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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optical transmission and dimensional stability of single-crystal sapphire after high-dose neutron irradiation at various temperatures up to 688 °C

The use of single-crystal sapphire optical fibers has been considered to extend fiber-optic sensing to the extreme temperature (>1000 °C) environments encountered in nuclear applications. However, before these sapphire fiber–based sensors can be deployed, their optical transmission and dimensional stability (which impacts drift of some sensors) must be characterized under representative testing conditions. Data regarding the optical transmission of sapphire following high-dose neutron irradiation at temperatures >100 °C is extremely limited. This work provides measurements of optical density (i.e., attenuation) and directional dimensional changes in bulk single-crystal sapphire materials irradiated to a fast neutron fluence of 2.4 × 10 21 n/cm 2 (3.5 displacements per atom) at temperatures ranging from 95 to 688 °C. Optical density measured after irradiation at 95 and 298 °C showed ultraviolet and visible absorption bands corresponding to known defect centers and temperature trends that were generally consistent with previous ex situ and in situ measurements made at much lower neutron fluence. However, optical density measured after irradiation at 688 °C was as much as two orders of magnitude higher, indicating that the fundamental mechanism for radiation-induced attenuation changes at this irradiation temperature. Additional analysis and comparison with previous works suggest that the attenuation may result from void formation, leading to increased Rayleigh scattering losses in the material and increased swelling that would also result in drift of Bragg grating-based sensors in sapphire fibers. These results pose serious questions regarding the feasibility of sapphire fiber–based sensors for high-temperature nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of Temperature Heterogeneity in Utility-Scale Power Plant Boilers by Spatially Distributed Ultrasonic Measurements

In extreme environments, even hardened insertion sensors fail quickly. For such environments, we have developed an ultrasonic (US) method for measuring the spatial distribution of temperatures in solid materials and, specifically, across containments of extreme processes. By deploying US sensors in multiple locations, spatial heterogeneity in temperatures inside harsh environments may be characterized. In this note, we update our progress on testing the developed approach in an industrial setting and its performance in describing the temperature distribution inside a 500 MW (electrical) coalfired utility boiler of an electrical power generation plant. We use waveguides (WG) structured to contain echogenic features that produce a train of echoes in response to an excitation pulse. The time of flight between echoes encodes the information on the temperature distribution in the corresponding segment of the WG, which we reconstruct using its parametrization. Five waveguides were welded to the boiler’s heat exchange surface (waterwall) and different locations at the same boiler elevation and produced reliable US waveforms 18 months after the installation. For several weeks during the latest trial, we performed US measurements in five locations spanning half of the boiler’s 14-meter width. The measurements were acquired during regular operation, including load cycling to adjust for demand and intermittent contributions of renewable power sources, and during boiler shutdown for emergency maintenance and the subsequent restart. The interpretation of US waveforms by signal processing resulted in an accurate estimation of temperature distribution along the waveguides. We captured daily cyclical variations in demand-following load. Measurements performed in multiple locations revealed unexpected temperature variations across the boiler. We conclude that an array of developed sensors can provide responsive and spatial temperature measurements while maintaining functionality despite prolonged exposure to an extreme environment.

Walton, Kenneth↗
DOE OSTI2024

Corrosion Testing Of Additively Manufactured Stainless Steel 316H In Molten Salt Environments

The development of a new ASTM standard for evaluating the corrosion resistance of additive manufactured (AM) stainless steel (SS) 316H in chloride molten salts is critical for the use of these materials in extreme environments such as molten salt reactors (MSRs). This report pertains to a work package of the Advaned Materials and Manufacturing Technologies (AMMT) developing a systematic methodology to link changes in AM fabrication parameters, namely surface finishing, porosity, microstructure, and chemical heterogeneity, to corrosion performance in NaCl-MgCl2 salt, a proposed secondary coolant for MSRs. During Fiscal Year 2024, Idaho National Laboratory investigators in the AMMT program, utilized SS316H bars, fabricated with laser bed powder fusion at Los Alamos National Laboratory, to establish and optimize the workflow for evaluating these process-to-performance relationships. Standard practices for specimen preparation were established using these specimens, with particular focus on descaling and sectioning methods that align with ASTM guidelines. A comprehensive experimental design for static corrosion testing was developed, with pre- and post-exposure analysis utilizing optical microscopy and scanning electron microscopy. So far standard descaling techniques were optimized, one static corrosion test was conducted on the LANL AM SS316H specimens, and pre- and post-corrosion practices were established. In addition, the work package yielded a review paper on corrosion testing gaps for AM materials in nuclear applications and submitted a proposal for a rapid-turnaround experiment to the Nuclear Science User Facilities Program to investigate the combined effects of proton irradiation and corrosion on AM SS316H. This work package establishes a foundation for evaluating processing-to-performance relationships for AM SS316H in harsh conditions, contributing to the safe and efficient design of components for next-generation nuclear reactors. The creation of a standardized methodology and the generation of relevant publications and future research pathways represent significant strides towards integrating AM materials into critical applications where corrosion resistance is paramount.

