Search NASA⌕ Search

SEARCH · Search NASA

Results for “Advanced 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structural evolution and magnetic hardness of (Sm,Zr)(Fe,Co,Ti) 12 alloy particles via reduction-diffusion

The quest for achieving high coercivity in Sm(Fe,Co,Ti) 12 alloys, despite their inherent strong magnetocrystalline anisotropy, has posed significant challenges. Recently, (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles have exhibited coercivity μ 0 H c > 1.2 T, showcasing promising prospects and significant potential for both manufacturing and research endeavors. This study delves into the structural evolution of (Sm,Zr)(Fe,Co,Ti) 12 (1:12) alloy particles made via the calciothermic reduction-diffusion synthesis process as influenced by the molar ratios of Ca atoms to O 2- ions (Ca/O), annealing time and annealing temperature. Critical insight that informs conditions to optimize the magnetic response is gained via systematic experimentation and advanced electron microscopy. Complex structural features, including core-shell morphologies and intricate multiphase compositions within individual particles, are unveiled. An optimal Ca/O ratio of 1.30 produces particles with a coercivity up to μ 0 H c = 1.63 T, while higher Ca/O ratios induce the formation of a Sm-rich TbCu 7 -type (1: 7 ) phase, which only partially transforms into the desired 1:12 phase during annealing. Persistent remnants of the 1:7 phase locally impact atomic structure, particle morphology, and coercivity. Furthermore, these findings underscore the complex interplay between synthesis parameters, resulting structures, and magnetic properties, informing the design and optimization of high-performance permanent magnets comprised of the (1:12) compound.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reconstruction of beam parameters and betatron radiation spectra measured with a Compton spectrometer

The photon flux resulting from high-energy electron beam interactions with high-field systems, such as those found in the upcoming FACET-II experiments at the SLAC National Accelerator Laboratory, yields deep insight into the electron beam’s underlying dynamics during the interaction. However, extracting this information is an intricate process. To demonstrate how to approach this challenge using modern methods, this paper utilizes simulated data that models plasma wakefield acceleration-derived betatron radiation in experiments to determine reliable methods of reconstructing key beam and beam-plasma interaction properties. For betatron radiation measurements, translating the observed 200⁢ keV to 30⁢ MeV photon double-differential energy-angle spectra obtained from an advanced Compton spectrometer requires testing multiple methods to optimize the pipeline from its response to incident electron beam information. The paper compares maximum likelihood estimation and machine learning to refine the translation of photon spectra into precise electron beam metrics, such as spot size, energy, and emittance, enhancing the understanding of beam behavior within these dense, high-field environments. We also introduce machine learning and the expected maximization algorithm to reconstruct the primary photon spectrum, employing a multilayer neural network for regression analysis of the energy and angle spectra. With appropriate modifications, the advanced methods reproduce relevant incident beam parameters with high accuracy, even for beam sizes in the <10 μ⁢m range. This capacity is critical to understanding intense beam propagation and its optimization in plasma.

Beam code development & simulation techniques↗
DOE OSTI2024

Design and optimization of flexible decoupled high-temperature gas-cooled reactor plants with thermal energy storage

Advanced nuclear power plants are well-positioned for future zero-carbon grids, however, the need for flexible power generation will be required over the traditional emphasis on baseload generation for meeting historical demands. To achieve such flexibility, this work examines viable configurations for coupling nuclear energy production with thermal energy storage. Previous designs on nuclear-thermal energy storage configurations for advanced reactor designs, which utilized reactor steam as the heat source for charging the thermal energy storage, are restricted by the heat diversion ratio and efficiency losses, thus their impacts can be limited. In this context, this study proposes configurations for fully decoupling the nuclear reactor from the power cycle and positioning the storage as an intermediate loop, thereby achieving an unconstrained heat diversion ratio and improved efficiency. Compared with a standard high-temperature gas-cooled reactor’s power cycle, steady-state thermodynamic modeling and dispatch optimizations quantify the benefits of a steam reheat cycle within the fully decoupled thermal energy system to separate the plant cycle from the high-pressure primary side. These benefits are further detailed, compatible with required high-temperature and high-pressure conditions, through (1) open-source dynamic transient models that examine the impact of off-design operation on the systems, (2) the investigation of components design and costing and finally (3) sizing and dispatch optimization. The fully-decoupled design achieves a cycle efficiency of 43.1%, an enhancement over the vendor’s standard efficiency of 42.2% (Xe-100 design). Here, the proposed design offers strengthened physical barriers from the nuclear island as well as superior operational flexibility and power boosting. Dispatch optimization and market analysis reveal that thermal energy storage size is highly dependent on the peak patterns of electricity prices and the minimum generation level constraint imposed on the balance of plant. Evaluation of off-design operation demonstrates that the full decoupling design with the suggested fail-safe control mechanisms ensures a minimal impact on reactor parameters, even during rapid power ramping.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

