α signal. Following the experimental validations, different upstream densities are set in the simulations to study the impurity distribution under different plasma density conditions. It is found that increasing upstream density can be helpful for the control of the neon radiation front (closer to the target). In this work we also compare two detachment regimes in simulations. Based on the same initial experimental parameters (shot #6270) on HL-3, a scan of upstream density and a scan of neon seeding rate are carried out respectively. It is found that the role of atomic and molecular processes is different in the two detachment regimes. The current density roll-over is ascribed to a drop in the divertor ion source, and the variation of D α radiation intensity via different excitation channels is associated with the relevant collisional reaction sources.

BOUT++↗

DOE OSTI2026

Probability of Hydrogen Ignition: A Landscape Review and Gaps Assessment

The primary hazard of a leak from a hydrogen system is due to the immediate or delayed ignition of the fuel leading to a jet flame or explosion. Therefore, understanding the hydrogen ignition probability is critical for analyzing the risk of hydrogen systems. This report reviews the current understanding of hydrogen ignition mechanisms and methods for modeling their probability. The stoichiometry, ignition strength, and ignition source temperature are all important characteristics that can affect both the probability of ignition and the outcome of the subsequent combustion event. A brief review of diffusion ignition demonstrates that ignition probability models must account for seemingly spontaneous ignition of hydrogen in addition to scenarios where the ignition source is readily identified. State-of-the art models for both immediate and delayed ignition probabilities are presented, including different physical aspects of the scenarios (e.g., flow rate, ignition source characteristics) that are considered in the different modeling approaches. Current models often fail to account for the unique properties of hydrogen compared to other fuels, and most lack rigorous validation with hydrogen as a fuel. A fault tree framework is proposed to systematically evaluate the probability of ignition by integrating various ignition mechanisms and their uncertainties. Furthermore, this type of framework could enable additional insights into the most important mechanisms and would enable uncertainty quantification in risk assessment modeling. Recommendations for future research include the need for experimental validation of ignition models and the development of comprehensive methodologies that incorporate the specifics of hydrogen behavior in real-world scenarios.

hydrogen↗

DOE OSTI2025

Predicting multi-nodal in-nozzle particle interactions in high-viscosity fluid mediums for acoustophoretic direct-ink writing of line-patterned composites

Patterned functional materials offer improved properties (electrical, thermal, etc.) over their bulk counterparts in many applications, including energy storage, flexible electronics, and sensors. However, manufacturing approaches for patterning materials over large areas with features on the order of hundreds of microns or less are limited. Acoustophoresis, which uses acoustic forces to control particle arrangement in a fluid medium, is a pathway to address this challenge. This process is dependent on particle and fluid properties and enables patterning of a broad range of materials. Herein, a model with experimental validation is presented to demonstrate that acoustophoresis can be combined with direct-ink writing (DIW) to fabricate line patterns over large cm-scale areas. An in-nozzle particle interaction model was developed to investigate the impact of processing conditions on multi-nodal acoustophoretic DIW. The model predicts patterned line widths within a factor of two relative to experimental results for a high viscosity case study. Here, the model was used to investigate the impact of frequency, particle loading, particle radius, and acoustic pressure on line width and patterning time, providing critical feedback regarding the processing conditions suitable for a target application. Model results illustrate that frequency has the greatest impact on line patterns: increasing from 1 to 3 MHz resulted in a greater than 65% reduction in line width and a greater than 85% reduction in patterning time. Additionally, experiments were conducted with an alumina-epoxy ink and a ~21 cm 2 area pattern was rastered in ~5.5 minutes, demonstrating a path towards large-area line-patterned composite fabrication.

25 ENERGY STORAGE↗

DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory. The shear modulus model for the ambient phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress–strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston–Tonks–Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to ⁠10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid–liquid but never a fully liquid state, never fully lost strength, stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Effect of Insertion of a Heat Flux Gage into a High Temperature Cylindrical Blackbody Cavity on the Gage

Detailed transient thermal models have been developed to simulate a heat flux gage calibration process capable of generating high heat flux levels. These heat flux levels are of interest to the reciprocating and gas turbine engine industries as well as the aerospace industry. The transient models are based on existing, experimentally validated steady state models of a cylindrical blackbody calibration system. The steady state models were modified to include insertion of a heat flux gage into the hot zone of the calibration system, time-varying electrical current that passes through the resistance heated blackbody, and the resulting heating of the heat flux gage. Heat fluxes computed using detailed transient models were compared to experimental measurements. The calculated and measured transient heat fluxes agreed to within 2 percent, indicating that the models had captured the physical phenomena in the transient calibration. The predicted and measured transient heat fluxes were also compared for two different blackbody configurations. The effect of convection on the blackbody extension was evaluated and found to be a minor factor.

Abdelmessih, Amanie N.↗

DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a 10 multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory (DFT). The shear modulus model for the ambient β phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the β phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress-strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston-Tonks-Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to 10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability 20 of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid-liquid but never fully liquid state, never fully lost strength, 25 stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