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DOE OSTI2024

Atomic Ordering-Induced Ensemble Variation in Alloys Governs Electrocatalyst On/Off States

The catalytic behavior of a material is influenced by ensembles—the geometric configuration of atoms. Traditional approaches, mainly utilizing solid-solution alloys in electrocatalysis, have often overlooked the challenges posed by concurrent changes in the electronic structure (i.e. d-band center) when the composition is altered. Here, this study introduces a methodology that distinctly separates the geometric effects (i.e. ensembles) from the electronic structure. We compare the reactivity of compositionally identical, but structurally different Pd 3 Bi ordered intermetallic and solid-solution alloys. Remarkably, we find that Pd 3 Bi intermetallics display nearly no reactivity for the methanol oxidation (MOR), while their solid-solution counterparts have significant reactivity. This highlights a unique case where materials with identical chemical compositions demonstrate drastically different catalytic behavior underscoring the critical importance of ensembles in electrocatalysis. Specifically, Pd 3 Bi intermetallics form smaller ensembles (average coordination number: 4.5 ± 1.6) with almost no measurable MOR activity at room temperature, in contrast to the solid-solution Pd 3 Bi that exhibit larger ensembles (average coordination number: 6.8 ± 0.9) and considerable MOR reactivity (0.5 mA cm −2 Pd ). An ordered Pd 3 Bi alloy, with an intermediate ensemble size (average coordination number: 5.3 ± 1.2), displays moderate MOR activity (0.1 mA cm −2 Pd ), further confirming the direct correlation between ensemble size and catalytic activity. Notably, all Pd 3 Bi alloys maintain similar electronic structures, because the chemical composition of the alloys is fixed, indicating that the differences in reactivity are predominantly from changes to the ensemble size. Our findings offer an approach for precisely controlling catalytic activity through manipulating the geometric configuration of the atoms within an alloy, paving the way for more efficient catalyst design.

alloys↗

DOE OSTI2025

Quantifying dislocation-type defects in post irradiation examination via transfer learning

The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Phase-field modeling for restructuring in the dark zone of high burnup UO 2

This report summarizes the mesoscale modeling work performed in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to capture the microstructural evolution and restructuring observed in the dark regions of high burnup UO 2 nuclear fuel. This is the first attempt to realistically simulate the restructuring behavior observed in different region of a high burnup fuel. We employ a grand-potential based phase-field model to concurrently evaluate the formation of subgrains and growth of fission bubbles within the fuel. A energy-based subgrain formation criteria is introduced to simulate the restructuring process. Effect of different initial conditions and different modeling parameters are studies systematically to capture how each of these parameters influence the characteristics of the restructured fuel. It is observed that the subgrain formation begins around existing fission gas bubbles and then proceeds towards triple junctions, grain boundaries and grain interiors. It is demonstrated that restructuring is influenced by a combination of initial dislocation densities, subgrain formation rate, and temperature. Rate of restructuring increases with increase in fuel temperature. A restructuring bias is observed within the microstructure due to variation in defect accumulation among different grains. Furthermore, bubble sizes and distribution does not have a significant effect on rate of restructuring. The predicted microstructures resembles the characteristics of the restructured regions as observed in experiments. Finally, a correlation is presented that demonstrates the evolution of the restructuring volume fraction as a function of local effective burnup. This work provides a first of its kind restructuring model for darkzone that can be used by BISON for performance prediction of high burnup UO 2 fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Severe Dirac Mass Gap Suppression in Sb 2 Te 3 -Based Quantum Anomalous Hall Materials

The quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTIs) when a Dirac mass gap opens in the spectrum of the topological surface states (SSs). Unaccountably, although the mean mass gap can exceed 28 meV (or ~320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr 0.08 (Bi 0.1 Sb 0.9 ) 1.92 Te 3 to that of its nonmagnetic parent (Bi 0.1 Sb 0.9 ) 2 Te 3 , to explore the cause. In (Bi 0.1 Sb 0.9 ) 2 Te 3 , we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr 0.08 (Bi 0.1 Sb 0.9 ) 1.92 Te 3 with concurrent but uncorrelated Dirac mass gap disorder. Additionally, these two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 μeV for nanoscale regions separated by <1 μm. This fundamentally limits the fully quantized anomalous Hall effect in Sb 2 Te 3 -based FMTI materials to very low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Evaluation of a frequency-dependent phase shift in chirped Raman lasers for atom gravimeters

