Search NASA⌕ Search

SEARCH · Search NASA

Results for “Complex concentrated alloys”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Understanding failure in austenitic steels: Key considerations for molten salt storage in CSP applications

Owing to their excellent properties, austenitic stainless steels are extensively used in boilers, furnaces, molten salt tanks, and other applications that are subjected to extreme mechanical loads and high-temperature conditions. Their high corrosion and creep resistance make them suitable for high-temperature operating environments. Additionally, good fatigue resistance and favorable mechanical and visual properties are essential. However, the premature failure of several components at elevated temperatures has been previously reported. Although the failure analysis of components in service is complex, processes such as cold work and welding have been identified as contributing factors to the performance degradation of these steels. This study aims to analyze the various documented failure modes and mechanisms in austenitic steels, including creep, cracking, stress relaxation cracking, and fatigue, to better understand the multi-objective design requirements for these alloys as structural materials for high-temperature molten salt tanks in Concentrating Solar Power (CSP) plants. Stabilized austenitic grades, such as AISI 347H, demonstrate superior resistance to creep and corrosion-related degradation when compared to non-stabilized grades like AISI 316L at temperatures relevant to concentrated solar power (CSP) applications. In contrast, nickel-based alloys offer enhanced corrosion resistance, albeit at a higher cost. This review underscores that creep, stress relaxation cracking, and thermo-mechanical fatigue are the predominant long-term failure risks in CSP hot tanks.

14 SOLAR ENERGY↗
DOE OSTI2021

Dilute Pd-in-Au alloy RCT-SiO 2 catalysts for enhanced oxidative methanol coupling

Dilute alloy catalysts have the potential to enhance selectivity and activity for large-scale reactions. Highly dilute Pd-in-Au nanoparticle alloys partially embedded in porous silica (“raspberry colloid templated” (RCT)-SiO 2 ) prove to be robust and selective catalysts for oxidative coupling of methanol. Palladium concentrations in the bimetallic nanoparticles as low as ~3.4 at.% catalyze the production of methyl formate with a selectivity of ~95% at conversions of ~55%, whereas conversions are low (<10%) for ~1.7 at.% Pd-in-Au nanoparticle and pure Au nanoparticle catalysts. Fractional reaction orders for both CH 3 OH and O 2 measured for ~3.4 at.% Pd-in-Au nanoparticles supported on RCT-SiO 2 indicated a complex mechanism in which the sites for O 2 dissociation are not saturated. Optimal methyl formate production was found for an equimolar mixture. There is no conversion of methanol in the absence of O 2 between 360 and 450 K. Finally, all observations are consistent with a mechanism derived from model studies, requiring that clusters of Pd be available on the catalyst for O 2 dissociation.

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

The influence of laser power modulation on melt pool dynamics in laser powder bed fusion

While the majority of laser powder bed fusion (LPBF) metal additive manufacturing uses a continuous wave (CW) laser heat source, some commercial applications of LPBF additive manufacturing instead involve the modulation of the laser power on tens-of-microsecond timescales as an adjustable process variable. This article reports the use of in situ, high speed x-ray and optical imaging to probe melt pool fluid flow, defect formation, and nearby powder motion during LPBF with both modulated and CW laser heat sources. We observe melt pool dynamics unique to modulated laser melting even at very high duty cycles that are related to fluctuations in vapor depression depth, complex pore formation mechanisms, and changes to denudation physics when compared to CW melting. These behaviors are present in Ti–6Al–4V, 316L stainless steel, and AL1100 alloys but vary slightly as a function of material, indicating a substantial dependence on the viscosity and surface tension of the liquid metal. While high duty cycles produce weld tracks of comparable quality to CW melting, lower duty cycles introduce substantial defect concentrations. At intermediate duty cycles, careful control of modulation parameters can repeatably and precisely yield one pore per laser pulse, suggesting a method for intentionally inserting engineered porosity at specific sites during an LPBF build.