REIMAGINING HEAT EXCHANGERS FOR NEXT GENERATION ENVIRONMENTAL SYSTEMS

Air-to-refrigerant heat exchangers (HXs) are essential components in space conditioning, refrigeration, and power systems, and recent efforts have focused on making these devices more compact, reducing refrigerant charge and lowering manufacturing costs. Historically, HX innovation has been limited by available computational resources, design tools, and manufacturing constraints. The best available technologies utilize tube-fin and micro- or macro-channel tubes with fins, which are not necessarily the optimal designs achievable with current technology. In this paper, we highlight the latest advancements in air-to-refrigerant HXs, specifically emphasizing innovations achieved through shape and topology optimization. A multi-scale design optimization approach is introduced, alongside similar methods in literature, which enable highly sophisticated shape-optimized tube designs with more than 50% reduction in size and 25% reduction in refrigerant charge, essential for A3 refrigerant charge limit compliance. The frameworks integrate traditional heat and mass transfer science with state-of-the-art machine learning, genetic algorithms, and adjoint algorithms to create novel designs. While many of these innovative designs may not be manufacturable using conventional methods, they allow us explore the boundaries of what is possible. These novel air-to-refrigerant HXs are key enablers for ultra-low-refrigerant charge heat pump and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗
DOE OSTI2024

Tailoring Pore Architecture and Heteroatom Functionality of Polymeric Waste-Derived Nanoporous Carbon for CO 2 Capture Applications

This study proposes upcycling polymeric waste, i.e., waste floral foam, into high-performance nanoporous carbon that efficiently captures CO 2 . This paper presents strategies for improving the properties of nanoporous carbon, which aid in a superior CO 2 capture performance. Initially, pristine nanoporous carbon was produced from waste floral foam using various KOH impregnation ratios. The nanoporous carbon with a 1:2 (waste floral foam:KOH) ratio exhibiting optimal CO 2 capture capability was further advanced through single and dual atom doping. The doping of N and codoping of N,S atoms into the nanoporous carbon altered its textural and surface chemical properties, making them efficient for CO 2 capture. Comparative CO 2 capture studies of pristine nanoporous carbon (NC-x), N-doped nanoporous carbon (N-NC2), and N,S-codoped nanoporous carbon (N,S-NC2) demonstrate the superiority of N-doping. N-doped nanoporous carbon exhibited the largest ultramicroporosity (0.3100 cm3/g, 63.43%) and highest heteroatom content (34.94 atomic %), contributing to its enhanced CO 2 capture capability (4.54 mmol/g). Finally, implementing the “waste-to-depollution” approach, this research lays the groundwork for producing low-cost, environmentally friendly nanoporous carbon with remarkable CO 2 capture attributes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Atomic-Scale Behavior of Radiation-Resistant ZnO under High-Energy Electron Bombardment

Understanding the atomic structure and defect characteristics of ZnO thin films is crucial for optimizing their electronic properties and performance in advanced applications. Here, we investigate the atomic structure and defect characteristics of atomic layer deposition (ALD)-grown ZnO thin films by using aberration-corrected scanning transmission electron microscopy (STEM). Atomic-resolution imaging identifies prevalent stacking faults, dipole disorder, and various grain boundary types, which are believed to influence the electronic properties of ZnO. Additionally, real-time electron beam exposure experiments demonstrate structural transformations, including crystal growth and surface rearrangements. These findings provide insights into the growth mechanisms of ALD ZnO under high-energy electron irradiation conditions, an important finding for the use of polycrystalline ZnO wide bandgap semiconductors in space-like conditions. In conclusion, our results underscore the capability of STEM in directly visualizing and quantifying atomic-scale defects and beam-induced transformations in radiation-resistant ZnO.

