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DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗

DOE OSTI2024

Crack opening calculation in phase-field modeling of fluid-filled fracture: A robust and efficient strain-based method

The phase-field method has become popular for the numerical modeling of fluid-filled fractures, thanks to its ability to represent complex fracture geometry without algorithms. However, the algorithm-free representation of fracture geometry poses a significant challenge in calculating the crack opening (aperture) of phase-field fracture, which governs the fracture permeability and hence the overall hydromechanical behavior. Although several approaches have been devised to compute the crack opening of phase-field fracture, they require a sophisticated algorithm for post-processing the phase-field values or an additional parameter sensitive to the element size and alignment. Here, we develop a novel method for calculating the crack opening of fluid-filled phase-field fracture, which enables one to obtain the crack opening without additional algorithms or parameters. Here we transform the displacement-jump-based kinematics of a fracture into a continuous strain-based version, insert it into a force balance equation on the fracture, and apply the phase-field approximation. Through this procedure, we obtain a simple equation for the crack opening which can be calculated with quantities at individual material points. We verify the proposed method with analytical and numerical solutions obtained based on discrete representations of fractures, demonstrating its capability to calculate the crack opening regardless of the element size or alignment.

58 GEOSCIENCES↗

DOE OSTI2025

Polylactic acid (PLA)-based multifunctional and biodegradable nanocomposites and their applications

Polylactic acid (PLA)-based nanocomposites are emerging as multifunctional, biodegradable materials, offering sustainable alternatives to petroleum-based plastics. This review examines recent advancements in PLA nanocomposites, focusing on enhanced mechanical strength, thermal stability, and biodegradability achieved through nanofillers like metallic particles, carbon-based materials, and ceramics. Techniques such as in situ polymerization, melt mixing, and electrospinning enable application-specific improvements. PLA's limitations, including brittleness and low barrier properties, are addressed to support diverse applications: in packaging (e.g., extended shelf life), biomedicine (e.g., degradable implants), and electronics (e.g., flexible devices). Despite challenges with filler dispersion and thermal resistance, continued innovations expand PLA's potential across multiple industries, contributing to a sustainable, circular economy.

Biodegradable polymers↗

DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Evaluation of methods and improvement of predictions for specification properties of petroleum-based and alternative aviation fuels

To support our research and process modeling for liquid fuels, including blends, from petroleum and synthetic sources such as from biomass intermediates, we evaluated composition-based prediction methods and improved predictions for five key specification properties of petroleum-based and alternative aviation fuels, namely distillation temperatures (10 % distilled, t 10 , and final boiling point, t FBP ), density, flash point, net heat of combustion, and freezing point. The types of fuels included were petroleum-based jet fuels, jet-fuel surrogate mixtures, synthetic blending components obtained from different sources, and blends of Jet A with many synthetic blending components. Expanded datasets to update associated parameters allowed significant improvements for one of the prediction methods used in earlier work, namely the Modified Weighted Average method published initially by Shi et al. By considering the importance of lighter compounds for flash points and heavier compounds for freezing points, the revised Modified Weighted Average method was further improved. For liquid density, the revised Modified Weighted Average method gave the best overall results. The revised Modified Weighted Average method, the American Society for Testing and Materials D7215 method, and the D7215 method modified by another group gave comparable results for flash point, while the revised Modified Weighted Average and D3338 methods gave the best results for net heat of combustion. Freezing point was well predicted using the revised Modified Weighted Average method and showed the most significant improvements over current predictions. Distillation temperature t 10 was not well predicted, while t FBP was predicted with a mean absolute error comparable to experimental reproducibility.

09 BIOMASS FUELS↗

DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗

DOE OSTI2025

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Microstructural features underpinning the mechanical behavior of powder metallurgy Cr-based alloys

Refractory materials such as Cr-based alloys offer the potential of enhanced elevated temperature performance but have been limited by their poor formability. However, powder metallurgy has been shown to be a viable pathway to fabricate these alloys. Nanophase separation sintering (NPSS) in particular has been used in the literature to accelerate the densification of powder metallurgy Cr- and W-based alloys. Here, we explore microstructure evolution during NPSS in a binary Cr 85 Ni 15 alloy consolidated via (i) cold pressing & pressureless sintering and (ii) hot isostatic pressing followed by hot extrusion & hot upsetting, and the role of these different processing routes on resulting material properties. The alloy lacked room temperature tensile ductility regardless of consolidation process, with multi-length scale characterization, including scanning electron microscopy, transmission electron microscopy, atom probe tomography, X-ray diffraction, and uniaxial tensile testing, revealing that brittleness was due to intrinsically poor Cr grain boundary cohesion. Tensile testing conducted at 760 °C showed marked strength reduction for the extruded & upset (94 %) condition compared to the pressed & sintered (18 %). The formation of orthorhombic CrNi 2 intermetallics functioned as precipitate strengtheners and prevented elevated temperature softening in the pressed and sintered condition. The findings offer foundational insights into aiding the future development of Cr-based alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Mechanistic and kinetic relevance of hydrogen and water in CO 2 hydrogenation on Cu-based catalysts