Light-pulse atom gravimetry has emerged as a powerful technique for precisely measuring absolute gravitational acceleration. Raman lasers are commonly used in this technique to coherently manipulate the atomic wave packet, of which the effective frequency is chirped continuously to compensate for the Doppler shift. In this study we investigate an additional phase shift in the Raman lasers that arises due to the frequency chirp. Here, we directly measure this phase shift by recording the beat signal of the Raman lasers with a high-speed oscilloscope, providing an independent evaluation of the resultant error in atom gravimeters caused by this phase shift. Concurrently, we detect the influence of the additional phase shift on gravity measurements using our atom gravimeter, and the results are in good agreement with the independent evaluation. Notably, there may still be a residual error at the microgal level when performing differential measurements by reversing the direction of the effective wave vector of the Raman lasers. We propose that this residual error can in principle be eliminated by selecting the same chirp frequency range for both directions in the differential measurement.

Xu, Yaoyao [Huazhong Univ. of Science and Technolo↗

DOE OSTI2023

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $\$$/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Mesoscale modeling of restructuring in high burnup UO 2 fuel

Here, this work aims to simulate the restructuring behavior observed in different regions of high burnup fuel, providing a first-of-its-kind restructuring model for the dark zone and rim region of high-burnup UO 2 fuel. We employed a grand-potential-based phase-field model to concurrently evaluate subgrain formation and the growth of fission gas bubbles within the fuel. An energy-based subgrain formation criterion was introduced to simulate the restructuring process. The effects of different initial conditions and different modeling parameters were systematically studied to capture how each of these parameters influences the characteristics of the restructured fuel. Subgrain formation was observed to begin around existing fission gas bubbles and proceed toward triple junctions, grain boundaries, and grain interiors. Restructuring was demonstrated to be influenced by a combination of initial dislocation densities, burnup rate, subgrain formation rate, and temperature. Under a given subgrain formation rate, the rate of restructuring increases with rising fuel temperature. A restructuring bias was observed within the microstructure, due to the variation in defect accumulation when comparing different grains. Microstructures corresponding to the dark zone and rim region can be obtained by parameterizing the model with the appropriate defect production rate, as determined based on the burnup rate and temperature. Furthermore, bubble size and distribution do not significantly affect the rate of restructuring. The predicted microstructures are consistent with experimental observations of the restructured regions. Finally, we present a correlation demonstrating the evolution of the restructuring volume fraction as a function of local burnup.

UO2↗

DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗

DOE OSTI2026

Reacting CO 2 with Light Alkanes to Value-Added Products

Catalytic conversion of anthropogenic carbon dioxide (CO 2 ) into value-added products is a promising strategy to mitigate global carbon emissions. Concurrently, the shale gas revolution has provided an abundant supply of light alkanes (methane, ethane, propane, and butane), presenting a unique opportunity to employ these underutilized hydrocarbons as an effective, low-cost hydrogen source for CO 2 reduction. In this Perspective, we summarize past efforts, current state, and future opportunities for reacting CO 2 with light alkanes to generate a diverse range of value-added products. Compared with direct alkane conversion, the introduction of CO 2 fundamentally alters reaction thermodynamics and kinetics, enabling selective C–H and C–C bond activation while suppressing catalyst deactivation from coke formation. Building on decades of research in dry reforming and CO 2 -assisted dehydrogenation, recent advances in catalyst design have enabled CO 2 -assisted dehydrogenation processes that approach chemical equilibrium for the selective production of olefins and syngas. Importantly, advances in catalyst design and reactor engineering have further expanded the product scope beyond gas-phase (syngas and olefins) to include liquid-phase (oxygenates and aromatics), and solid-phase products (carbon nanomaterials). We highlight key catalyst design principles for controlling reaction pathways and discuss major challenges and opportunities in developing selective and versatile platforms for the simultaneous upgrading of CO 2 and light alkanes.