3D printing↗
DOE OSTI2021

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗
DOE OSTI2021

CHARGED POINT DEFECTS AND DEFECT COMPLEXES IN PHOSPHORENE AND STRUCTURAL PREDICTION OF TAU-11, A DENSITY FUNCTIONAL THEORY APPROACH

With the advent of powerful computers, first principle calculations have become a robust and reliable tool to complement experiments in the study of materials. First principle calculations such as density functional theory can be used to guide experiments to save time and effort when it comes to new and advanced materials discovery. Study of charge defects in materials and crystal structure prediction are such areas in which density functional theory calculations facilitate the prediction of defect formation energy, charge transition levels and elemental site occupations. Experimentally, studying charge defects requires inferring results from various techniques. Likewise, determining site occupancies for similar elements using experimental techniques is challenging and sometimes impossible. In this work, we study the charged intrinsic and extrinsic defects and defect complexes in phosphorene and determine the site occupancies of Al, Fe and Si in the τ11 phase using density functional theory. In the defect work, we calculate the defect formation energy, charge transition level, binding energies and Stokes shift for vacancy, dopant substitution and dopant-vacancy defect complexes in phosphorene. We found that vacancy defect in phosphorene becomes negatively charged in n-type doping and may passivate the dopants and reduce carrier concentration and mobility. For non-metal dopants in phosphorene, we predict that O, S and Si prefer to form dopant-vacancy complexes removing the vacancy defect states from the band gap. Mn dopant-vacancy defect complex exhibits possibility of switching between two magnetic spin states. Lastly, using density functional theory, we complement the results from neutron powder diffraction to determine the site occupancies of Al and Si in τ11 phase which was used to determine the alloying element to develop a high temperature and low density alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High Radiation Resistance in the Binary W‐Ta System Through Small V Additions: A New Paradigm for Nuclear Fusion Materials

Abstract Refractory High‐Entropy Alloys (RHEAs) are promising candidates for structural materials in nuclear fusion reactors, where W‐based alloys are currently leading. Fusion materials must withstand extreme conditions, including i) severe radiation damage from energetic neutrons, ii) embrittlement due to H and He ion implantation, and iii) exposure to high temperatures and thermal gradients. Recent RHEAs, such as WTaCrV and WTaCrVHf, have shown superior radiation tolerance and microstructural stability compared to pure W, but their multi‐element compositions complicate bulk fabrication and limit practical use. In this study, it is demonstrated that reducing alloying elements in RHEAs is feasible without compromising radiation tolerance. Herein, two Highly Concentrated Refractory Alloys (HCRAs) − W 53 Ta 44 V 3 and W 53 Ta 42 V 5 (at.%) − were synthesized and investigated. We found that small V additions significantly influence the radiation response of the binary W–Ta system. Experimental results, supported by ab‐initio Monte Carlo simulations and machine‐learning‐driven molecular dynamics, reveal that minor variations in V content enhance Ta–V chemical short‐range order (CSRO), improving radiation resistance in the W 53 Ta 42 V 5 HCRA. By focusing on reducing chemical complexity and the number of alloying elements, the conventional high‐entropy alloy paradigm is challenged, suggesting a new approach to designing simplified multi‐component alloys with refractory properties for thermonuclear fusion applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Solid solutions limited by grain-boundary solute clustering in ultrafine-grained alloys

Immiscible Ag-Cu alloys exhibit complex behavior due to varying Cu solid solubilities reported under equilibrium and metastable conditions. In ultrafine-grained alloys, these limits are further complicated by a high fraction of grain boundaries, where solute atoms tend to segregate and, in some cases, form clusters. Here, this study investigates the influence of Cu solute segregation and clustering on solid-solution limits in ultrafine-grained Ag-Cu alloys synthesized by magnetron sputtering with varying Cu content. X-ray diffraction peak shifts reveal a solid-solution concentration plateau for Cu contents from 4.9 to 11.7 at %, in contrast to the peak shifts predicted by density-functional theory for Ag-Cu alloys. Scanning transmission electron microscopy further reveals limited solid solubility and the formation of numerous Cu-rich clusters at grain boundaries. Atomistic simulations demonstrate that such limited solubility does not arise from grain boundary segregation alone, but only when strong solute-solute interactions promote the formation of grain-boundary Cu solute clusters.

