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At least 919 records · Page 51

A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.

additive manufacturing↗

Validation and optimization of batch and continuous particle separation processes

Recent advances in simulation capabilities coupled with advanced manufacturing can be leveraged to create highly efficient compact reactors with low emissions, low cost, high reliability, and high flexibility for energy conversion. This type of reactor is especially important for conversion of bio-mass, a storable, renewable and fast-growing energy source, considering the distributed nature of raw material and cost of biomass collection and transportation. To achieve this goal, it is critical to developing new process technologies that are significantly more efficient than traditional processes for which the added manufacture provides a fast way of realization and evaluation. Based upon the flagship MFiX multiphase computational fluid dynamic software suite, NETL has developed an Optimization Toolset to facilitate the CFD simulation based reactor design and optimization, and process intensification. This technical report presents the recent development and application of the Optimization Toolset and the framework for process optimization of particle separation in batch and continuous processes. In these processes, a fluidized bed reactor under proper operating condition was used to separate particles of different physical properties. MFiX-DEM simulations were conducted for a batch system to simulate the process covering a wide range of superficial gas velocities from which the optimal operational parameter can be determined through analyzing the system response. After successful demonstration of the framework, a continuous system for particle separation was designed based on CFD simulations. Similar optimization exercises were conducted for the continuous particle separation considering multiple operating parameters. The design was then fabricated in the laboratory through 3D printing for testing. The particle separation performance was carefully measured through high-speed video for particle tracking. Quantitative comparison between numerical results and experimental measurements were conducted for confirmation and validation.

30 DIRECT ENERGY CONVERSION↗

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗

A Novel Multi-Spacecraft Interplanetary Global Trajectory Optimization Transcription

As the frontier of space exploration continues to advance, so does the design complexity of future interplanetary missions. One avenue of this increasing complexity includes a class of designs known as "Distributed Spacecraft Missions"; missions where multiple spacecraft coordinate to perform shared objectives. Current approaches for the global trajectory optimization of these Multi-Vehicle Missions (MVMs) are prone to shortcomings including laborious iterative design, considerable human-in-the-loop effort, treatment of the multi-vehicle problem as multiple separate trajectory optimization subproblems (resulting in suboptimal solutions where the whole is less than the sum of its parts), and poor handling of coordination objectives and constraints. There are only a handful of software platforms in existence capable of fully-automated, rapid, interplanetary mission and systems global optimization including the Parallel Global Multiobjective Optimizer (PaGMO), the Gravity Assisted Low-thrust Local Optimization Program (GALLOP), and the Evolutionary Mission Trajectory Generator (EMTG). However, none of these tools is capable of performing such tasks for MVM designs. The work outlined in this paper lays the groundwork for a technique to begin addressing these shortcomings. We present a fully-automated technique which frames interplanetary MVMs as Multi-Objective, Multi-Agent Hybrid Optimal Control Problems (MOMA HOCP). First, the basic functionality of this technique is validated on the single-vehicle problem of reproducing the Cassini interplanetary cruise.

Interplanetary↗

A Novel Multi-Spacecraft Interplanetary Global Trajectory Optimization Transcription

As the frontier of space exploration continues to advance, so does the design complexity of future interplanetary missions. One avenue of this increasing complexity includes a class of designs known as ``Distributed Spacecraft Missions"; missions where multiple spacecraft coordinate to perform shared objectives. Current approaches for the global trajectory optimization of these Multi-Vehicle Missions (MVMs) are prone to shortcomings including laborious iterative design, considerable human-in-the-loop effort, treatment of the multi-vehicle problem as multiple separate trajectory optimization subproblems (resulting in suboptimal solutions where the whole is less than the sum of its parts), and poor handling of coordination objectives and constraints. There are only a handful of software platforms in existence capable of fully-automated, rapid, interplanetary mission and systems global optimization including the Parallel Global Multiobjective Optimizer (PaGMO), the Gravity Assisted Low-thrust Local Optimization Program (GALLOP), and the Evolutionary Mission Trajectory Generator (EMTG). However, none of these tools is capable of performing such tasks for MVM designs. The work outlined in this paper lays the groundwork for a technique to begin addressing these shortcomings. We present a fully-automated technique which frames interplanetary MVMs as Multi-Objective, Multi-Agent Hybrid Optimal Control Problems (MOMA HOCP). First, the basic functionality of this technique is validated on the single-vehicle problem of reproducing the Cassini interplanetary cruise.

Napier, Sean W.↗

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

Water‐Assisted Liftoff of Polycrystalline CdS/CdTe Thin Films Using Heterogeneous Interfacial Engineering

Abstract Recent advances in device design and process optimizations have enabled the production of CdTe devices on flexible substrates, but the necessary high‐temperature processing (>450 °C) to recrystallize grains limits the use of alternative lightweight substrates. Here, a new synthesis method is reported to create a freestanding CdS/CdTe film by combining high‐temperature depositions (CdS/CdTe on Si/SiO 2 ) and a simple lift‐off process in a water environment at room temperature. Analysis of the results indicate that the delamination is facilitated by the innate lattice mismatch as well as the presence of an unexpected Te‐rich layer (≈20 nm), which accumulates on the SiO 2 surface. High‐resolution electron microscopy and spectroscopy measurements confirm that the CdS/CdTe film is physically liberated from the substrate without leaving any residue, while also preserving their initial structural and compositional properties.

