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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application

As an environmentally friendly low global warming potential (GWP) refrigerant, Carbon dioxide (CO 2 ) has continuously gained popularity and research attention as alternative refrigerant for supermarket refrigeration system. In this paper, to fulfill the increasing need of accurate Low-GWP supermarket refrigeration models for development of supervisory level control and optimization strategies, a high fidelity model is developed for CO 2 transcritical supermarket refrigeration system which includes compressor rack of low temperature (LT) compressors and medium temperature (MT) compressors, air-cooled gas cooler, evaporator, expansion valves and other auxiliary equipment. A resistance-capacity model structure is proposed to simulate the display cases. Semi-thermodynamic models are proposed to estimate reciprocating compressors volumetric efficiency and power consumption. Furthermore the zone modeling approach is used for evaporator simulation, and air-cooled gas cooler is modeled with distributed modeling method. The expansion valve simulation is based on orifice flow model. To calibrate these models, both manufacture performance data and experimental data are used. The experiments are conducted with a full instrumental CO 2 supermarket refrigeration system installed in Oak Ridge National Lab Flexible Research Platform (FRP). The simulation model can predict the system performance, including power consumption, cooling capacity, mass flow rate, temperature, and pressure, with high accuracy (within ±4%) compared to experimental data. In addition, this developed model has been used to create the system optimum high side pressure for high side expansion valve control, and to generate wide operating range simulation data for developing the batter-equivalent commercial refrigeration system model which can be used in grid interactive control development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Assessing the life-cycle environmental impacts of the wood pallet sector in the United States

Wood pallets play a critical role in the movement and storage of goods worldwide. They are an important component in the complex global supply chain and used by almost every industry. It is therefore important to assess the environmental implications of the wood pallet supply chain and identify optimization strategies that can be implemented. In this paper, primary 2018 annual production data collected from U.S. pallet manufacturers were used to develop the first industry-average life-cycle inventory (LCI). A new functional unit was proposed to perform a more refined and accurate environmental life-cycle assessment of the wood pallet supply chain. Using the LCI data developed, a cradle-to-grave industry-average life-cycle impact assessment was performed. This novel approach quantifies environmental impacts of a generic multi-use pallet, including repair and remanufacturing. The total global warming impact was 10.4 kg CO 2 e per 45.4 t of pallet loads of product delivered using wood pallets. The manufacturing stage contributed the most, about 35%, followed by the raw material supply stage. About 41% of total primary energy consumption was from renewable sources, with most sourced from biomass. Fossil fuels comprised about 52% of the total (225 MJ per functional unit) primary energy consumption. Total environmental impact was significantly affected by two main parameters: reference service life and load-bearing capacity. Pallet repair was also found to be an important component of the wood pallet supply chain, which has a low environmental footprint compared with the overall impact of a pallet and enables mitigation of overall impact by extending the reference service life. Finally, at end-of-life, common industry practices demonstrated substantial potential environmental benefits that can minimize overall environmental impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2020

Ultralow Thermal Conductivity and Thermoelectric Properties of Rb 2 Bi 8 Se 13

Complex bismuth chalcogenides are narrow band gap semiconductors with intrinsic low thermal conductivity, which exhibit high potential as highly efficient thermoelectric materials. Here, we assess the basic thermoelectric properties of polycrystalline Rb 2 Bi 8 Se 13 in the temperature range 300-823 K as well as performance-optimizing strategies. We find that the as-made bulk samples are single phase with the monoclinic crystal structure (P2 1 /m) and crystallize in layer morphology. The pristine sample exhibits an exceedingly low lattice thermal conductivity of 0.6- 0.46 W·m -1 ·K -1 at 300-823 K, which derives from strong lattice anharmonicity, large Grüneisen parameters, and low phonon velocities induced by the complex crystal structure with heavy atom Bi and a large unit cell. Here, the Cl doping successfully enhances the carrier concentration in Rb 2 Bi 8 Se 13 with a negligible impact on the electronic band structure, displaying common doping behaviors. Se vacancy, on the contrary, leads to n-type doping and enhances the effective mass and power factors more significantly. Consequently, a maximum ZT of ~0.75 at 823 K for the 0.3% Se-vacancy-doped sample is obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure-Based Analysis of Semisynthetic Anti-TB Rufomycin Analogues