Density-functional theory↗
DOE OSTI2020

Progress in Understanding the Origins of Excellent Corrosion Resistance in Metallic Alloys: From Binary Polycrystalline Alloys to Metallic Glasses and High Entropy Alloys

Some of the factors responsible for good corrosion resistance of select polycrystalline and emerging alloys in chloride solutions are discussed with a goal of providing some perspectives on the current status and future directions. Traditional metallic glass alloys, single phase high entropy alloys (HEAs), early metallic glasses, and high entropy metallic glasses are all emerging corrosion-resistant alloys (CRAs) that utilize traditional strategies for improved corrosion resistance as well as take advantage of some other novel beneficial attributes. These materials enjoy many degrees of freedom as far as choice of both composition and structure, providing great flexibility in the pursuit of superior corrosion resistance. The new materials depart from classical solvent-solute type polycrystalline binary or ternary alloys. Thus, such emerging materials provide significant opportunities to achieve even greater improvements in corrosion resistance in harsh environments. Several examples of the unique corrosion properties of selected materials in the context of modern theories of corrosion are discussed herein. Discussion is restricted to solid-solution binary or ternary polycrystalline alloys, several metallic glass alloys, and single phase HEAs. A common feature of many CRAs is that composition and microstructure often affect both passivity and resistance to localized corrosion that can be divided into initiation, stabilization, and propagation stages. Enormous complexities in protective oxide structures and chemistries and the large number of combinatorial possibilities in newer materials such as HEAs preclude trial-and-error approaches and perhaps even combinatorial experimental design. Computational materials methodologies will be required in the search for new corrosion-resistant alloys in these material classes. The search must consider the best scientific insights available regarding how major and minor alloy additions, as well as various microstructural attributes, contribute to corrosion mitigation. Additional scientific insights, as they emerge, will enable choices beyond the reliance on high concentrations of alloying elements that are known to affect passivity breakdown and pit stabilization. A challenge is to connect the “basic attributes” of an alloy with its properties. The strength of this connection will likely require new scientific principles enabling deep multiphysics insights in order to link feature(s) such as composition and metallurgical phases to the desired corrosion properties. Application of data informatics will likely also play a role given the plethora of variables that are important in corrosion and the difficulty in assessing all relationships. Here, the opportunity exists to accelerate the design of emerging materials for high corrosion resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure and corrosion of CrFeMnV multi-principal element alloy

In this study, a multi-principal element alloy consisting of equiatomic concentrations of Cr, Fe, Mn, and V was produced by arc melting. The combination of X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy indicated that the CrFeMnV MPEA was comprised of a BCC matrix with a dispersion of a secondary phase. Cyclic potentiodynamic polarization tests in 0.6M NaCl revealed high corrosion resistance – superior to that of stainless steel 304L, as evident from high breakdown and repassivation potentials. X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry provided insight into the formed multi-layer surface film after constant immersion and potentiostatic conditioning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of Carbon on Void Nucleation in Iron

The study reports the significance of carbon presence in affecting void nucleation in Fe. Without carbon, void nucleation rates decrease gradually at high temperatures but remain significantly high and almost saturated at low temperatures. With carbon present, even at 1 atomic parts per million, void nucleation rates show a low-temperature cutoff. With higher carbon levels, the nucleation temperature window becomes narrower, the maximum nucleation rate becomes lower, and the temperature of maximum void nucleation shifts to a higher temperature. Fundamentally, this is caused by the change in effective vacancy diffusivity due to the formation of carbon-vacancy complexes. The high sensitivity of void nucleation to carbon comes from the high sensitivity of void nucleation to the vacancy arrival rate in a void. The void nucleation is calculated by first obtaining the effective vacancy diffusivity considering the carbon effect, then calculating the defect concentration and defect flux change considering both carbon effects and pre-existing dislocations, and finally calculating the void nucleation rate based on the recently corrected homogeneous void nucleation theory. The study is important not only in the fundamental understanding of impurity effects in ion/neutron irradiation but also in alloy engineering for judiciously introducing impurities to increase swelling resistance, as well as in the development of simulation and modeling methodologies applicable to other metals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MOSCATO Development and Integration in Fiscal Year 2025: Implementation of Multiphase, Multiphysics Modeling Capabilities for Molten Salt Systems