CO2↗

DOE OSTI2023

Optimal Power Sharing Speed Compensation in On-road Wireless EV Charging Systems

Dynamic wireless charging of electric vehicles (EV) is an emerging technology with the potential to address range anxiety and reduce the size of batteries or provide charge-sustaining operation. Charging demand for dynamic wireless charging systems (DWCS) varies greatly in response to location-specific traffic behaviors including the number and speed of vehicles. This paper highlights the potential reduction of load variation with speed compensation and simulates opportunities to maximize the number of cars charged concurrently through “power sharing” or altering power output to slower cars while maintaining a maximum delivered energy. An improved sub-minute model for synthetic traffic is proposed to effectively model DWCS load on high speed roadways and a power electronics model is created based on an existing prototype developed by ORNL to investigate the potential for power sharing. Reductions in the average speed of traffic can greatly increase instantaneous DWCS load by as much as 26%, complicating capacity sizing. Parametric studies with a LCC-S power electronics model shows feasible power reduction of more than 20%, while maintaining a maximum achievable efficiency greater than 90%. Speed compensation on a roadway with large speed variation can reduce average power expected by 21% and increase the maximum number of cars charged simultaneously by 30%. The application of power sharing may significantly reduce load variability due to speed, allow for increased car hosting capability, and guarantee a maximum energy delivered.

Lewis, Donovin D.↗

DOE OSTI2024

Post Irradiation Examination Dislocation Defect Detection Software

This software provides dislocation-type defect identification and segmentation using a standard open source computer vision model, YOLOv8, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of expert annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on multiple alloys. It includes multiple layers of frozen layers used for transfer learning from multidisciplinary data and is extensible to alloys that are not included in the training dataset.

Anderson, MatthewW↗

DOE OSTI2025

Mesoscale Modeling for Restructuring and Fragmentation in High Burnup UO 2

This report summarizes the mesoscale modeling conducted in fiscal year 2025 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, focusing on the microstructural evolution and restructuring in high burnup UO 2 nuclear fuel and its impact on fuel fragmentation. We developed a pioneering phase-field model to simulate restructuring behavior across different regions of high burnup fuel, including the dark zone and rim region. A grand-potential-based phase-field model is employed to concurrently evaluate subgrain formation and the growth of fission gas bubbles within the fuel. An energy-based subgrain formation criterion was introduced to simulate the restructuring process. The effects of temperature and burnup rate were studied to capture how each of these parameters influences the characteristics of the restructured fuel. Subgrain formation was observed to initiate around existing fission gas bubbles and proceed toward triple junctions, grain boundaries, and grain interiors. Under a given subgrain formation rate, the rate of restructuring increases with rising fuel temperature. The restructuring occurs faster with higher burnup rate. A restructuring bias was observed within the microstructure, due to the variation in defect accumulation when comparing different grains. Microstructures corresponding to the dark zone and rim region can be obtained by parameterizing the model with the appropriate defect production rate, as determined based on the burnup rate and temperature. The predicted microstructures are consistent with experimental observations of the restructured regions. Based on the mesoscale simulations, a mechanistic model for restructuring and grain size evolution was implemented in BISON. Thus, this work provides a first-of-its-kind restructuring model for different regions of high-burnup fuel to BISON. It was shown that the model predicts appropriate grain size evolution along fuel radius as those observed in experiments. This model enables BISON to predict the effect of restructuring on the fission gas release. Finally, the phase-field fracture simulation with dark zone specific microstructures were presented to provide the fragmentation criteria for the dark zone. This work introduces a first-of-its-kind restructuring model for high burnup fuel, significantly enhancing BISON's predictive capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Assessing microbial systems and process configurations for improved ethanol production from sugary stovers by integrating soluble sugars and holocellulose

Here, this study evaluated microbial systems and technological approaches to configure the whole slurry co-fermentation process for ethanol biosynthesis from a novel stover system rich in sugars. Two approaches, namely separate and simultaneous hydrolysis and co-fermentation (SHCF and SSCF, respectively), were investigated using Escherichia coli monoculture and E. coli-yeast coculture. The SSCF with E. coli monoculture produced 32.75 g/L ethanol, representing only 44.87% yield, which left 65.13 g/L of total sugars unconverted and exhibited limited xylose consumption. Subsequently, a coculture-based SHCF significantly enhanced sugar consumption, leading to increase in ethanol yield and concentration to 66.94% and 48.86 g/L, respectively. Nevertheless, xylose utilization remained minimal due to the preference for glucose and the inhibitory effects of certain compounds. Thereafter, modification of the medium composition by supplementing betaine and sodium metabisulfite improved ethanol production to 53.18 g/L by reducing the toxic effects of inhibitors. Finally, a dual-phase SSCF (DP-SSCF) was explored by allowing the consumption of sugars from the pretreated slurry in the first phase, followed by concurrent cellulose hydrolysis and utilization of the resulting glucose in the second phase. This strategy increased ethanol titer to 63.14 g/L, with 84.3% yield and 0.88 g/L/h productivity.

