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Results for “optimal design parameters”

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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-halo Connection with Optimal Statistics

Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or limited to clustering statistics that can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of Sloan Digital Sky Survey DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark matter only simulations with a ΛCDM cosmology. To constrain the theoreStical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find a significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4σ) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first of its kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2021

Optimizing $\mathrm{ACRT}$ to reduce inclusion formation during the $\mathrm{VGF}$ growth of cadmium zinc telluride: I. Computational approach

Theoretical computations reveal insight into parameters that govern the accelerated crucible rotation technique (ACRT) applied to the gradient freeze (VGF) growth of cadmium zinc telluride (CZT). A metric based on the classic Mullins and Sekerka instability criterion is put forth that provides a quantitative means of assessing the impact of different ACRT rotation schedules on morphological stability and inclusion formation during CZT growth. Here this metric is employed in a 2-k factorial design of experiments that identifies the maximum rotation rate as the most important ACRT parameter, followed by the acceleration period and the rest period. Subsequent optimization of rotation schedule is performed for a specific VGF growth system, and mechanistic explanations are provided for the preferred ACRT schedule, which improves interface stability via slower rotation rates and longer acceleration periods than would be applied using classical ACRT schedules.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Non-dimensional performance and safety parameters for heat pipes

The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.

42 - ENGINEERING↗
DOE OSTI2020

Sensitivity Studies, Gap Analysis, and Benchmark Experiment Optimization for Reactor Applications

In regards to nuclear data, some reactor applications may lack validation experiments, which reduces confidence in predicted results. This is especially true for emerging advanced reactor, micro reactor, and Accelerator Driven System (ADS) designs. This work presents an approach to design new criticality experiments that have similar k eff cross section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. This work focuses on cross-section sensitives and gap analysis for three examples relevant to the reactor physics community including a Travelling Wave Reactor (TWR) type-design, Kilopower (a space reactor design), and a lead-bismuth eutectic cooled accelerator-driven system (ADS) to transmute minor actinides.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

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↗
DOE OSTI2025

Operation at Reduced Atmospheric Pressure and Concept of Reliability Redundancy for Optimized Design of Insulation Systems

Electrified transportation is calling for insulation design criteria that is adequate to provide elevated levels of power density, power dynamics and reliability. Increasing voltage levels are expected to cause accelerated intrinsic and extrinsic aging effects which will not be easily predictable at the design stage due to a lack of suitable modeling. Designing reliable insulation systems would require finding solutions able to control accelerated aging due to an unpredictable increase of intrinsic stresses and the onset of extrinsic stresses as partial discharges. This paper proposes the concept of reliability redundancy for the insulation design of aerospace electrical asset components, which is also validated at lower-than-standard atmospheric pressure. The principle is that extrinsic-aging-free design might be achieved upon determining the aging stress or abnormal service stresses distribution and being sure that aging will not generate conditions that can incept extrinsic aging (partial discharges) during operation life. However, such information is never, in practice, fully available to insulation system designers. Hence, especially in critical applications such as electrified aircraft, aerospace, and combat ships a further level of reliability should be added to a partial-discharge-free design, which can consist of the use of corona-resistant materials and/or of life models able to consider the accelerated aging effect of partial discharges (or any other type of extrinsic-accelerated aging factor). Innovative life modeling considering both extrinsic and intrinsic aging stresses, insulating material testing to estimate model parameters, and a metric for quantifying the extent of corona (or partial discharge) resistance can lead to establishing feasibility and limit conditions for optimized or fully reliability-redundant design. It is shown in the paper that if an extrinsic-aging-free design is not feasible, and it is therefore replaced by a redundant design, a further level of reliability redundancy can be provided by effective condition monitoring plans.