MOSCATO (Molten Salt Chemistry and Transport) is a multiphysics code that provides high-fidelity, coupled simulations of fluid flow, heat transfer, mass transfer, chemistry, electrochemical phenomena, and alloy corrosion for molten salt systems. In FY25, significant developments were made to the code package, enhancing its capabilities for modeling all relevant phenomena within operating moltens salt reactors (MSRs). The developments and activities in FY25 included: 1. Implementation of Level-Set methods to enable modeling of single-bubble behavior in molten salts. In FY25, the Level-Set two-phase flow modeling implementation was improved to simulate single bubble behavior with molten salt media. The large density and viscosity ratios between typical gases and molten salt liquids present challenges for these types of numerical solvers. With enhancements to the pressure projection method, MOSCATO’s Level-Set solver was able to be successfully validated to experiments related to helium bubble rise in stagnant molten salt. The simulated bubble rising velocity showed reasonable good agreement with experimental measurements. The bubble shape and dynamics were also visually compared with experimental snapshots, demonstrating a good qualitative match. 2. Generation of mass transfer correlations for multiphase flow systems. To enable calculations of the tritium transport across the interface between gas bubbles and salt, we modeled high- Schmidt-number mass transfer around a sphere across a broad range of Reynolds numbers. The mesh near the sphere surface was highly refined to resolve steep concentration gradients caused by the low diffusion coefficient. Literature-based mass transfer correlations were compared with the numerical results, and modifications were proposed to improve agreement, particularly at higher Schmidt numbers. These mass transfer correlations were subsequently provided to other national laboratories to help enable high quality mass transfer simulations using lower-order solvers under development within the NEAMS program. 3. Preliminary implementation of a bubbly flow solver. To model bubbly flow in molten salt, we implemented a bubbly flow solver for void fractions less than 5%. To do so, an algebraic relative velocity model that assumes small bubbles with rapid momentum equilibration was added to MOSCATO to compute bubble velocities. Preliminary comparisons with experimental data showed reasonable agreement, and further improvements are underway. 4. Generation of mass transfer correlations for MSRE subchannel The Molten-Salt Reactor Experiment (MSRE) was a landmark historical project that demonstrated the feasibility of molten-salt reactor technology. The MSRE campaign also generated a significant body of experimental data and reports that continue to support molten-salt–related research. In this report, the MSRE core subchannel was used as the reference geometry for a mass transfer study performed with MOSCATO. The geometry and computational mesh were adapted from a previous study, providing adequate resolution for the relatively low Reynolds number in this case. Additional mesh refinement was applied to reach higher Schmidt numbers, enabling the derivation of a reliable mass-transfer correlation for the present scenario. 5. Simulations of oxygen ingressions into molten salt. In the previous fiscal year, we initiated a study to simulate oxygen ingression in stagnant salt. As oxygen enters the salt through its surface, it reacts with Ce 3+ to form solid CeO 2 and other reaction products. To more fully capture the complex diffusion-convection-reaction mechanisms, capabilities for modeling natural convection in the salt vessel were added. These were needed as the flow of the ingressed gas induced flow in the salt caused by surface shear and non-isothermal effects. With these updated physics in place, we were able to successfully reproduce the experimental results for the rate of change of CeCl 3 concentrations versus time. 6. Flow corrosion model validation. In FY24, MOSCATO’s corrosion model was validated against static corrosion experiments. In FY25, this work was extended to a flow corrosion experiment, where FLiNaK salt was driven by natural convection, with initial salt impurities to initiate corrosion. Despite uncertainties in parameters such as elemental diffusion coefficients in the alloy and unknown H + concentrations, the simulations achieved good agreement with experimental results, especially in predicting sample mass losses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

DISSOLUTION OF SURROGATE U-Zr FUEL USING ALNIFLEX CONDITIONS

Non-aluminum clad spent nuclear fuel (NASNF) stored in L Basin at the Savannah River Site (SRS) is widely varied in fuel composition, design, packaging, and physical condition. The complexity of the NASNF inventory presents significant challenges, and technology development is necessary for successful disposition. One such fuel in the inventory is metallic uranium-zirconium (U-Zr) alloy fuel, the focus of this study. Electrolytic or nitric acid only dissolution of metallic U-Zr alloy can form insoluble zirconium oxide, which results in up to 52% loss of U to insoluble solids, and can be subject to potentially uncontrolled oxidation reactions [1, 2]. The AlNiflex process was determined to be a viable dissolution flowsheet for the U-Zr alloy fuel. Under a narrow set of solution concentrations, a combination of hydrofluoric acid (HF), nitric acid (HNO3), aluminum nitrate (Al(NO3)3), and hexavalent chromium can safely dissolve U-Zr intermetallic alloys, keep Zr soluble, and not significantly corrode stainless steel (SS) vessels [3, 4.

