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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multi-Objective Boundary Analysis of Discrete and Integrated SiC FET Modular Non-inverting Buck and Boost Converters for Fuel Cell EVs

This paper presents a multi-objective analysis of discrete and integrated SiC FET-based non-inverting buck-boost converter modules for modular fuel cell electric vehicle (EV) systems. Two converter ratings, 60 kW and 90 kW, are evaluated for both implementations, scalable up to 420 kW and 450 kW, respectively. Performance is assessed across efficiency, volumetric and gravimetric power density, cost, thermal stress, and estimated lifetime, where lifetime is derived from SiC FET B10 power-cycling data and junction temperature variations at rated power. A normalized overall performance index combined with a Pareto-boundary framework is used to identify configurations that optimally balance competing objectives. Results show that most configurations lie on the Pareto front, providing balanced trade-offs, while certain high-power discrete (90 kW at 450 kW) and integrated (60 kW at 180−420 kW) configurations are dominated. In general, discrete modules are more favorable for lower-power modular systems due to higher power density and lower cost, whereas integrated modules become more advantageous at higher power levels due to improved thermal behavior and longer lifetime. These findings provide practical design guidance for scalable fuel cell converter architectures and highlight the importance of system-level trade-offs in modular power electronics design.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2023

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9

We report the fourth installment in the series of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) quasar survey, which includes quasars observed between 2017 September and 2021 June. There are in total 13,066 quasars reliably identified, of which 6685 are newly discovered that are not reported in the Sloan Digital Sky Survey (SDSS) DR14 quasar catalog or Million Quasars catalog. Because LAMOST does not provide accurate absolute flux calibration, we recalibrate the spectra with the SDSS/Pan-STARRS1 multiband photometric data. The emission-line properties of Hα, Hβ, Mg II , and C Iv and the continuum luminosities are measured by fitting the recalibrated spectra. We also estimate the single-epoch virial black hole masses (M BH ) using the derived emission-line and continuum parameters. This is the first time that the emission-line and continuum fluxes were estimated based on LAMOST recalibrated quasar spectra. The catalog and spectra for these quasars are available online. After the 9 yr LAMOST quasar survey, there are in total 56,175 identified quasars, of which 24,127 are newly discovered. The LAMOST quasar survey not only discovers a great number of new quasars but also provides a database for investigating the spectral variability of the quasars observed by both LAMOST and SDSS and finding rare quasars, including changing-look quasars and broad absorption line quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

Abstract This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe 2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe 2 , the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

Zhang, Xu↗
DOE OSTI2026

Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks

In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi-objective optimization of CEBAF heat load management and trip rates using AI/ML

The goal of this project is to investigate the use of RL to simultaneously optimize the cryogenic heat load and trip rate of the CEBAF linacs and make it generic to solve multi-objective problems that will enable its application at other Department of Energy (DOE) facilities with similar challenges. The existing approach optimizes the gradient distribution to minimize the trip rate and does not take into account the total heat load. A secondary objective of the project is the develop a framework to correct cavity quality curves (Q-curves) or RF-heat to Gradient relation curves for individual cavities. These curves are currently obtained using historical measurements and may have changed their behavior due to various events. Correction in these curves will allow us to accurately approximate the total RF heat generated by the linacs which is critical for the optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Contribution of Gaia Sausage to the Galactic Stellar Halo Revealed by K Giants and Blue Horizontal Branch Stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Digital Sky Survey, and Gaia

We explore the contribution of the Gaia Sausage to the stellar halo of the Milky Way by making use of a Gaussian mixture model (GMM) and applying it to halo star samples of Large Sky Area Multi-Object Fiber Spectroscopic Telescope K giants, Sloan Extension for Galactic Understanding and Exploration K giants, and Sloan Digital Sky Survey blue horizontal branch stars. The GMM divides the stellar halo into two parts, of which one represents a more metal-rich and highly radially biased component associated with an ancient, head-on collision referred to as the Gaia Sausage, and the other one is a more metal-poor and isotropic halo. A symmetric bimodal Gaussian is used to describe the distribution of spherical velocity of the Gaia Sausage, and we find that the mean absolute radial velocity of the two lobes decreases with the Galactocentric radius. We find that the Gaia Sausage contributes about 41%–74% of the inner (Galactocentric radius r gc < 30 kpc) stellar halo. The fraction of stars of the Gaia Sausage starts to decline beyond r gc ~ 25–30 kpc, and the outer halo is found to be significantly less influenced by the Gaia Sausage than the inner halo. After the removal of halo substructures found by integrals of motion, the contribution of the Gaia Sausage falls slightly within r gc ~ 25 kpc but is still as high as 30%–63%. Finally, we select several possible Sausage-related substructures consisting of stars on highly eccentric orbits. The GMM/Sausage component agrees well with the selected substructure stars in their chemodynamical properties, which increases our confidence in the reliability of the GMM fits.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗
DOE OSTI2024

Paired Hydro-Battery Hybrid System Operations Using MIQP Based Multi-Objective Optimization

Hybridizing a hydropower plant with a battery energy storage system is often a very expensive decision. So to make the case for feasibility of hybridization the cost benefit analysis must be comprehensive. Most literature found on this subject only uses one out of many different available value streams to carry out a feasibility analysis. To that end, in this study we propose a multi-objective optimization formulation and a mixed-integer quadratic programming optimization engine that considers multiple different value streams and optimizes hydro-battery hybrid system paired operations to maximize revenue generated from energy arbitrage while simultaneously minimizing the total hydro turbine mileage thus reducing turbine starts and stops and improving turbines life all while following all environmental constraints and not violating any limitations either regulatory or preferential (i.e., to support recreational activities like white water rafting, etc.). In this study we also implemented the developed optimization methodology to a real-world case study of Bagnell dam hydropower facility (8 units totaling 240 MW rated power capacity) which is owned and operated by our industry partner Ameren Energy Inc. The case study outcomes shows that by hybridizing the Bagnell dam hydropower facility with a 60 MW x 2-hr battery energy storage system, the annual benefits can be increased over \$6 million while reducing the annual mileage averaged per turbine and number of start/stops by over 98\% and 85\% respectively.