Montanari, Gian Carlo↗
DOE OSTI2025

Advancing the STS Neutron Moderator Design with an Automated Optimization Workflow and Unstructured Mesh Modeling

With the Second Target Station approaching its final design phase, a detailed neutronics evaluation of its critical components is necessary. Optimizing the dimensions of the two cold-source moderators that are at the heart of this facility presents a multi-objective optimization problem for which an accurate geometric description is crucial. We have applied a fully automated optimization workflow in which a detailed unstructured mesh geometry is automatically generated with Attila4MC, starting from a parametrized CREO geometry followed by preprocessing with SpaceClaim. With this geometry, a MCNP run is performed to calculate the brightness metrics, which are subsequently provided to the optimization algorithm in DAKOTA that provides new parameters and drives the optimization loop until convergence. In this paper, we show the results of the analysis that are used for the final design of the cylindrical and tube moderator. The optimization simulations provide a refinement to and confirmation of the conclusions of the previous design iteration. Additional to the optimization, a sensitivity study is performed to study the effect of minor geometry changes, which is important for the final engineering design. In conclusion, with these studies, we demonstrate that the automated workflow and high-fidelity unstructured mesh modeling are efficient tools for a thorough design evaluation.

DAKOTA↗
DOE OSTI2021

Performance of a Natural Gas Solid Oxide Fuel Cell System With and Without Carbon Capture

The fuel cell program at the United States Department of Energy (DOE) National Energy Technology Laboratory (NETL) is focused on the development of low-cost, highly efficient, and reliable fossil-fuel-based solid oxide fuel cell (SOFC) power systems that can generate environmentally-friendly electric power with at least 90 percent carbon capture. NETL’s SOFC technology development roadmap is aligned with near-term market opportunities in the distributed generation sector to validate and advance the technology while paving the way for utility-scale natural gas (NG)- and coal-derived synthesis gas-fueled applications via progressively larger system demonstrations. The present study represents a part of a series of system evaluations being carried out at NETL to aid in prioritizing technological advances along research pathways to the realization of utility-scale SOFC systems, a transformational goal of the fuel cell program. In particular, the system performance of utility-scale NG fuel cell (NGFC) systems with and without carbon dioxide (CO2) capture is presented. The NGFC system analyzed features an external auto-thermal reformer (ATR) feeding the fuel to the SOFC system consisting of planar anode-supported SOFC with separated anode and cathode off-gas streams. In systems with CO2 capture, an air separation unit (ASU) is used to provide the oxygen for the ATR and for the combustion of unutilized fuel in the SOFC anode exhaust along with a CO2 purification unit to provide a nearly pure CO2 stream suitable for transport for usage in enhanced oil recovery operations or for storage in underground saline formations. Remaining thermal energy in the exhaust gases is recovered in a bottoming steam Rankine cycle while supplying any process heat requirements. A reduced order model (ROM) developed at the Pacific Northwest National Laboratory (PNNL) is used to predict the SOFC performance. The ROM, while being computationally effective for system studies, provides other detailed information about the state of the stack, such as the internal temperature gradient, generally not available from simple performance models often used to represent the SOFC. Such additional information can be important in system optimization studies to preclude operation under off-design conditions that can adversely impact overall system reliability. The NGFC system performance was analyzed by varying salient system parameters, including the percent of internal (to the SOFC module) NG reformation—ranging from 0 to 100 percent—fuel utilization, and current density. The impact of advances in underlying SOFC technology on electrical performance was also explored.

solid oxide fuel cell (SOFC), natural gas fuel cel↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2026

Experimental design of a W-band corrugated waveguide for wakefield acceleration studies

Radiofrequency (RF) structures in the sub-terahertz (sub-THz) regime are of interest for applications in structure wakefield acceleration due to their compact size and potential for high gradients. Here, we present the design, fabrication, and experimental setup of a metallic corrugated waveguide at 110 GHz, optimized for the 42 MeV electron beam parameters at the Argonne Wakefield Accelerator (AWA). The emittance exchange (EEX) beamline at AWA was used to perform longitudinal bunch shaping for two cases: (1) a high decelerating gradient experienced by a single drive electron bunch about 250 μm long; (2) a high transformer ratio using a two-bunch train consisting of a triangularly shaped drive bunch followed by a long witness bunch to probe the wakefield. Preliminary experimental results are presented, and data analysis is ongoing.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

The XLZD Design Book: towards the next-generation liquid xenon observatory for dark matter and neutrino physics

