Thermo-economic assessment and optimization of a hybrid triple effect absorption chiller and compressor
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Energy & Fuels↗
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Results for “parametric optimization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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An Assessment of Additively Manufactured Bonded Permanent Magnets for a Distributed Wind Generator
In this paper, we examine and compare the performance of a generator design optimized using additively manufactured NdFeB-SmFeN in nylon-polymer-bonded permanent magnets (PMs) against a generator design with conventional NdFeB sintered PMs. To realize this, a commercially available 15-kW wind generator's rotor is re-optimized using both additively manufactured and sintered NdFeB magnets using simple geometric parameterization that allowed for two specific magnet shapes, namely, arc-shaped and crown-shaped designs. Results showed that for a similar generator performance, the designs with additively manufactured bonded PMs are more cost-competitive in terms of the estimated PM material cost and also have negligible eddy current magnet losses.
additive manufacturing↗
Optimization of Conductive Fins to Minimize UO2 Fuel Temperature and Radial Temperature Gradient
To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor.
Paaren, Kyle M.↗
e RPCA : Robust Principal Component Analysis for Exponential Family Distributions
Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.
Zheng, Xiaojun↗
Approximating the linear response of physical chaos
Abstract Parametric derivatives of statistics are highly desired quantities in prediction, design optimization and uncertainty quantification. In the presence of chaos, the rigorous computation of these quantities is certainly possible, but mathematically complicated and computationally expensive. Based on Ruelle’s formalism, this paper shows that the sophisticated linear response algorithm can be dramatically simplified in higher-dimensional systems featuring statistical homogeneity in the physical space. We argue that the contribution of the SRB (Sinai–Ruelle–Bowen) measure gradient, which is an integral yet the most cumbersome part of the full algorithm, is negligible if the objective function is appropriately aligned with unstable manifolds. This abstract condition could potentially be satisfied by a vast family of real-world chaotic systems, regardless of the physical meaning and mathematical form of the objective function and perturbed parameter. We demonstrate several numerical examples that support these conclusions and that present the use and performance of a simplified linear response algorithm. In the numerical experiments, we consider physical models described by differential equations, including Lorenz 96 and Kuramoto–Sivashinsky.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation
High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.
36 MATERIALS SCIENCE↗
Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency
Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our past RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potential that provides an analytic embedding of local coordination through a three-body term. A hierarchical RL workflow combining continuous-action Monte Carlo Tree Search and property-based rewards efficiently explores the 26-dimensional parameter space, sequentially optimizing lattice parameters, densities, angles, and cohesive energies of 21 silica polymorphs. The resulting models, Q-Tersoff and ML-Tersoff, reproduce the energetic ordering of low-energy phases and capture the angular correlations and amorphous structure factors of silica with improved fidelity over pairwise force fields, while remaining orders of magnitude faster than high-dimensional machine-learned potentials. Both models underperform for elastic constants and high-energy frameworks, delineating the limits of the current analytic form. The approach establishes a general and interpretable route to angle-aware, short-range potentials that bridge physics-based and machine-learned descriptions of silicate materials.
36 MATERIALS SCIENCE↗
Parametric Testing and Evaluation of a Novel Biphasic Absorption Process for Post-Combustion Carbon Capture
A novel biphasic CO2 absorption process (BiCAP) is being developed at the University of Illinois. BiCAP is enabled by using a new class of biphasic solvents developed based on multi-property criteria for postcombustion carbon capture. The process was previously demonstrated at the lab-scale, including in 10 kWe absorption and desorption column systems. In the current study, the process has been scaled up by building and testing a 40 kWe integrated capture skid at a power plant. Parametric tests for two biphasic solvents were conducted with the synthetic flue gas made of bottle CO2 gas and air to validate the performance and operational flexibility of the unit with respect to varying process or operating conditions. Operation of the phase separator, which is unique equipment to BiCAP, was verified to be stable and efficient. Two liquid phases could be separated via static settlement based on a density difference and >90% of the CO2 absorbed was concentrated in the separated CO2-rich phase. Results of parametric tests showed that the minimum heat duty appeared at the optimal stripping pressure. A higher stripping temperature was more favourable to improve overall energy performance. Introducing a secondary cold solvent feed stream to the top of the stripper via bypassing the cross heat exchanger helped reduce the heat loss carried with water vapour. Test results also revealed that the heat duty requirement was comparable when the CO2 concentration in flue gas decreased from ~11 to 4 vol.% as the phase separation decoupled the absorption and desorption steps in BiCAP. Under typical steady-state conditions, both biphasic solvents lowered the process heat duty by >40% compared with the reference 30 wt.% monoethanolamine solvent.