09 BIOMASS FUELS↗

DOE OSTI2023

Porous Semiconducting K–Sn–Mo–S Aerogel: Synthesis, Local Structure, and Ion-Exchange Properties

Chalcogenide-based aerogels are emerging porous semiconducting nanomaterials that appeal to applications in clean energy and the environment. Here, we report a novel gel, potassium–tin–molybdenum–sulfides (KTMS), that integrates the electrostatically bound K + ions in the covalent network of Sn–Mo–S. Its gelation requires a concurrent reduction of Mo 6+ → Mo 4+/5+ and the oxidation of S 2– → Sn – (n ≈ 1) and Sn 2+ → Sn 4+ . KTMS is an amorphous semiconductor showing quantum confinement effects on band gap energies, 2.1 → 1.4 → 0.9 eV for its wet- → aero- → xerogels. Synchrotron X-ray pair distribution function (PDF) and extended X-ray absorption fine structure (EXAFS) revealed a complex local structure of KTMS consisting of molecular Mo 2 (S 2 ) 6 and Mo 3 S(S 2 ) 6 clusters. In addition, the Sn–S coordination is related to crystalline Na4Sn3S8 and SnS2. KTMS also demonstrated the removal of the radionuclides of Cs + , Sr 2+ , and UO 2 2+ from ppm to ppb levels with distribution constants (Kd) up to ≥104 mL/g. Notably, despite the lack of atomic periodicity in the amorphous KTMS, the K+ ion is ion-exchangeable with chemically diverse Sr 2+ , Cs + , and UO 2 2+ in aqueous solutions; especially the ion-exchange properties of Sr 2+ and UO 2 2+ ≡(O=U=O) 2+ is not known to any chalcogels known to date. The sequestration of Cs + and Sr 2+ was achieved by the exchange of K + in the amorphous KTMS, and the removal of [O=U 6+ =O] 2+ synergistically involves surface sorption via -S····U 6+ =O 2 2+ covalent interactions and ion-exchange via the hard–soft Lewis acid–base paradigm. Overall, cooperative roles played by the diverse bonding motifs, surface-exposed Lewis basic frameworks, and polarizability of the (poly)sulfides make it an exceptional adsorbent for chemically diverse radioactive species. This finding will guide the design of superior sorbents for chemically distinct metal ion separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Entanglement properties of disordered quantum spin chains with long-range antiferromagnetic interactions

Entanglement measures are useful tools in characterizing otherwise unknown quantum phases and indicating transitions between them. Here we examine the concurrence and entanglement entropy in quantum spin chains with random long-range couplings, spatially decaying with a power-law exponent $\textit{α}$. Using the strong disorder renormalization group (SDRG) technique, we find by analytical solution of the master equation a strong disorder fixed point, characterized by a fixed point distribution of the couplings with a finite dynamical exponent, which describes the system consistently in the regime $α > \frac{1}{2}$ . A numerical implementation of the SDRG method yields a power-law spatial decay of the average concurrence, which is also confirmed by exact numerical diagonalization. However, we find that the lowest-order SDRG approach is not sufficient to obtain the typical value of the concurrence. We therefore implement a correction scheme which allows us to obtain the leading-order corrections to the random singlet state. This approach yields a power-law spatial decay of the typical value of the concurrence, which we derive both by a numerical implementation of the corrections and by analytics. Next, using numerical SDRG, the entanglement entropy (EE) is found to be logarithmically enhanced for all $\textit{α}$, corresponding to a critical behavior with an effective central charge $\textit{c}$ = ln(2), independent of $\textit{α}$. This is confirmed by an analytical derivation. Using numerical exact diagonalization (ED), we confirm the logarithmic enhancement of the EE and a weak dependence on $\textit{α}$. For a wide range of partition size $\textit{l}$, the EE fits a critical behavior with a central charge close to $\textit{c}$ = 1, which is the same as for the clean Haldane-Shastry model with a power-law-decaying interaction with $\textit{α}$ = 2. Only for small $l \ll L$, in a range which increases with the number of spins N, we find deviations which are rather consistent with the strong disorder fixed point central charge $\textit{c}$ = ln(2). Furthermore, we find using ED that the concurrence shows power-law decay, albeit with smaller power exponents than obtained by SDRG. Finally, we also present results obtained with DMRG and find agreement with ED for sufficiently small $\textit{α}$ < 2, whereas for larger $\textit{α}$ DMRG tends to underestimate the entanglement entropy and finds a faster decaying concurrence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Effects of turbulent diffusion and back-reaction on the dust distribution around two resonant planets