Here, we ally steady-state kinetics, kinetic isotope effects, and density functional theory (DFT) calculations to illustrate that Cu-based catalysts remain saturated by H-adatoms (H*) and molecular formic acid (HCOOH**) during CO 2 hydrogenation. High H* coverage under methanol synthesis conditions is evidenced by reverse water-gas shift (RWGS) rates that exhibit positive H 2 reaction orders only at P H2 ≲ 0.5 bar, above which methanol synthesis and RWGS rates exhibit first and zeroth order dependence on P H2 , respectively. HCOOH** also accumulates on the surface with increasing P CO2 as informed by the Langmuir-type dependence on P CO2 (0.25-23 bar) for both methanol synthesis and RWGS. As both HCOOH** and H* have one H-atom per site occupied, the two species share the same P H2 dependence and give rise to CO 2 reaction orders that are independent of P H2 . Surface coverages determined based on kinetic analyses are further corroborated with DFT-derived adsorption energies that show favorable HCOOH** adsorbate-adsorbate interactions as well as repulsive interactions for bidentate formate (HCOO**) on H*-saturated surfaces. Methanol selectivity remains invariant with P CO2 and P CO despite CO inhibiting reaction rates, thereby demonstrating methanol synthesis and RWGS occur on the same active site. In contrast, water preferentially inhibits methanol synthesis rates, increases methanol synthesis H 2 reaction order from 1.0 to 1.5, and alters the methanol synthesis H 2 /D 2 kinetic isotope effect; the inhibitory effect of H 2 O thus cannot be attributed to competitive adsorption alone and instead reflects a change in the rate-determining step for methanol synthesis. The disparate kinetics of methanol synthesis and RWGS evince a branching pathway where methanol is formed from formates and CO is formed from carboxylates. The presented work thus identifies the relevant surface species, underscores the distinct catalytic role of water in branching methanol synthesis and RWGS pathways, and, in doing so, details a mechanistic picture that yields predictable rates and reaction orders for both methanol synthesis and RWGS on Cu-based CO 2 hydrogenation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Dominant balance-based adaptive mesh refinement for incompressible fluid flows

This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Cobalt Dissolution from Metal Oxides and Battery Cathode Materials with Acetic Acid-Based Deep Eutectic Solvents

Recovery of critical metals with alternative solvents beyond those in traditional pyrometallurgy and hydrometallurgy is needed in consideration of environmental challenges and the growing demand for metals in energy technologies. Deep eutectic solvents (DESs) have emerged as sustainable alternatives for solvometallurgy in metal separation and recovery. In this study, DESs based on hydrogen bond acceptors (HBAs) including choline chloride (ChCl), acetylcholine chloride (AChCl), and betaine (Bet) were investigated for their effectiveness when paired with acetic acid (AA) as the hydrogen bond donor (HBD) for the dissolution of cobalt from cobalt oxide (CoO), lithium cobalt oxide (LiCoO 2 ), and lithium nickel manganese cobalt oxide (LNMC). Based on the spectroscopic analysis of the metal dissolution and coordination, Bet:AA was found to provide the highest solubility for CoO (0.33 M) in the form of an octahedral complex. On the other hand, ChCl:AA solvent was more effective at dissolving LiCoO 2 with 0.04 M Co 2+ corresponding to 17% dissolution efficiency and LNMC with 0.06 M Co 2+ corresponding to 72% dissolution efficiency at 50 °C, compared to Bet:AA (9% for LiCoO 2 and 31% for LNMC). Although the solubilities of LiCoO 2 and LNMC have not significantly improved in ChCl:AA, this difference in effectiveness between the solvents clearly reveals the role of the HBA in solubilization. The coordination synergy between the chloride and the –OH moiety facilitates the breakdown of the LiCoO 2 driven by the alteration of the solvent polarity. Cobalt in these solutions was found dominantly as a tetrahedral [CoCl 4 ] 2– complex. A chemical separation of cobalt oxalate from a mixed-metal oxide system based on Co, Fe, and Ni was also demonstrated, confirming the potential of these solvents for practical metal recovery.