This study employed structural information from cocrystals of rufomycin 4 (1a) and caseinolytic protein C1 (ClpC1)-NTD-wt to guide design and semisynthesis of rufomycin analogues, evaluate their antituberculosis (TB) biological profiles, and establish structure–activity relationships (SAR). Covering three regions of interest (ROIs, A–C) as modification sites, 14 of the 30 semisynthetic analogues (2–31) showed similar or improved MICs relative to the main natural precursors, rufomycins 4/6 (1a/b). Compounds 5 and 27 exhibited up to 10-fold enhanced potency against Mycobacterium tuberculosis (Mtb) in vitro, with MIC values of 1.9 and 1.4 nM, respectively. Evaluation of ClpC1-binding properties used existing ClpC1-NTD complexes with rufomycin 4 (PDB: 6cn8) and ecumicin (PDB: 6pbs) as references. The newly reported X-ray ClpC1-NTD cocrystal structure of 11 (syn. But4-Cl) revealed significant conformational effects involving the side chains of certain amino acids of the heptapeptide and confirmed the importance of ROIs A–C for medicinal chemistry efforts. Observed interactions of the N-terminal tail of ClpC1 with the rufomycin analogues vs ecumicin explains their different modes of inactivating the ClpC1/P1/P2 homeostatic machinery. Collectively, the observations inform further SAR optimization strategies for the rufomycin class of antibiotics and complement our understanding of their mode of action.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Full-Scale Ab Initio Simulation of Magic-Angle-Spinning Dynamic Nuclear Polarization

Theoretical models aimed at describing magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) NMR have great potential in facilitating the in silico design of DNP polarizing agents and formulations. These models must typically face a trade-off between the accuracy of a strict quantum mechanical description and the need for using realistically large spin systems, for instance, using phenomenological models. In this paper, we show that the use of aggressive state-space restrictions and an optimization strategy allows full-scale ab initio MAS-DNP simulations of spin systems containing thousands of nuclei. Our simulations are shown to reproduce experimental DNP enhancements quantitatively, including their MAS rate dependence, for both frozen solutions and solid materials. They also reveal the importance of a previously unrecognized structural feature found in some polarizing agents that helps minimize the sensitivity losses imposed by the spin diffusion barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Response Surface Methodology As a New Approach for Finding Optimal MALDI Matrix Spraying Parameters for Mass Spectrometry Imaging

Automated spraying devices have become ubiquitous in laboratories employing matrixassisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in part because they permit control of a number of matrix application parameters that can easily be reproduced for intra- and interlaboratory studies. Determining the optimal parameters for MALDI matrix application, such as temperature, flow rate, spraying velocity, number of spraying cycles, and solvent composition for matrix application, is critical for obtaining high-quality MALDI-MSI data. However, there are no established approaches for optimizing these multiple parameters simultaneously. Instead optimization is performed iteratively (i.e., one parameter at a time), which is time consuming and can lead to overall non-optimal settings. In this report, we demonstrate the use a novel experimental design and the response surface methodology to optimize five parameters of MALDI matrix application using a robotic sprayer. Thirtytwo combinations of MALDI matrix spraying conditions were tested, which allowed us to elucidate relationships between each of the application parameters as determined by MALDI-MS (specifically, using a 15 Tesla Fourier transform ion cyclotron resonance mass spectrometer). As such, we were able to determine the optimal automated spraying parameters that minimized signal de-localization and enabled high MALDI sensitivity. We envision this optimization strategy can be utilized for other MALDI-MSI applications, tissue types, and matrix application approaches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Quantifying the Value of Technology and Policy Innovation in Water Resource Portfolios

