Neutron Imaging at LANSCE: Characterizing Nuclear Materials for Next Generation Reactor Designs.
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Long, Alexander Makenzie↗
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Results for “Generative Design”
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Neutron Imaging at LANSCE: Characterizing Nuclear Materials for Next Generation Reactor Designs
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Long, Alexander Makenzie↗
Designing Counterfactual Generators On-the-Fly
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Computer science↗
Designing Counterfactual Generators using Deep Model Inversion
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Computer science↗
Advanced multimaterial shape optimization methods as applied to advanced manufacturing of wind turbine generators
Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.
17 WIND ENERGY↗
Enhanced Integer Permutation based Genetic Algorithm for Optimization of Tube-Fin Heat Exchanger Circuitry with Splits and Merges
Tube-fin heat exchangers (HXs) are widely used in air-conditioning and heat pump applications. The performance of these heat exchangers is strongly influenced by the refrigerant circuitry. Studies have proved that by optimizing the refrigerant circuitry, the performance of HXs can be significantly improved. In our previous research, an Integer Permutation based Genetic Algorithm (IPGA) was developed to obtain the optimal circuitry designs. Our previous research showed that IPGA demonstrates superior capability to obtain better refrigerant circuitries with lower computational cost than the other methods in literature. And the optimal circuitry designs obtained from IPGA are manufacturable with the available tooling. However, the IPGA developed previously cannot generate designs with splitting and merging of circuits. To remedy this limitation, a new chromosome which can represent circuitry with splitting and merging of circuits is developed. In addition to the six genetic operators implemented previously, two new genetic operators are developed to generate splits and merges. As a result, the enhanced IPGA can explore the solution space more thoroughly than the previous IPGA. A case study using an evaporator from an A-type indoor unit shows that, given the similar capacity improvements obtained from the enhanced IPGA compared with the previous IPGA, the refrigerant pressure drop reduction obtained from the enhanced IPGA is 26.5% compared against 1.0% pressure drop reduction from the previous IPGA. The benchmark of the enhanced IPGA with other methods in literature demonstrates that the enhanced IPGA can generate circuitry designs with performance superior to those obtained from other methods.
LI, Zhenning↗
Multi-Objective design of interlocking metasurfaces using conditional diffusion models
Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.
Conditional diffusion models↗
Design, Control and Application of Next Generation Qubits
Design, Control and Application of Next Generation Qubits Arun Bansil, Northeastern University (Principal Investigator) Claudio Chamon, Boston University (Co-Investigator) Adrian Feiguin, Northeastern University (Co-Investigator) Liang Fu, MIT (Co-Investigator) Eduardo Mucciolo, Univ. of Central Florida (Co-Investigator) Qimin Yan, Temple University (Co-Investigator) The quest for developing technologies for manipulating and storing information quantum mechanically is currently led by approaches that include Josephson-junctions, ion-traps, and qubits generated by defect spins in solids. Topological qubits, however, are inherently more robust to decoherence by environmental effects, and should be able to sprint ahead once practical barriers have been overcome. At the present stage of the development of the field, it is important to explore a variety of architectures and materials beyond the conventional paradigms in order to seed breakthroughs toward building a scalable quantum computer. Our comprehensive theoretical research program involved four interconnected thrusts as follows. • A materials discovery effort in two-dimensional compounds in search of materials to support Majorana zero modes and defect structures suitable as qubits. • Exploration of architectures for topological quantum computation by investigating both superconducting Majorana qubits, and robust platforms for braiding with new “meta-materials” built of arrays of Majorana qubits. • Investigation of properties of hybrid metal-organic qubits based on transition-metal centers in graphene, and molecular crystals of polyaromatic complexes with embedded transition-metal atoms. • Development of tensor-network and semiclassical approaches to study decoherence in the presence of random and dispersive spin baths, and NV centers in diamond. The full spectrum of theoretical and numerical approaches was used to address the goals of this project including first-principles, density-matrix-renormalization group, tensor networks, and data-driven high-throughput approaches using materials database and machine-learning.
36 MATERIALS SCIENCE↗
Topology Optimization of Curved Electrodes with Suggested Simplified Corrugated Designs
The demand for space-efficient energy storage, from grid to device scale, has soared with the rapid electrification of society. To increase energy density while maintaining high rate performance, shaping electrodes has been shown to be an effective strategy. Computational optimization can suggest designs that improve energy storage performance; however, generated designs are often complex, leading to manufacturing challenges. Additionally, most optimization is typically conducted in standard rectangular domains, whereas some applications, such as in the aerospace industry or in consumer electronics, may benefit from optimized electrodes that fit in a nonstandard form factor. Here, in this work, we use topology optimization to design capacitive full-cell electrodes in a curved domain. We propose a simplified corrugated electrode design based on the optimization results and show that the energy stored in the simplified design is only reduced by about 5–27% when compared to that in the fully optimized design, with more deviation occurring when ionic transport in the porous electrode is particularly impeded. Overall, we find that these simplified designs are promising, particularly given the more conventional manufacturing techniques required, and have a 42–369% increase in energy stored compared to monolithic electrodes.