Cobalt separation↗

DOE OSTI2025

Microstructural evolution and hardness changes in ion irradiated nickel-based Haynes 282 superalloy

This study investigated the crucial aspects of thermal and irradiation stability in precipitation hardened Haynes 282 Ni-based superalloy. The Haynes 282 Ni-based alloy was irradiated by 8 MeV Ni 3+ ions to assess its resistance to phase instabilities and mechanical property alterations. The mid-range doses (at a depth of ∼1 µm) were 1 and 10 displacements per atom (dpa) at temperatures of 600°C and 750°C. Nanoindentation tests provided insights into bulk equivalent hardness of the irradiated and pristine regions, while scanning transmission electron microscopy (STEM) and energy dispersive X-ray spectroscopy (EDS) were used to examine the microstructural evolution of irradiation-induced defects and γ′ -Ni 3 (Al, Ti) precipitates and defect structure under irradiation. These precipitates, with an average diameter of 29 nm and a number density of 5 x 10 21 /m 3 , acted as robust dispersion strengthening agents with high radiation point defect sink strength. Remarkably, irradiation did not significantly alter the size or number density of the γ′ precipitates, indicating exceptional thermal stability and radiation resistance of these precipitates at the examined conditions. Radiation-induced dislocation loops were observed at 600°C, albeit without a substantial impact on mechanical properties due to the dominance of γ′ precipitates on the overall alloy strength. The superior stability of γ′ precipitates observed in this study contrasts with several previous research findings on Ni-based alloys that reported poor precipitate stability. Plausible reasons for this difference are discussed. Moreover, this work explicitly outlines a physically grounded approach to ensure accurate microstructure-hardness correlations and clarifies the hardening model by addressing common misapplications of superposition in prior studies.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Intrinsically sodiophilic, mesoporous metal-free wetting layers based on inexpensive carbon black for sodium-metal batteries

In this article, elevated temperature molten Na batteries are seeing a resurgence of interest for low-cost electrochemical energy storage for the grid. Of the many recent innovations in this battery concept, new methods focused on intermediate temperature operation (e.g. 110–190 °C) have gained prominence as a way to enable comparable performance with less thermal energy loss and lower-cost materials of construction. However, the poor wettability of molten Na on suitable solid-electrolyte separators such as sodium Beta Alumina Solid-Electrolyte (Na-β”-Al 2 O 3 , ‘BASE’) requires continued innovation in interface engineering to promote full utilization of the solid-electrolyte surface area and minimize cell resistance. There have been many successful approaches to improve Na-wettability to-date including heat treatment in an inert atmosphere to remove adsorbed surface species, deposition of alloying metals such as Pb, Sn, or Bi, and use of carbon-based interfacial layers. However, these approaches either lack the ability to provide good wetting at very low temperatures (near the melting point of Na) or rely on non-scalable processes and/or toxic/expensive metals. To solve these issues, a new carbon-based sodiophilic treatment is demonstrated, which utilizes inexpensive components to form a meso/macroporous sodiophilic layer, is easily applied via drop-casting or spray-coating, provides excellent wetting as low as 110 °C, and is completely metal-free. It is found that the good sodium wetting can be attributed to the wider range of pore sizes in the carbon layers demonstrated in this study. Na wetting may occur as surface tension is initially broken by larger pores, followed by the intrusion of molten Na into smaller pores due to the apparent intrinsic affinity of Na-metal for carbon surfaces, in conjunction with the capillarity effect. Low cell-level area specific resistances of 20–30 and 13–15 Ω·cm 2 are demonstrated at 110 and 140 °C respectively. Finally, the utility of this metal-free wetting layer for solid-Na anodes is explored, showing that the metal-free wetting layer can reach a critical current density of 1.88 mA·cm -2 at 30 °C.

25 ENERGY STORAGE↗

DOE OSTI2026

Optimizing pressurized-water reactor equilibrium cycle using a novel loading pattern encoding and rule-based genetic crossover operators