Magginetti, David J.↗

High-precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this work presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high-frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. Finally, the mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

42 ENGINEERING↗

Evaluation of electric vehicle component performance over eco-driving cycles

We report that battery electric vehicles (BEVs) are a critical pathway towards achieving energy independence and meeting greenhouse and criteria pollutant gas reduction goals in the current and future transportation sector. Emerging connected and automated vehicle (CAV) technologies further open the door for developing innovative applications and systems to leverage vehicle efficiency and substantially transform transportation systems. Therefore, we present a simulation study of various BEV types and compare the performance when driving on real-road drive cycles to highly optimized eco-driving cycles using advanced CAV technologies. Additionally, the results demonstrate that eco-driving has a high potential to reduce energy consumption for all types of BEVs considered. The investigated BEVs include a compact vehicle, a transit city bus, and a Class 7 delivery truck. The impact of eco-driving on conventional vehicles was also compared to comparable BEVs. Compared to the BEVs, eco-driving provides a larger reduction in the conventional vehicle's braking energy loss, and also provides conventional vehicles with greater reductions in the engine mechanical energy output but the fuel savings did not show a consistent trend among all the conventional vehicle types. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

An approach for fast and accurate simulation of phase change material based thermal energy storage in buildings

Latent heat thermal energy storage (LHTES) has significant potential for mitigating peak electricity demand and enabling load shifting in buildings. Phase Change Material embedded heat exchangers (PCM-HX) can significantly improve energy demand management due to high storage capacity. However, PCM-HX evaluation typically depends on computationally expensive fully transient simulations, posing significant challenges for scalable system- and building-level energy assessments across different climates and system architectures. This paper presents a generalized, accurate, and computationally efficient methodology for simulating building energy systems integrated with LHTES. The PCM-HX transient performance is represented by performance maps generated using a Generalized Resistance-Capacitance Model (GRCM) that enables accurate predictions of arbitrary PCM-HXs at low computational cost. The feasibility of the proposed approach was verified using a case study considering a dual-mode heat pump-thermal energy storage (HP-TES) system simulated in Modelica with Spawn of EnergyPlus™ for a DOE prototype small office building in two locations: Tampa, FL, and International Falls, MN. The PCM-HX performance maps provided accurate predictions of PCM-HX transient behavior, with mean absolute percentage deviations within 2–4% compared to GRCM while also achieving at least 1800× reduction in computational time. Moreover, the HP-TES system achieved energy savings of up to 17.4% in Tampa, FL, and 62.2% in International Falls, MN, demonstrating the broader applicability of the proposed methodology across different climate zones. This work highlights the importance of robust PCM-HX models in enabling accurate and computationally efficient building-level simulations and enabling future research opportunities for investigating optimized HP-TES designs and advanced control strategies for grid-interactive buildings.

Modelica↗

A life cycle assessment of e-hydrogen production using proton-exchange membrane water electrolysis coupled with desalination in Saudi Arabia

Hydrogen, considered a crucial element in the transition towards a sustainable energy future, offers the potential to mitigate greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. Here, this study explores the viability of hydrogen production using proton exchange membrane water electrolysis (PEMWE) as a key driver of decarbonization within the Vision 2030 framework in the Kingdom of Saudi Arabia. A first-of-a-kind life cycle assessment (LCA) of electrolytic hydrogen (e-hydrogen) production using PEMWE in the Kingdom is performed. As the hydrogen will be produced in a freshwater scarce region, the inclusion of water desalination processes adds an important dimension to the assessment, reflecting the local context and resource availability. Two main renewable energy scenarios are assessed: solar energy through photovoltaics (PV) and wind energy through onshore turbines. The global warming potential (GWP) results indicate a GHG emissions reduction of up to 95 % compared to the state-of-the-art steam methane reforming process if the electrolysis process is powered exclusively by renewable electricity. The scenarios powered by solar and wind energy result in 3.66 and 0.76 kg CO 2 eq/kg H 2 , respectively. The metal depletion is assessed to consider the requirement of rare materials, with a 7.19 × 10 −2 kg Cu eq/kg H 2 for the solar scenario and 2.82 × 10 −2 kg Cu eq/kg H 2 for the wind scenario. A contribution analysis reveals that the majority of emissions in both scenarios originate from the electricity used for electrolysis, with the electrolyser itself contributing minimally. The absolute impact of the water desalination process is the same in both scenarios; however, it appears more prominent in the wind-powered case due to the significantly lower overall emissions in that scenario. The findings underscore the importance of renewable energy integration and process optimization in minimizing environmental impacts and advancing the sustainability of e-hydrogen production.