Absorption↗
Constrained curve fitting for semi-parametric models with radial basis function networks
Common to many analysis pipelines in lattice gauge theory and the broader scientific discipline is the need to fit a semi-parametric model to data. We propose a fit method that utilizes a radial basis function network to approximate the non-parametric component of such models. The approximate parametric model is fit to data using the basin hopping global optimization algorithm. Parameter constraints are enforced through Gaussian priors. The viability of our method is tested by examining its use in a finite-size scaling analysis of the $q$-state Potts model and $p$-state clock model with $q=2,3$ and $p=4,\infty$.
Peterson, Curtis T.↗
Towards stabilization of pressurized oxy-coal diffusion flames
Designing an effective burner is vital for the development of pressurized oxy-fuel combustion technologies. Turbulent jet diffusion burners are adopted for the pressurized oxy-combustor in this research and a bluff-body is employed to create a recirculation zone to stabilize the flame in pressurized oxy-combustor. The objective of this numerical work is to perform a systematic analysis of the characteristics of this pressurized non-premixed flame. Specifically, a 15-bar POFC combustor of power 100 kWth is modeled employing the Ansys FLUENT commercial platform, using Reynolds-averaged Navier-Stokes (RANS) modeling. The present computational work focuses on identifying the aerothermodynamic features of the isothermal and reacting flow with a disk-shape bluff-body. Results show that fuel-oxidizer stream momentum ratio has great impacts on the temperature profile of the down-fired, co-axial, pressurized oxy-coal, diffusion flame. Parametric study of the width of the burner tip identifies an optimal range for this pressurized burner.
Li, Lei↗
Impact of a Bluff Body on a Pressurized Oxy-fuel Flame
Designing an effective burner is vital for the development of pressurized oxy-fuel combustion technologies. In the present work, turbulent jet diffusion burners are adopted for a pressurized oxy-combustor, with a bluff-body employed to create a recirculation zone, thereby stabilizing the flame in such a combustor. The objective of this numerical study is to perform a systematic analysis of the characteristics of such a pressurized non-premixed flame. Specifically, a 15-bar pressurized oxy-fuel combustor of power 100 kWth is modeled by means of the Ansys FLUENT commercial platform, using the Reynolds-averaged Navier-Stokes (RANS) approach. The present work focuses on identifying the aerothermodynamic features of the pressurized oxy-fuel burner with a disk-shape bluff-body. It is shown that the fuel-to-oxidizer stream momentum ratio has a great impact on the temperature profile of the down-fired, co-axial, pressurized oxy-coal, diffusion flame. A parametric study of the blockage ratio of the burner identifies an optimal range for this pressurized burner.
Li, Lei↗
Coupled beam-target-moderator optimization for the Second Target Station
To support the design of the Second Target Station, that aims to provide the world’s highest peak brightness of cold neutrons, studies that optimize the dimensions of the target and moderators are invaluable. In this work, we investigate the influence of the target dimensions and beam profile on the performance and optimal size of the moderators. We perform optimization runs with detailed MCNP6.2 simulations using high-fidelity unstructured mesh geometries generated from parametrized CAD models. We demonstrate that small changes in target height do not influence the moderator performance if the beam dimensions are chosen adequately. We quantify the effect of beam footprint and target width on the moderator performance and show that the optimal moderator dimensions are insensitive to limited changes in target and beam profile.
Dakota↗
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process
Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.
42 ENGINEERING↗
HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)
This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.
36 MATERIALS SCIENCE↗
HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)
This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.
13 HYDRO ENERGY↗
Parametric reduced order models for graded lattice structures
Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Sensitivity-Driven Experimental Design to Facilitate Control of Dynamical Systems
Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high-fidelity physics-based modeling, offline trajectory planning, feedback control design, and data acquisition strategies to reduce uncertainties. Here this article proposes an optimization-centric perspective which couples these elements in a cohesive framework. We introduce a novel use of hyper-differential sensitivity analysis to understand the sensitivity of feedback controllers to parametric uncertainty in physics-based models used for trajectory planning. These sensitivities provide a foundation to define an optimal experimental design which seeks to acquire data most relevant in reducing demand on the feedback controller. Our proposed framework is illustrated on the Zermelo navigation problem and a hypersonic trajectory control problem using data from NASA’s X-43 hypersonic flight tests.
42 ENGINEERING↗
Micropolar deep material network
This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.
36 MATERIALS SCIENCE↗