This report describes the experimental strategy and technologies for XLZD, the next-generation xenon observatory sensitive to dark matter and neutrino physics. In the baseline design, the detector will have an active liquid xenon target of 60 tonnes, which could be increased to 80 tonnes if the market conditions for xenon are favorable. It is based on the mature liquid xenon time projection chamber technology used in current-generation experiments, LZ and XENONnT. The report discusses the baseline design and opportunities for further optimization of the individual detector components. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3σ evidence potential for WIMP-nucleon cross sections as low as 3 x 10 –49 cm 2 (at 40 GeV/c 2 WIMP mass). The observatory will also have leading sensitivity to a wide range of alternative dark matter models. It is projected to have a 3σ observation potential of neutrinoless double beta decay of 136 Xe at a half-life of up to 5.7 x 10 27 years. Additionally, it is sensitive to astrophysical neutrinos from the sun and galactic supernovae.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Tsuchinoko v1.0.0

Tsuchinoko is a Qt application for adaptive experiment execution and tuning. Live visualizations show details of measurements, and provide feedback on the adaptive engine's decision-making process. The parameters of the adaptive engine can also be tuned live to explore and optimize the search procedure. While Tsuchinoko is designed to allow custom adaptive engines to drive experiments, the gpCAM engine is a featured inclusion. This tool is based on a flexible and powerful Gaussian process regression at the core. A Tsuchinoko system includes 4 distinct components: the GUI client, an adaptive engine, and execution engine, and a core service. These components are separable to allow flexibility with a variety of distributed designs.

Pandolfi, Ronald↗
DOE OSTI2022

HPS-RL: Hyperparameter tuning for deep RL applications (HPS-RL) v1

Genetic Algorithms meets Deep RL for Hyperparameters Hyperparameter optimization and architecture search can easily become cumbersome and finding the right hyperparameters can seriously impact the robustness of the deep RL application being developed. We use genetic algorithms to evolve optimum deep RL architectures in a scalable manner. HPS-RL is designed to work with multiple gym enviornments, allow users to test their own optimization functions and tune multi-objective parameters in multiple deep RL algorithms. HPS-RL uses multi-threading and is being extended with mpipy for distributed processing on HPC. https://arxiv.org/abs/2201.11182

Kiran, Mariam↗
DOE OSTI2025

Adaptive Computing (AC) [SWR-24-106]

The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.

Griffin, Kevin [National Renewable Energy Laborato↗
DOE OSTI2026

Adaptive Computing (AC) (Open Source) [SWR-24-106]

The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly, adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.

Griffin, Kevin [National Laboratory of the Rockies↗
DOE OSTI2022

Measuring Chemical Likeness of Stars with Relevant Scaled Component Analysis

Identification of chemically similar stars using elemental abundances is core to many pursuits within Galactic archeology. However, measuring the chemical likeness of stars using abundances directly is limited by systematic imprints of imperfect synthetic spectra in abundance derivation. We present a novel data-driven model that is capable of identifying chemically similar stars from spectra alone. We call this relevant scaled component analysis (RSCA). RSCA finds a mapping from stellar spectra to a representation that optimizes recovery of known open clusters. By design, RSCA amplifies factors of chemical abundance variation and minimizes those of nonchemical parameters, such as instrument systematics. The resultant representation of stellar spectra can therefore be used for precise measurements of chemical similarity between stars. We validate RSCA using 185 cluster stars in 22 open clusters in the Apache Point Observatory Galactic Evolution Experiment survey. We quantify our performance in measuring chemical similarity using a reference set of 151,145 field stars. We find that our representation identifies known stellar siblings more effectively than stellar-abundance measurements. Using RSCA, 1.8% of pairs of field stars are as similar as birth siblings, compared to 2.3% when using stellar-abundance labels. We find that almost all of the information within spectra leveraged by RSCA fits into a two-dimensional basis, which we link to [Fe/H] and α-element abundances. We conclude that chemical tagging of stars to their birth clusters remains prohibitive. However, using the spectra has noticeable gain, and our approach is poised to benefit from larger data sets and improved algorithm designs.

79 ASTRONOMY AND ASTROPHYSICS↗