36 MATERIALS SCIENCE↗
DOE OSTI2025

RD53 pixel readout integrated circuits for ATLAS and CMS HL-LHC upgrades

The RD53 collaboration has since 2013 developed new hybrid pixel detector chips with 50 × 50 μm2 pixels for the HL-LHC upgrades of the ATLAS and CMS experiments at CERN. A common architecture, design and verification framework has been developed to enable final pixel chips of different sizes to be designed, verified and tested to handle extreme hit rates of 3 GHz/cm2 (up to 12 GHz per chip) together with an increased trigger rate of 1 MHz and efficient readout of up to 5.12 Gbits/s per pixel chip. Tolerance to an extremely hostile radiation environment with 1 Grad over 10 years and induced SEU (Single Event Upset) rates of up to 100 upsets per second per chip have been major challenges to make reliable pixel chips. Three generations of pixel chips, and many specific mixed signal building blocks and radiation test chips, have been submitted and extensively tested to get to final production chips. The large, complex and high rate pixel chips have been developed with a strong emphasis on low power consumption together with a concurrent development and qualification of novel serial powering at chip, module and system level, to minimize detector material budget.

Alimonti, G↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Sample test array and recovery (STAR) platform at the National Ignition Facility

We have developed the Sample Test Array and Recovery (STAR) platform for the National Ignition Facility (NIF) for studying the thermal and hydrodynamic responses of materials in extreme environments. The STAR platform expands the range of obtainable fluences and quadruples the rate that materials experiments can be conducted at the NIF. Example configurations are demonstrated for fluences spanning 0.56–34 J/cm 2 with environmental isolation for post-shot material recovery and inspection and up to 1740 J/cm 2 without isolation, with surface heating rates of up to 2 × 10 14 K/s. Lastly, an example experiment involving thermally driven shock and spallation of aluminum alloy 7075 is briefly discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

On the frontiers of coupled extreme environments

Coupled extreme environments pose among the highest demands on materials, stressing the limits of temperature, pressure, electric and magnetic fields, atomic displacement rates, and chemical potentials that can be simultaneously sustained. Here, this issue highlights exciting new materials science methods that will enable rapid progress in what has traditionally been the most difficult materials arena. New opportunities in artificial intelligence, multi-physics simulations capabilities, the use of extreme conditions to synthesize novel materials, and our ability to interrogate materials under relevant conditions provide new avenues to understand and design new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Challenges in practical button cell testing for hydrogen production from high temperature electrolysis of water

High temperature electrolysis of water using solid oxide electrochemical cells (SOEC) is a promising technology for hydrogen production with high energy efficiency and may promote decarbonization when coupled with renewable energy sources and excess heat from nuclear reactors. Over the past several decades there have been extensive scientific and engineering studies on cell materials and degradation behaviors that have greatly improved current density, decreased total resistance, and lowered degradation rates. Although the technology is now at a near-commercial level, maintaining consistency in cell testing and minimizing variance in practical testing environments is an often overlooked but crucial topic. To promote high quality data collection, testing procedures and balance of plant component details are extremely important to consider. This work discusses some key factors affecting the reproducibility of practical SOEC testing on the button cell level, namely, current collection layers, cell sealing procedures, the reliability of steam and hydrogen delivery systems, cell testing fixture design, and reduction procedures. To provide a baseline and a level of standardization for the SOEC community, this work also discloses details of the standard operating procedure and techniques adopted for o-SOEC testing at Idaho National Laboratory (INL).