Chalishazar, Vishvas H.↗
DOE OSTI2024

Multi-objective optimization of PWR core design using NSGA-II in RAVEN’s optimization framework

Designing an PWR loading pattern is a combinatorial problem challenging to solve by brute force or traditional methods due to the sheer amount of possible combination, and constraints. Nature-inspired algorithms, such as the genetic algorithm, have demonstrated the potential to tackle this problem. The goal of this work was to improve and demonstrate the capabilities for constrained, multi-objective optimization (MOO) of loading patterns using NSGA-II in RAVEN’s optimization framework.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Multi-Objective Cycle Optimization of an Integrally Geared Waste Heat Recovery Unit for a Combined Cycle Power System

This paper has presented the cycle design and optimization details for a sCO2-based WHRS targeting the Solar Turbines Titan 130. The PreheatSR cycle layout was chosen to effectively address the issue of acid dew point corrosion and ensure high system performance is not significantly impacted by use of alternative fuels. The optimization process discussed uses a multi-objective optimization to discover a series of optimal cycle configurations given ambient temperature variability for a chosen site location while considering the initial capital cost of the cycle components. Cycle models built that incorporated off-design methods for the heat exchangers and turbomachinery allowed for the investigation of cycle operation that maximizes power output for individual cycle conditions. The resulting Pareto front serves as a guide for how to configure the WHRS cycle for the highest yearly energy extracted for a given investment.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

13 HYDRO ENERGY↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2025

Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Commonly produced as a byproduct of uranium fission, 99 Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k eff ) and natural uranium fission power, which are directly correlated with the amount of 99 Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

Accelerator-driven systems↗
DOE OSTI2022

Development of Aluminum Scandium Nitride Molecular Dynamics Force Fields with Scalable Multi-Objective Bayesian Optimization

Scandium (Sc)-doped aluminum nitride (AlN) exhibits improved piezoelectric properties, which is favorable for sensor applications. Although many experimental studies exist to fine tune the material properties for design purposes, an atomistic level understanding of the structure-property (S-P) relationships is needed, which is the aim of this work. Molecular dynamics can be used to understand the S-P relationships. However, the limited availability of suitable force fields has been a major challenge for accurate property predictions. In this article, a robust force field calibration method using a scalable multi-objective Bayesian optimization approach is presented. Optimizations with three, six, and eight objectives are applied to calibrate aluminum scandium nitride force fields based on the piezoelectric characteristics, modulus of elasticity, and lattice parameters at different Sc-doped levels. Furthermore, the performances of the different force fields are compared, and the performance of the higher dimensional objective problems is discussed. The highly scalable molecular dynamics force field development method is successfully implemented, resulting in the creation of several aluminum scandium nitride molecular dynamics force fields for piezoelectric applications at varying Sc dope levels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution

Neuroevolution has had significant success over recent years, but there has been relatively little work applying neuroevolution approaches to spiking neural networks (SNNs). SNNs are a type of neural network that includes temporal processing component, are not easily trained using other methods, and can be deployed into energy-efficient neuromorphic hardware. In this work, we investigate two evolutionary approaches for training SNNs. We explore the impact of the hyperparameters of the evolutionary approaches, including tournament size, population size, and representation type, on the performance of the algorithms. We present a multi-objective Bayesian-based hyperparameter optimization approach to tune the hyperparameters to produce the most accurate and smallest SNNs. We show that the hyperparameters can significantly affect the performance of these algorithms. We also perform sensitivity analysis and demonstrate that every hyperparameter value has the potential to perform well, assuming other hyperparameter values are set correctly.

Parsa, Maryam↗
DOE OSTI2022

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. Here, in this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Multi-objective Decisions on Integrated Energy Systems Planning and Operation for Industrial Combined Heat and Power Supply

Unlike the power sector—which can transmit electricity over long distances via established grids—the industrial sector poses a unique challenge due to its geographically concentrated large-scale heat processes. Enhancing energy security in such industrial parks provides a dual benefit: reduced exposure to volatile fossil fuel prices and improved economic viability, largely driven by economies of scale in energy supply and distribution. This study presents a comprehensive technoeconomic analysis of a nuclear energy hub, employing a mechanism-focused approach to evaluate uncertainties in operational strategies and capacity optimization. Load profiles from three major energy intesive industries—chemical, refinery, and steelmaking—are examined, each presenting unique challenges and opportunities for nuclear energy integration. We adopt a multi-objective optimization framework, converting multiple objectives into a single objective function through the e-constraint method. The findings highlight clear trade-offs between system conditions and varying levels of energy independence. Overall, this analysis is granular enough to address industry-specific concerns yet sufficiently generalizable to provide actionable insights into the feasibility of nuclear-based clean heat solutions for the industrial sector.

25 - ENERGY STORAGE↗