Abstract Recent water infrastructure planning models demonstrate that explicitly accounting for hydrological changes in long‐term water planning reduces costs and increases system robustness. However, current models do not consider the effects of other changes occurring in the system over long time horizons, namely technology and policy innovation. By leveraging state‐of‐the‐art dynamic control techniques, we propose a suite of tools that explicitly relate hydrologic change, technology innovation, and policy interventions to system‐level water costs. We design robust and cost‐optimal strategies for water supply expansion under hydrological variability and hundreds of plausible innovation states, for example, treatment cost and diversification improvements, deployment times, and demand reduction campaigns. Our approach circumvents the need to pre‐assign probabilities to innovation scenarios and, instead, directly calculates the effects of technology, policy, and hydrology variations on system cost and planning strategies to identify avenues of impactful innovation. These tools can support technology development hubs, urban, state, and federal water planners in anticipating cost implications of technology and policy innovation at the local level, and prioritizing high‐impact innovation goals based on quantitative targets. Results for the case study of Santa Barbara, California identify high‐value innovation attributes for the city as well as combinations of innovation attributes across technologies and policies, and quantify their potential utility cost reductions.

13 HYDRO ENERGY↗
DOE OSTI2020

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Probing lithium mobility at a solid electrolyte surface

Abstract Solid-state electrolytes overcome many challenges of present-day lithium ion batteries, such as safety hazards and dendrite formation 1,2 . However, detailed understanding of the involved lithium dynamics is missing due to a lack of in operando measurements with chemical and interfacial specificity. Here we investigate a prototypical solid-state electrolyte using linear and nonlinear extreme-ultraviolet spectroscopies. Leveraging the surface sensitivity of extreme-ultraviolet-second-harmonic-generation spectroscopy, we obtained a direct spectral signature of surface lithium ions, showing a distinct blueshift relative to bulk absorption spectra. First-principles simulations attributed the shift to transitions from the lithium 1 s state to hybridized Li- s /Ti- d orbitals at the surface. Our calculations further suggest a reduction in lithium interfacial mobility due to suppressed low-frequency rattling modes, which is the fundamental origin of the large interfacial resistance in this material. Our findings pave the way for new optimization strategies to develop these electrochemical devices via interfacial engineering of lithium ions.

25 ENERGY STORAGE↗
DOE OSTI2025

Nanoscopic strain evolution in single-crystal battery positive electrodes

Single-crystal Ni-rich layered oxides (SC-NMC) with a grain-boundary-free configuration have effectively addressed the long-standing cracking issue of conventional polycrystalline Ni-rich materials (PC-NMC) in lithium-ion batteries, prompting a shift in optimization strategies. However, continued reliance on anisotropic lattice volume change—a well-established failure indicator in PC-NMC—as a metric for understanding strain and guiding compositional design for SC-NMC becomes controversial. Here, in this study, by leveraging multiscale diagnostic techniques, we unravelled the distinct nanoscopic strain evolution in SC-NMC during battery operation, challenging the conventional composition-driven strategies and mechanical degradation indicators used for PC-NMC. Through particle-level chemomechanical analysis, we reveal a decoupling between mechanical stability and lattice volume change in SC-NMC, identifying that structural instability in SC materials is primarily driven by multidimensional lattice distortions induced by kinetics-driven reaction heterogeneity and progressively deactivating chemical phases. Using this mechanical failure mode, we redefine the roles of cobalt and manganese in maintaining mechanical stability. Unlike cobalt’s detrimental role in PC-NMC, we find cobalt to be critical in enhancing the longevity of SC-NMC by mitigating localized strain along the extended diffusion pathway, whereas manganese exacerbates mechanical degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Engineering high-coherence superconducting qubits

Advances in materials science and engineering have played a central role in the development of classical computers and will undoubtedly be critical in propelling the maturation of quantum information technologies. In approaches to quantum computation based on superconducting circuits, as one goes from bulk materials to functional devices, amorphous films and non-equilibrium excitations — electronic and phononic — are introduced, leading to dissipation and fluctuations that limit the computational power of state-of-the-art qubits and processors. In this Review, the major sources of decoherence in superconducting qubits are identified through an exploration of seminal qubit and resonator experiments. The proposed microscopic mechanisms associated with these imperfections are summarized, and directions for future research are discussed. The trade-offs between simple qubit primitives based on a single Josephson tunnel junction and more complex designs that use additional circuit elements, or new junction modalities, to reduce sensitivity to local noise sources are discussed, particularly in the context of materials optimization strategies for each architecture.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2025