Defects↗
DOE OSTI2025

CeO 2 Promoted CuO/MgO-Al 2 O 3 Catalyst with Enhanced Activity and Water-Resistance for CO Oxidation

Copper (Cu)-based catalysts have emerged as cost-effective and sustainable alternatives to noble metal systems (e.g., Pt, Pd) for catalytic CO oxidation. However, their practical application is hindered by insufficient low-temperature activity and rapid deactivation under wet conditions containing moisture. To address these challenges, this work introduces CeO 2 -modified CuO/MgO-Al 2 O 3 (Cu-Ce/MA) catalysts, strategically designed to enhance the catalytic performance and water resistance simultaneously. These catalytic materials were evaluated for CO oxidation under both dry and humid conditions, revealing that CeO 2 modification significantly improves the low-temperature activity. Specifically, the optimal catalyst, Cu-30Ce/MA, achieved a 50% CO conversion temperature (T 50 ) of 151 °C, a marked reduction from 218 °C on Cu/MA reference catalyst. Furthermore, the water resistance improves in a CeO 2 content-dependent manner, with higher CeO 2 loadings imparting greater stability in humid environments. Detailed characterizations demonstrate that CeO 2 promotes the dispersion of CuO and stabilizes Cu sites, while also enhancing the low-temperature reducibility and CO adsorption capacity. Crucially, CeO 2 modification suppresses the competitive H 2 O adsorption, mitigating water-induced deactivation. These synergistic effects collectively rationalize the superior activity and durability of Cu-Ce/MA catalysts. By elucidating the dual role of CeO 2 in optimizing Cu-based systems, this study advances the rational design of cost-effective catalysts for real-world CO emission control, particularly under water-rich industrial conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods

Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.

King, Ethan [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

Accelerating the identification of novel secondary metabolites in bioenergy plant root exudates using MicroED

Small molecule metabolites drive inter- and intraspecies communication and dependencies in diverse biological systems, yet a large proportion of these important chemical compounds remain uncharacterized in plants and microbes. Approximately 90% of the metabolites in root exudate profiles are unknown compounds, despite the importance of root exudate composition in plant-microbe interactions. We need advanced analytical capabilities that will support rapid discovery and structural elucidation of metabolites from biological samples that may be limited in quantity and high in complexity. To fill this gap, this project aimed to develop an integrated workflow involving metabolite extraction, separation, and crystallization from plant root exudates followed by characterization using nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and microcrystal electron diffraction (MicroED). Using crude root exudates from sorghum, this project successfully developed higher throughput exudate fractionation strategies to obtain pure compounds for crystallization and identified crystals in multiple fractions that diffracted. Additional efforts to increase the throughput of high-quality crystal generation for MicroED, such as crystallization screening and crystallization chaperone exploration, will be needed to further advance root exudate metabolite identification. The overall optimized sample preparation process can then be integrated with the existing data collection and data analysis pipelines for MicroED at PNNL to facilitate more rapid natural product discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Collaborative Research: Enhancing Laser-Based Ion Sources with High Data Rate Techniques

This collaborative research project focuses on leveraging advanced machine learning techniques to analyze and optimize data from high-repetition-rate laser experiments. The main goal is to apply modern computing hardware, customized data acquisition firmware/software, and machine learning approaches to improve data analysis and experimental control. The project also explores how methodology can be developed on smaller-scale experimental setups and then translated to larger facilities within DOE's LaserNetUS network. With extensive data collection and modeling, the research aims to predict and optimize experimental parameters to enhance performance and efficiency.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Conceptual Design of a Water Tank Critical Facility for SPARC