08 HYDROGEN↗
DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗
DOE OSTI2024

The human plasma lipidome response to exertional heat tolerance testing

The year of 2023 displayed the highest average global temperatures recorded in history— the duration and severity of extreme heat are projected to increase. Rising global temperatures represent a major public health threat, especially to occupations exposed to hot environments, such as construction and agricultural workers, and first responders. Despite efforts of the scientific community, there is still a need to characterize the pathophysiological processes leading to heat related illness and develop biomarkers that can predict its onset. Here, we performed a plasma lipidomic analysis on male and female subjects who underwent heat tolerance testing (HTT), consisting of a 2-h treadmill walk at 5 km/h with 2% inclination at a controlled temperature of 40oC. We identified 995 lipids from 27 classes, with nearly half of all detected lipids being responsive to HTT. Lipid classes related to substrate utilization were predominantly affected by HTT, with a downregulation of triacylglycerols and upregulation of free fatty acids and acyl-carnitines. We additionally examined correlations between changes in plasma lipids by using the even chain acyl-carnitines is physiological strain index (PSI). Here, even chain acyl-carnitines, bi-products of incomplete beta oxidation, and diacylglycerols displayed the highest correlation to PSI. PSI did not correlate with plasma lactate levels, which suggests that correlations with related to metabolic efficiency versus physical exertion. Overall, our results show that HTT has a strong impact on the plasma lipidome and that incomplete beta oxidation may underlie heat intolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

High Fluence Active Irradiation and Combined Effects Testing of Sapphire Optical Fiber Distributed Temperature Sensors [Slides]

All objectives have been completed. Heated irradiation indicates potential for sapphire fiber-based sensors to be used in extreme environments beyond silica fiber temperature limits. Sapphire optical fiber may not be appropriate for high fluence applications. Clad sapphire optical fibers have a temperature-dependent attenuation that has not been observed in unclad sapphire. With the appropriate pre-treatments and data post-processing, sapphire optical fiber has the potential to serve as a distributed sensor up to 1700ºC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Summary of Previous Mechanical Test Data on ODS Alloys 14YWT and OFRAC up to 1000ºC

The Nanostructured Ferritic Alloys (NFA) 14YWT and OFRAC were developed for future fission and fusion nuclear energy reactors requiring high-temperature mechanical properties that are tolerant to extreme neutron irradiation environments. The NFA contain a high concentration of Ti-, Y- and O-enriched nanoclusters (NC) and ultra-fine grains to achieve high temperature strength and creep properties and high sink strength for trapping irradiation induced point defects to minimize hardening and swelling and transmutated He atoms to form intragranular nano-size bubbles that prevent formation of coarse bubbles on grain boundaries that cause embrittlement. The mechanical properties of 14YWT and OFRAC have been acquired from tensile and creep tests conducted in the past at Oak Ridge National Laboratory. The development of 14YWT started in 2000, resulting in the production of numerous heats. Tensile properties were obtained from eleven heats of 14YWT from room temperature to 800ºC. Tensile data for the SM10 heat of 14YWT was extended to 1,000ºC. Initial development of OFRAC occurred in 2016. Tensile tests conducted from room temperature to 800ºC revealed similar properties of OFRAC with those obtained from the newer generation of 14YWT heats. The creep properties of 14YWT-SM10 evaluated using constant stress tensile and load time-to-failure tests at 800ºC showed low minimum creep rates with stresses of 200 and 100 MPa. The single time-to-failure test of 14YWT-SM10 at 800ºC and 100 MPa was terminated after 20,357 hours with no specimen failure and a low creep strain of ~0.24 %. The creep properties of OFRAC determined from strain-rate jump tests at were similar those of 14YWT-SM10. The stress exponent for 14YWT and OFRAC at 800ºC are similar and are consistent with threshold stress behavior. Since both 14YWT and OFRAC are candidates for fuel cladding in future fast reactors, several fabrication studies were recently conducted and have successfully demonstrated that thin wall tubes can be fabricated from 14YWT and OFRAC by cold pilger rolling and high precision tube rolling. The high temperature mechanical properties and feasibility of fabricating thin wall tubes make 14YWT and OFRAC candidates for application as heat pipes in advanced micro-reactors. The purpose of this report is to summarize the previously obtained tensile and creep data at temperatures up to 1000ºC for NFA 14YWT and OFRAC that have been acquired over the past 20 years at ORNL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Methodologies for Design, Characterization and Testing of Electrolytes that Enable Extreme Fast Charging of Lithium-ion Cells

Selection, testing and validation of electrolyte candidates for Li-ion cells are discussed, based on a 10-minute target for extreme fast charge (XFC). A combination of modeling and laboratory measurements create a timely and synergistic approach to identifying candidate electrolyte formulations. Multi-solvent systems provide a balanced set of properties, wherein lower molecular-weight solvents offer reduced viscosity, increased species diffusivity, and mitigation of concentration polarization at high charge rates. Carefully selected formulations can exhibit peak conductivity and usable conductivity range of two to three times that of the baseline EC-EMC (3:7, wt.) + LiPF 6 . Candidates are also chosen based on stability and longevity within the cell environment. Lab testing coincides with property predictions from the Advanced Electrolyte Model (AEM) and a macro-scale cell model. Furthermore, cell testing utilized coin and pouch cells having NMC532 or NMC811 cathodes with graphite electrodes. Results indicate combinations of low-molecular weight solvents are key for fast-charge electrolytes as they extend the useful conductivity range to both low and higher salt concentrations, and possess higher self-diffusivities compared to conventional solvents. This reduces impacts from concentration polarization. The choice of electrolyte also influences the tendency for lithium metal deposition at the anode, as showcased by experimental and modeling results herein.