Energy - Storage↗
Issues in Bi-Directional Interoperability between BIM and BEM
The state-of-the-art interoperability between Building Information Modeling (BIM) and Building Energy Modeling (BEM) is mainly one-way. BEM software like EnergyPlus takes design input from BIM and simulates the energy consumption of the building. BIM, however, is unable to leverage energy simulation results from BEM smoothly. This lack of bi-directional interoperability between BIM and BEM prevents buildings from seeking more energy-efficient design options and limits BIM applications in optimal generative design. In this research, the authors have conducted the following activities related to two-way interoperability between BIM and BEM: (1) surveyed its current status through literature review, (2) investigated barriers to the bi-directional interoperability, and (3) suggested possible approaches to enable bi-directional interoperability and research outlook in the field. As a result, the authors have identified three main barriers to BIM BEM interoperability, namely, inefficient collaboration, limited scope in information transformation, and lack of BEM to BIM transformation. Future directions are suggested to address these barriers.
building design↗
Topology optimization for the full-cell design of porous electrodes in electrochemical energy storage devices
In this paper, we introduce a density-based topology optimization framework to design porous electrodes for maximum energy storage. We simulate the full cell with a model that incorporates electronic potential, ionic potential, and electrolyte concentration. The system consists of three materials, namely pure liquid electrolyte and the porous solids of the anode and cathode, for which we determine the optimal placement. We use separate electronic potentials to model each electrode, which allows interdigitated designs. As a result, a penalization is required to ensure that the anode and cathode do not touch, i.e., causing a short circuit. We compare multiple 2D designs generated for different fixed conditions, e.g. material properties. A 3D design with complex channel and interlocked structure is also created. All optimized designs are far superior to the traditional monolithic electrode design with respect to energy storage metrics. We observe up to a 750% increase in energy storage for cases with slow effective ionic diffusion within the porous electrode.
25 ENERGY STORAGE↗
Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.
97 MATHEMATICS AND COMPUTING↗
Generative AI for design of nanoporous materials: review and future prospects
Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.
36 MATERIALS SCIENCE↗
Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage
Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Input-response space-filling designs
Traditional space-filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. Here, we use the multi-objective optimization method of Pareto fronts to generate multiple non-dominated designs with different emphases on the input and response space-filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.
42 ENGINEERING↗
Numerical Modeling and Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC) (Cooperative Research and Development Final Report, CRADA Number: CRD-22-22968)
This work generated a first-of-its-kind automated workflow to couple time-domain simulations of wave energy converters written in one software language with a set of design generation and evaluation scripts written in another software language. This automated workflow used an existing optimization package to analyze the sensitivity of different design parameters on the power output of a specific WEC, iProTech’s Pitching Inertial Pump (PIP). Geometric, inertial, and power take-off variables were all varied and optimized to find values that produced the highest amount of power generated over varying wave conditions. The findings on these parameter sensitivity studies are used to inform future design iterations of the PIP WEC. Including more design variables in the optimizations will only increase computational run time and further software development is needed to analyze a larger optimization.
16 TIDAL AND WAVE POWER↗
Diagonal state designs with reconfigurable real-time circuits
Unitary designs are widely used in quantum computation, but in many practical settings it suffices to construct a diagonal state design generated with unitary gates diagonal in the computational basis. In this work, we introduce a simple and efficient diagonal state 3-design based on real-time evolutions under 2-local Hamiltonians. Our construction is inspired by the classical Girard-Hutchinson trace estimator in that it involves the stochastic preparation of many random-phase states. Though the exact Girard-Hutchinson states are not tractably implementable on a quantum computer, we can construct states that match the statistical moments of the Girard-Hutchinson states with real-time evolution. Importantly, our random states are all generated using the same Hamiltonians for real-time evolution, with the randomness arising solely from stochastic variations in the durations of the evolutions. In this sense, the circuit is fully reconfigurable and thus suited for near-term realizations on both digital and analog platforms. Moreover, we show how to extend our construction to achieve diagonal state designs of arbitrarily high order.
Shen, Yizhi [LBL, Berkeley] (ORCID:000000024160548↗
Wind Turbine Generator Reliability Analysis to Reduce Operations and Maintenance (O&M) Costs
Wind turbine major systems (blades, pitch, main bearing, gearbox, and generator) are integrated into a composite system. Specifications for these systems and components are developed to achieve symmetry of operation, avoiding negative interaction. For instance, the main bearing, gearbox, and generator (drivetrain) components are interdependent, functioning in unison for efficient energy production. Hence, wind resource and grid interactions affecting the drivetrain impact the performance and reliability of the turbine generator. This paper discusses generator reliability covering the technology evolution over the last 20 years. EPRI's Wind Network for Enhanced Reliability (WinNER) web-based tool and Shermco Industries databases are presented, and conclusions are drawn regarding failures specific to generator design, manufacturing, and operating conditions. Additionally, this paper compares the life expectancy of stator-fed configurations and doubly fed generator systems.
17 WIND ENERGY↗