08 HYDROGEN↗

Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code

This paper discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package developed and maintained at Oak Ridge National Laboratory. It has been developed to scale well from laptop to small computing clusters to advanced supercomputers. Special features of Shift include hybrid capabilities for variance reduction such as CADIS and FW-CADIS, and advanced parallel decomposition and tally methods optimized for scalability on supercomputing architectures. Shift has been validated and verified against various reactor physics benchmarks and compares well to other state-of-the-art Monte Carlo radiation transport codes such as MCNP5, CE KENO-VI, and OpenMC. Some specific benchmarks used for verification and validation include the CASL VERA criticality test suite and several Westinghouse AP1000 ® problems. These benchmark and scaling studies show promising results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Heterogeneous catalysis is at the heart of chemical industry. Being able to tune and design efficient catalysts for processes of interest is of an utmost importance, and for this, the molecular-level understanding of heterogeneous catalysts is the first step, and indeed a prime focus of the modern catalysis research. For a long time, a single most thermodynamically stable structure of the catalytic interface attained in reaction conditions had been envisioned as the reactive phase. However, some catalytic interfaces continue to undergo structural dynamics in the steady state, triggered by high temperatures, pressures, and binding and changing reagents. Among particularly dynamic interfaces are such widely-used catalysts as crystalline and amorphous surfaced supporting (sub-)nano metallic clusters. Recently, it became clear that this dynamic fluxionality causes the supported clusters to populate many distinct structural and stoichiometric states in catalytic conditions. Hence, the catalytic interface should be viewed as an evolving statistical ensemble of many (not one) structures. As such, every member in the ensemble contributes to the properties of the catalyst differently, and in proportion to its probability to be populated. This new notion flips the established paradigm and calls for new theory, modeling approaches, operando measurements, and updated design strategies. The statistical ensemble nature of surface-supported sub-nano cluster catalysts can be exemplified by oxide-supported and adsorbate-covered Pt, Pd, Cu, CuPd clusters, catalytic toward oxidative and non-oxidative dehydrogenation. They have access to a variety of 3D and quasi-2D shapes. The compositions of their thermal ensembles are dependent on the cluster size, leading to size-specific catalytic activities and the famous “every atom counts” phenomenon. The support and adsorbates affect catalyst structures, and state of the reacting species causes the ensemble to change in every reaction intermediate. The most stable member of the ensemble dominates the thermodynamic properties of the corresponding intermediate, whereas the kinetics can be determined by more active but less populated metastable catalyst states, and that suggests that many earlier studies might have overlooked the actual active sites. Both effects depend on the relative time-scales of catalyst restructuring and reaction dynamics. The catalyst may routinely operate off-equilibrium. Ensemble phenomena lead to surprising exceptions from established rules of catalysis, such as scaling relations, and Arrhenius behavior. Catalyst deactivation is also an ensemble property, and its extent of mitigation can be predicted through the new paradigm. These findings were enabled by advances in theory, such as global optimization and subsequent utilization of multiple local minima, and pathways sampling, as well as operando catalyst characterization. The fact that the per-site and per-species resolution is needed for the description and predicting of catalyst properties gives theory the central role in catalysis research, as most experiments provide ensemble-average information and cannot detect the crucial minority species that may be responsible for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗

Reaction Mechanism of Electrodeposited ε-MnO 2 : A Proton-Centered Pathway in Aqueous Zn-Ion Systems

Aqueous Zn/MnO 2 batteries have garnered significant interests owing to their abundance, high theoretical specific capacity, safety, and low cost. However, large-scale application of these systems is limited by the incomplete understanding of the MnO 2 reaction chemistry. The different crystal lattice structures among MnO 2 polymorphs contribute to the variations in reported reaction mechanisms. Among them, ε-MnO 2 polymorph, the dominant phase in electrolytic manganese dioxide (EMD), is notably observed during the charge cycles of aqueous Zn/MnO 2 batteries. Here, in this work, we investigate the electrochemical behavior of an ε-MnO 2 cathode synthesized via electrodeposition from a ZnSO 4 and MnSO 4 electrolyte, onto a 3-dimensional carbon cloth substrate. Proton intercalation emerges as the dominant charge storage mechanism, critically enabling the reversibility of ε-MnO 2 during cycling, as revealed by operando synchrotron X-ray diffraction and X-ray absorption spectroscopy. Additionally, a proton-coupled dissolution/redeposition pathway operates alongside minor Zn 2+ intercalation, as quantified by Rietveld refinement. Morphological and chemical heterogeneities are studied by transmission X-ray microscopy further validates this reaction mechanism. These mechanistic insights provide the foundation for rationally designing Zn/MnO 2 batteries with optimized proton dynamics and charge transfer, advancing these systems as a viable solution for safe, cost-effective grid-scale energy storage.

36 MATERIALS SCIENCE↗