Gogolski, Jarrod M. [Savannah River National Labor↗
DOE OSTI2025

Tailoring mechanical properties of a multi-principal element alloy through a multi-length-scale approach

In this study, a multi-length-scale strengthening approach was used to tailor the microstructure and the mechanical properties of a NiCoCr-based multi-principal element alloy (MPEA). Grain size refinement, severe lattice distortion, and stacking fault energy (SFE) reduction with Mo addition (up to 10 at.%) enhance yield strength by 85% with only 10% reduction in ductility in as-annealed MPEAs. A pronounced increase in the strain hardening rate was observed with the addition of Mo, which is ascribed to the promotion of complex stacking fault (SF) interaction and intersection, accompanied by Lomer-Cottrell (L-C) and Hirth locks inhibiting dislocation motion and substantial increase in the accumulation of back stress. To push the limit of the yield strength further, the Suzuki segregation phenomenon was manipulated by a careful control of SF density by pre-straining and a subsequent 500 °C heat treatment. The stress-strain responses of the pre-strained and heat treated MPEAs showed an obvious SF density and Mo concentration dependence. Furthermore, the yield strength of the pre-strained Mo-added MPEAs with subsequent heat treatment was increased up to true stress of 2.3 GPa with a corresponding fracture elongation of 12% true strain. SFs formed during pre-straining served as Cr segregation sites during subsequent heat treatment, which substantially varies the local SFE within the SF, presenting a roughened landscape and frustrating the dislocation dynamics.

mechanical properties↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

GEN3D Experimental and Numerical Development of GEN3 Durability Models

Understanding of the high temperature durability of particles and the materials that contain them is critical to next generation of concentrating solar power (CSP) technology. Here we studied the durability of particles and their containment materials under extreme UV cycling, thermal cycling, and in low-speed high temperature mechanical wear situations. The optical stability of seven candidate particles has been determined following exposure to the high temperature conditions present in a generation 3 particle-based CSP technology. Particle solar weighted absorptance and emittance measured periodically during 10,000 high solar irradiance exposure cycles and up to 400 hours of isothermal aging has been documented. The particle aging due to repeated exposure to concentrated solar flux represented the 30-year lifetime of a power plant. Models were fit to the absorptivity and emissivity data following the isothermal aging provides the projected optical degradation of the particles as a function of temperature. Mechanical wear was studied through the use of custom developed wear testing facilities for measuring high temperature impact wear, abrasion wear, and particle attrition. Additionally, a novel technique for measuring the high temperature mechanical properties of single particles was developed. Through these tests it was observed that high nickel alloys generally showed lower wear than comparable iron based steels, particularly at elevated temperatures of 800°C. Mechanical wear at these temperatures is a highly complex phenomenon combining both mechanical wear and oxidation. Additionally, the containment materials wear rates are influenced by the particles (both hardness and roundness), making the wear mechanisms complex. The initial wear test conducted in the abrasion test rig revealed a substantial amount of oxide materials in the particle bed after testing (in relative to later tests), and substantially more wear, likely indicating a need for concern in startup operation of particle facilities to not incur high wear from the presence of oxides. Particle attrition experiments have only been conducted for a single material but increase size distribution, reduction in circularity, and particle diameter is observed. Efforts to develop predictive models was limited due to a testing campaign that prioritized testing materials for particle pathway developers over building a comprehensive design of experiments. The results discussed in this report inform future CSP developers and researchers further de risking the technology and assisting in its future development. ParticleBased CSP development provides a path to dispatchable solar power generation with storage at a price competitive in the current energy market. Lowering the cost of CSP technology provides a carbon free power generation solution that can assist in the transition from fossil fuels to renewable sources of electricity.

14 SOLAR ENERGY↗
DOE OSTI2025

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

materials science↗