25 ENERGY STORAGE↗
DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Review on Perovskite Solar Cells: From Single‐Junction Devices to Tandem Deployment in Space

Perovskite solar cells (PSCs) have emerged as a transformative photovoltaic technology, offering high power conversion efficiency (PCE) and the potential for cost-effective manufacturing. However, stability and large-scale manufacturing remain critical challenges that must be addressed for widespread adoption. This review provides a roadmap from single-junction perovskite solar cells to tandem deployment in space. First, material-level innovations are discussed, including mixed-cation and low-dimensional perovskites, transport materials, and additives that improve thermal and structural stability while enhancing efficiency. Then, we examine both established industrial standards and emerging scientific protocols aimed at stabilizing PSCs under operational conditions, including tandem cell integration strategies and encapsulation techniques to mitigate performance degradation. Manufacturing scalability is a focal point, where deposition methods and green solvents are explored to improve large-area film uniformity and reduce environmental impact. Additionally, the increasing viability of PSCs in extraterrestrial environments is assessed, with emphasis on their performance in space applications, radiation resistance, and flexible lamination methods for deployment in extreme conditions. Progress across materials innovation, device architectures, stability testing protocols, and both terrestrial and extraterrestrial applications collectively drives perovskite photovoltaics toward higher efficiency, stability, and cost-effectiveness.

flexible PSCs↗
DOE OSTI2025

A multidimensional approach to quantum state tomography of photoelectron wavepackets

There is a growing interest in reconstructing the density matrix of photoelectron wavepackets, in particular in complex systems where decoherence can be introduced either by a partial measurement of the system or through coupling with a stochastic environment. To this end, several methods to reconstruct the density matrix, quantum state tomography protocols, have been developed and tested on photoelectrons ejected from noble gases following absorption of extreme ultraviolet (XUV) photons from attosecond pulses. It remains a challenge to obtain model-free, single scan protocols that can reconstruct the density matrix with high fidelities. Current methods require extensive measurements or involve complex fitting of the signal. Efficient single-scan reconstructions would be of great help to increase the number of systems that can be studied. We propose a new and more efficient protocol that is able to reconstruct the continuous variable density matrix of a photoelectron in a single time delay scan. It is based on measuring the coherences of a photoelectron created by absorption of an XUV pulse using a broadband infrared (IR) probe that is scanned in time and a narrowband IR reference that is temporally fixed to the XUV pulse. We illustrate its performance for a Fano resonance in He as well as mixed states in Ar arising from spin-orbit splitting. We show that the protocol results in excellent fidelities and near-perfect estimation of the purity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

High-throughput quantification of quasistatic, dynamic and spall strength of materials across 10 orders of strain rates

Abstract The response of metals and their microstructures under extreme dynamic conditions can be markedly different from that under quasistatic conditions. Traditionally, high strain rates and shock stresses are achieved using cumbersome and expensive methods such as the Kolsky bar or large spall experiments. These methods are low throughput and do not facilitate high-fidelity microstructure–property linkages. In this work, we combine two powerful small-scale testing methods, custom nanoindentation, and laser-driven microflyer (LDMF) shock, to measure the dynamic and spall strength of metals. The nanoindentation system is configured to test samples from quasistatic to dynamic strain-rate regimes. The LDMF shock system can test samples through impact loading, triggering spall failure. The model material used for testing is magnesium alloys, which are lightweight, possess high-specific strengths, and have historically been challenging to design and strengthen due to their mechanical anisotropy. We adopt two distinct microstructures, solutionized (no precipitates) and peak-aged (with precipitates) to demonstrate interesting upticks in strain-rate sensitivity and evolution of dynamic strength. At high shock-loading rates, we unravel an interesting paradigm where the spall strength vs. strain rate of these materials converges, but the failure mechanisms are markedly different. Peak aging, considered to be a standard method to strengthen metallic alloys, causes catastrophic failure, faring much worse than solutionized alloys. Our high-throughput testing framework not only quantifies strength but also teases out unexplored failure mechanisms at extreme strain rates, providing valuable insights for the rapid design and improvement of materials for extreme environments.

Eswarappa Prameela, Suhas (ORCID:0000000334530184)↗