This work presents an extended multi-batch approach applied in shuffling scheme optimization for equilibrium cycle for pressurized water reactors using Genetic Algorithms (GAs). A new ruled based GA crossover operator called Inherited Location and Batch (ILB) was introduced to enhance offsprings reproduction efficiency specialized for equilibrium cycle optimization problem. This approach was implemented within the Plant ReLoad Optimization (PRLO) framework and validated using a generic reactor model based on the AP1000 design, with core parameters calculated via the CASMO/SIMULATE software package. The ILB approach is then applied for both single and multi-objective problems in maximizing cycle length and core average exposure while minimizing the average enrichment of the 57 fresh fuel assemblies (FAs) per cycle. The optimal solutions are selected based on their dominance to the objectives from all feasible solutions. This research identified three optimal solutions satisfied safety constraints: The first solution minimizes feed enrichment costs with a cycle length of 338.8 days and core exposure of 25.39 MWd/MT; the second solution extends cycle length to 361.2 days, with the highest core exposure of 26.84 MWd/MT, using 3.75 wt% average fuel enrichment; the third solution balances both objectives with a cycle length of 349.6 days, core exposure of 25.82 MWd/MT with a slight enrichment increase compared to the first solution. Collectively, these findings underscore the efficiency and effectiveness of the proposed approach in achieving practical multi-objective optimal equilibrium cycle designs using GAs optimizer.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Design of novel refractory equiatomic multi-principal elemental alloys based on Mo-Nb-Ti system for Gen IV reactor applications

Excellent irradiation damage resistance demonstrated by multi-principal elemental alloys (MPEAs) has sparked significant interest among researchers, prompting exploration into their vast compositional space, to validate their suitability for nuclear applications. A combined approach of thermodynamic and empirical parameters calculations alongside CALPHAD (CALculation of PHAse Diagrams) for phase formation predictions enable high-throughput material selection for sophisticated applications like nuclear, overcoming laborious and time-consuming experiments. Key thermodynamic and empirical parameters for eight novel equiatomic MPEAs, based on seven low thermal neutron cross section refractory elements, for predicting phase formation were calculated, and equilibrium and non-equilibrium simulations in CALPHAD were employed to comprehensively model the systems. Pseudo binary phase diagram simulations showed that Zr, V or equiatomic CrV additions to the base MoNbTi alloy (MoNbTi-Zr, MoNbTi-V and MoNbTi-CrV alloys) favor the formation of isomorphous body-centered cubic (BCC) phase at high temperatures, while Cr, Al, equiatomic ZrV, or equiatomic CrAl additions (MoNbTi-Cr, MoNbTi-Al, MoNbTi-ZrV or MoNbTi-CrAl alloys) limit the solubility of them. Equilibrium CALPHAD simulations at 750 oC were consistent with XRD results on MoNbTi, MoNbTiZr and MoNbTiCr alloys, and partially for others. Notably, elemental segregation observed in the backscattered electron (BSE) scanning electron microscopy (SEM) images of the alloys was accurately simulated through non-equilibrium Scheil solidification calculations in CALPHAD, further verified by experiments. The precipitation of TiCr2 Laves phase in Cr containing MoNbTiCr and MoNbTiCrAl was accurately predicted while discrepancies were noted in MoNbTiCrV. The equilibrium simulations also provided insights into phase compositions at specific temperatures offering a pathway for tailoring the desired microstructure and properties of these systems. Empirical parameters calculations successfully predicted random solid solution in the base MoNbTi alloy, and with an exception in MoNbTiV and MoNbTiAl, predicted intermetallic precipitation in the rest, especially, Laves phase precipitation in Cr containing alloys.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Control-inspired design and power optimization of an active mechanical motion rectifier based power takeoff for wave energy converters

Ocean waves have high energy density and are persistent and predictable. Yet, converting wave energy to a useable form remains challenging. A significant hurdle is the oscillatory nature of waves resulting in the alternating loads, which necessitate the use of rectification at some stage of the energy conversion. This research effort presents a novel design of active mechanical motion rectifier (AMMR) for the power takeoff (PTO), which provides enhanced controllability and better power performance when compared to passive mechanical motion rectifiers (MMR). Inspired by transistors used in synchronous electrical rectifiers, the proposed design uses controllable electromagnetic clutches in the mechanical transmission to allow active engagement-disengagement control; thus, rectifying the oscillatory motion into a unidirectional rotation for high energy conversion efficiency and allowing the generator in unidirectional rotation to control the bidirectional wave capture structure for maximizing the power output. A semi-analytical computational approach is developed to efficiently evaluate the optimal power achieved using the proposed AMMR-based PTO and active control. It is found that the AMMR-based PTO design yields a higher optimal power than the previous passive MMR design across the wave spectrum. The influences of generator inertia and reactive power are discussed. Furthermore, the effects of control parameters on the power output and the optimal trajectories are analyzed. Wave tank tests with the AMMR prototype demonstrated the effectiveness of AMMR based PTO design and validated the numerical analysis.

16 TIDAL AND WAVE POWER↗