The influence of V-defects, leakage, and random alloy fluctuations on the carrier transport in red InGaN MQW LEDs

Red InGaN-based light-emitting diodes (LEDs) exhibit lower internal quantum efficiencies than violet, blue, and green InGaN LEDs due to a reduction in radiative recombination rates relative to non-radiative recombination rates as the Indium composition increases. Additionally, the larger polarization and band offset barriers between high indium content InGaN quantum wells and GaN quantum barriers increase the forward voltage. In blue and green LEDs, random alloy fluctuations and V-defects play a key role in reducing the forward voltage. When V-defects are present, either naturally or intentionally introduced, they create an alternative path for carrier injection into the MQWs through the V-defect sidewalls. This injection mechanism explains the turn-on voltages of green LEDs. However, in InGaN red LEDs, these two phenomena do not reduce the forward voltage as effectively as in blue and green LEDs, and consequently, the computed forward voltage remains significantly higher than the measured one. Furthermore, currents are observed at low voltages before the turn-on voltage (V<ℏω/e=2.0 V) of red LEDs. To address this, we introduce dislocation-induced tail states in the modeling, suggesting that leakage current through these states may play a significant role both below and at turn-on voltages. The simulation also indicates that leakage carriers below turn-on accumulate, partially diffuse in the QWs, screen the polarization-induced barrier in the low injection regime, and further reduce the forward voltage. Despite these beneficial effects, a drawback of dislocation-induced tail states is the enhanced nonradiative recombination in the dislocation line region. This study provides a detailed analysis of device injection physics in InGaN QW red LEDs and outlines potential optimization strategies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Energetic particle physics: Chapter 7 of the special issue: on the path to tokamak burning plasma operation

We review the physics of energetic particles (EPs) in magnetically confined burning fusion plasmas with focus on advances since the last update of the ITER Physics Basis (Fasoli et al 2007 Nucl. Fusion 47 S264). Topics include basic EP physics, EP generation, diagnostics of EPs and instabilities, the interaction of EPs and thermal plasma instabilities, EP-driven instabilities, energetic particle modes (EPMs), and turbulence, linear and nonlinear stability and simulation of EP-driven instabilities and EPMs, 3D effects, scenario optimization strategies based on EP phase-space control, EPs in reduced field scenarios in ITER before DT, and the physics of runaway electrons. We describe the simulation and modeling of EPs in fusion plasmas, including instability drive and damping as well as EP transport, with a range of approaches from first-principles to reduced models, including gyrokinetic simulations, kinetic-MHD models, gyrofluid models, reduced models, and semi-analytical approaches.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Evaluation of the response to electrons and pions in the scintillating fiber and lead calorimeter for the future electron-ion collider

The performance of the Baby Barrel Electromagnetic Calorimeter (Baby BCAL) — a small-scale lead-scintillating-fiber (Pb/ScFi) prototype of the GlueX Barrel Electromagnetic Calorimeter (BCAL) — was tested in a dedicated beam campaign at the Fermilab Test Beam Facility (FTBF). This study provides a benchmark for the Pb/ScFi component of the future Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron-Ion Collider (EIC). The detector response to electrons and pions was studied at beam energies between 4 and 10 GeV, extending previous GlueX tests to a higher energy regime. The calibrated detector exhibits good linearity within uncertainties, and its electron energy resolution meets EIC requirements. The data further constrain the constant term in the energy resolution to below 1.9%, improving upon previous constraints at lower energies. Simulations reproduce key features of the electron and pion data within the limitations of the collected dataset and the FTBF test environment. Electron-pion separation in the test beam setup was analyzed using multiple methods, incorporating varying degrees of beam-related effects. The inclusion of longitudinal shower profile information enhanced the separation performance, underscoring its relevance for the full-scale BIC in ePIC. These results provide essential benchmarks for the Pb/ScFi section of the future BIC, validating detector simulations and guiding optimization strategies for electron-pion discrimination.

47 OTHER INSTRUMENTATION↗