In evolved and dusty circumstellar discs, two planets with masses comparable to Jupiter and Saturn that migrate outwards while maintaining an orbital resonance can produce distinctive features in the dust distribution. Dust accumulates at the outer edge of the common gas gap, which behaves as a dust trap, where the local dust concentration is significantly enhanced by the planets’ outward motion. Concurrently, an expanding cavity forms in the dust distribution inside the planets’ orbits, because dust does not filter through the common gaseous gap and grain depletion in the region continues via inward drifting. There is no cavity in the gas distribution because gas can filter through the gap, although ongoing gas accretion on the planets can reduce the gas density in the inner disc. Such behaviour was demonstrated by means of simulations neglecting the effects of dust diffusion due to turbulence and of dust backreaction on the gas. Both effects may alter the formation of the dust peak at the gap outer edge and of the inner dust cavity, by letting grains filter through the dust trap. We performed high-resolution hydrodynamical simulations of the coupled evolution of gas and dust species, the latter treated as pressureless fluids, in the presence of two giant planets. We show that diffusion and backreaction can change some morphological aspects of the dust distribution but do not alter some main features, such as the outer peak and the expanding inner cavity. Furthermore, these findings are confirmed for different parametrizations of gas viscosity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Enhancing fatigue life of aluminum alloy castings through cavitation water jet peening: Experiments and simulations

This study presents an investigation into the enhancement of the fatigue life of aluminum castings through the application of cavitation water-jet peening (CWJP). CWJP harnesses the impacts of water cavitation to induce surface compressive residual stress within metallic materials. In this work, CWJP was applied to a high pressure die-cast (HPDC) Al–Si alloy A380 with three different water-jet traverse velocities. The fatigue-life improvement, evaluated in a 4-point bending configuration (stress ratio R = 0.1), was found to vary with applied stress level and ranges from 1.6 to 10 times that of the parent alloy. The data also shows that decreasing the traverse velocity results in greater compressive residual stresses within the surface layer and a concurrent increase in surface roughness. This residual stress layer extends to a depth of 400 μm below the surface, as confirmed by through-thickness residual stress and microhardness measurements. CWJP treatment effectively slows down fatigue crack propagation, as evidenced by microstructural observations of narrower striation spacing. Simulations reveal that compressive residual stresses, in addition to surface hardening during CWJP, are key to improving fatigue life. A 20% increase in surface hardness and 150 MPa compressive residual stress imposed by CWJP process provides an average 5-fold enhancement of fatigue life across different stress levels. This study demonstrates the potential of CWJP as an effective surface treatment to enhance the fatigue life of aluminum castings, such as HPDC components for automotive applications.

Al casting↗

DOE OSTI2024

Evaluation of Additively Manufactured Monolithic SiC and SiC-Matrix Ceramic Matrix Composites for Concentrating Solar Receiver Applications - Fabrication and Testing of Receiver Design Feature Specimens in a Simulating Lab Test via Laser Heating

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Owing to its high temperature stability combined with excellent thermal and optical properties, SiC has been the material of choice for application in high-temperature solar receivers. We report the results of our study of the effective heat transfer characteristics of several candidate SiC structure motifs, or feature geometries, which are fabricated via additive manufacturing. The SiC structure motifs studied include different permutations of three-dimensional periodic lattices and defined shapes. A solar-thermal simulating laboratory test setup is constructed using a 4kW CO2 laser system with beam shaping optics to apply concurrent radiative heating power on one face of 2”-diameter cylindrical feature specimens, representing the structure motifs of interest for receiver element design, while flowing through the sample as heat transfer fluid. Using the test setup, simulative test conditions representative of a concentrated solar flux of up to ~2000 suns could be achieved in the lab tests under varying air flow through the test structure. A simple 1D numerical analysis scheme is developed to extract an effective or compound heat transfer coefficient representative of the test structure under steady-state heat flow conditions. The test results and their use to guide the selection and optimization of SiC material and structure motifs for the receiver element design fabrication are discussed.

14 SOLAR ENERGY↗