Critical experiments, sometimes referred to zero-power reactors, are crucial tools in developing and validating nuclear physics predictions and thus an indispensable capability to ensure criticality safety during all parts of the nuclear fuel cycle and in reducing uncertainties in reactor physics predictions toward optimizing nuclear energy production. The System Physics Advanced Reactor Facility (SPARC) project was recently initiated to enable large-scale criticality experiments using a horizontal split table machine well suited toward solid core materials systems (fuel, moderator, reflector). The facility selected for this mission was a former pool-type research reactor building and thus also well suited toward a second critical experiment capability able to house full-size light-water reactor (LWR) fuel bundles. A conceptual design study was undertaken to review past water tank critical experiments used for LWR physics experiments and to develop an early engineering design for a new critical assembly tank (CAT). The work described here shows that a relatively simple CAT concept can be constructed and deployed in the SPARC facility to meet the urgent demands for new critical experiments on advanced LWR fuel bundles designs. The SPARC facility layout is conducive to the receipt and upending of LWR fuel bundles using existing containers and equipment from the LWR industry. The facility’s overhead crane can then be used to handle fuel bundles and place them in a vertical storage rack or in the CAT for critical experiments, both of which fit within the building’s “open basement” alongside other equipment planned for SPARC and the horizontal split table. A slightly lower area in the basement can serve as a large drain tank so that fail-safe valves drain the CAT reactor tank for safe shutdown. Neutronic configurations were determined where a 3 × 3 array of fuel bundles can be surrounded by full-length “loose rods” to adjust reactivity so that critical is achieved when the bundles are fully submerged. Viable configurations were determined for both pressurized- and boiling-water-reactor-type fuel bundles. This design concept was used to develop an early planning basis for establishing the CAT capability alongside the otherwise planned SPARC project in order to help streamline the process. Recent presidential executive orders have highlighted the need to achieve power uprates in LWR plants and the CAT capability will be a crucial element of these initiatives. Based on the work described herein, it is recommended that an earnest and timely project begin in order to establish this urgently needed capability.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Demonstration of Algorithm for Sensor Placement Optimization using Simulation Data

This deliverable reports FY26 progress in advancing a neural-network-based Green’s-function framework for reconstructing neutron-flux distributions from ex-core measurements and for translating reconstruction requirements into a practical detector-layout strategy. Building on the FY25 formulation, the present work had two main objectives: (1) refine and re-evaluate the reconstruction methodology on an updated Purdue University Reactor Number One (PUR-1) model, and (2) develop a systematic, Green’s-function-guided procedure for boundary detector placement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Hydrogen, Methane, Brine Flow Behavior, and Saturation in Sandstone Cores During H 2 and CH 4 Injection and Displacement

Large-scale underground hydrogen storage (UHS) is a critical component in the emerging hydrogen economy. Knowledge of multiphase flow behavior involving hydrogen in storage reservoir formations is crucial to characterizing hydrogen transport properties and essential for the deliverability and storage operations of UHS. There are still many gaps in fully understanding hydrogen–methane–brine multiphase phase flow that require further investigation. In this work, H 2 and CH 4 were injected through brine-saturated sandstone cores using a tri-axial core holder system fitted with flow rate meters and pressure transducers, while the effluent gas concentrations were analyzed using an online micro gas chromatograph. Brine displacement, permeability, and gas breakthrough curves were measured. We studied the flow behavior of hydrogen and methane in sandstone cores through testing brine displacement by gas injection and comparing the hydrogen displacement of methane with the methane displacement of hydrogen. We also tested the differences between horizontal and vertical flow in brine displacement. The results showed that brine displacement was more efficient in a core with higher permeability and porosity, resulting in a higher initial gas saturation. A higher gas injection rate brought about faster gas breakthrough measured by pore volume and sharper concentration curves. Hydrogen did not exhibit abnormal flow in the sandstone when the flow was horizontal and downward vertical. Gas overriding was observed in brine displacements when the flow was horizontal, with hydrogen showing this behavior more profoundly compared to methane. Downward vertical gas injection induced higher efficiency brine displacement compared to horizontal displacement and resulted in a higher initial gas saturation in the sandstone cores. These findings address critical knowledge gaps regarding gas flow patterns and displacement behaviors during hydrogen injection and recovery phases in UHS facilities using methane as the cushion gas. The insights from this research offer valuable guidance for optimizing UHS systems, ensuring operational efficiency, and advancing sustainable energy solutions in alignment with decarbonization goals.

08 HYDROGEN↗
DOE OSTI2025

Improving leaf spring phenology modelling for temperate tree species: An integration of the Farquhar–Medlyn photosynthesis model with the optimality‐based approach

Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality-based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R-OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site-species-year records from across Europe. Our results show that R-OPT outperforms both the default OPT and non-optimality-based models (e.g. the chilling-forcing trade-off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R-OPT excels in lowland, moist environments but is less effective in high-altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of environmental factors influencing phenology in the context of climate change.

coupled Farquhar–Medlyn photosynthesis model↗