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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

LEMD

Deep neural network-based force field enables researchers to study materials systems of large size and/or time scale efficiently with comparable accuracy to DFT-based methods. However, such force fields, if improperly trained or mistakenly selected, could result in ill-conditioned results. This SBIR Phase I project has explored the possibility to characterize and index force fields in Deep potential theme by quantizing local atomic environments of atoms in a simulation system. This project has also demonstrated the feasibility of building a public accessible web infrastructure providing high quality machine learning algorithm-based force field for next generation materials modeling and simulation to accelerate the study and discovery of new energy-related materials.

Su, Ninghai↗
DOE OSTI2025

Selecting 1D projections for 2D tomography reconstruction

Previous works on reconstructing the 4D phase space using tomography require optimal selection of projection views to achieve accurate reconstruction. In 2D reconstruction, the process is straightforward, as an object can be evenly sampled by dividing the angles evenly. However, extending this concept from 2D to 4D is not intuitive. This work demonstrates that quaternions can be used to more effectively describe views in 4D and introduces the Fibonacci Flower algorithm and repulsive force algorithm to evenly space views in 4D space in order to achieve higher reconstruction accuracy.

Accelerator Physics↗
DOE OSTI2017

Virtual Inertia: Current Trends and Future Directions

The modern power system is progressing from a synchronous machine-based system towards an inverter-dominated system, with a large-scale penetration of renewable energy sources (RESs) like wind and photovoltaics. RES units today represent a major share of the generation, and the traditional approach of integrating themas grid following units can lead to frequency instability. Many researchers have pointed towards using inverters with virtual inertia control algorithms so that they appear as synchronous generators to the grid, maintaining and enhancing system stability. Our paper presents a literature review of the current state-of-the-art of virtual inertia implementation techniques, and explores potential research directions and challenges. The major virtual inertia topologies are compared and classified. Through literature review and simulations of some selected topologies it has been shown that similar inertial response can be achieved by relating the parameters of these topologies through time constants and inertia constants, although the exact frequency dynamics may vary slightly. The suitability of a topology depends on system control architecture and desired level of detail in replication of the dynamics of synchronous generators. We present a discussion on the challenges and research directions which points out several research needs, especially for systems level integration of virtual inertia systems.

14 SOLAR ENERGY↗
DOE OSTI2017

Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

The discovery of new materials tailored for a given application typically requires the screening of a large number of compounds, and this process can be significantly accelerated by computational analysis. In such an approach the performance of a compound is correlated to a materials property, a so-called descriptor. Here we develop a descriptor-based approach for the adsorption of CO and NO to Cu, Ni, Co, and Fe sites in zeolites. Here we start out by discussing a possible design strategy for zeolite catalysts, define the studied test set of sites in the zeolites SSZ-13 and mordenite, and define a set of appropriate descriptors. In a subsequent step we use these descriptors in single-parameter, two-parameter, and multiparameter regression analysis and finally use a machine-learning genetic algorithm to reduce the number of variables. We find that one or two descriptors are not sufficient to accurately capture the interactions between molecules and metal centers in zeolites, and indeed a multiparameter approach is necessary. Even though many of the descriptors are directly correlated, we identify the position of the s orbital and the number of valence electrons of the active site as well as the HOMO–LUMO gap of the adsorbate as most important descriptors. Furthermore, the reconstruction of the active sites upon adsorption plays a crucial role, and when it is explicitly included in the analysis, correlations improve significantly. In the future we expect that the fundamental methodology developed here will be adapted and transferred to selected problems in adsorption and catalysis and will assist the rational design of materials for the given application.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

42 ENGINEERING↗
DOE OSTI2023

Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane

Boron-containing materials, such as hexagonal boron nitride (h-BN), recently shown to be active and selective catalysts for the oxidative dehydrogenation of propane (ODHP), have been shown to undergo significant surface oxyfunctionalization and restructuring. Although experimental ex situ studies have probed the change in chemical environment on the surface, the structural evolution of it under varying reaction conditions has not been established. Herein, we perform global optimization structure search with a grand canonical genetic algorithm to explore the chemical space of off-stoichiometric restructuring of the h-BN surface under ambient as well as ODHP-relevant conditions. A grand canonical ensemble representation of the surface is established, and the predicted 11B solid-state NMR spectra are consistent with previous experimental reports. In addition, we investigated the relative sliding of h-BN sheets and how it influences the surface chemistry with ab initio molecular dynamics simulations. Furthermore, the B–O linkages on the edges are found to be significantly strained during the sliding, causing the metastable sliding configurations to have higher reactivity toward the activation of propane and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Fuel Efficient Speed Optimization for Real-World Highway Cruising

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states. The proposed algorithm assumes piecewise constant slopes and speed limits, and it solves the transition vehicle speeds for each junction of such segments. In addition, a method to extract route information from digital maps is presented, which complies with the proposed optimization algorithm. The algorithms are evaluated in simulation, using Autonomie, a simulation tool for vehicle energy consumption modeling. The optimal control trajectory (e.g., torque, gear) is first computed offline. It is then applied in an open-loop fashion in online simulation, using a forward-looking model of the vehicle in Autonomie. Simulation results show over 8% fuel saving for two selected real-world routes.

Simulation and modeling, Optimization, Vehicle net↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2021

Impact of graph structures for QAOA on MaxCut

The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has been studied extensively on graphs with specific structure; however, little is known about the general performance of the algorithm on arbitrary graphs. Here, we investigate how different graph characteristics correlate with QAOA performance at depths at most three on the MaxCut problem for all connected non-isomorphic graphs with at most eight vertices. Some good predictors of QAOA success relate to graph symmetries, odd cycles, and density. For example, on eight vertex graphs, the average probability for selecting an optimal solution for graphs that contain no odd cycles after three iterations of QAOA is 60.6% compared to 48.2% for those that do. The data generated from these studies are shared in a publicly accessible database to serve as a benchmark for QAOA calculations and experiments. Knowing the relationship between structure and performance can be used to identify classes of combinatorial problems that are likely to exhibit a quantum advantage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. In conclusion, the proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Differentially Private Map Matching (DPMM) v1.0

Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching. This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints. DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model offers closer query responses to the raw data in terms of individual and aggregate trajectory-level statistics with an average at absolute deviation from the baseline for individual statistics on ϵ = 1.0. Beyond individual trajectory statistics, the DPMM outperforms the other benchmark DP-based mechanisms on different aggregate statistics with up to 8x improvement in utility.

Peisert, Sean [Lawrence Berkeley National Laborato↗
DOE OSTI2024

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗
DOE OSTI2025

SITCOMTN-162: Testing the implementation of Metadetection and Cell-Based Coadds on Abell 360 LSSTComCam data

The purpose of this technote is to test the technical quality of LSSTComCam commissioning data, specifically the Rubin_SV_38_7 field, by utilizing cell-based coadds and Metadetection by measuring the tangential and cross weak lensing shear profiles of the massive cluster Abell 360 (called A360 throughout the technote). The process entails generating the cell-based coadds for Metadetection to run on, identifying and removing cluster member galaxies, applying quality cuts, calibrating the shear measurements, and validation. Cell-based coadds and Metadetection are both currently in the process of being implemented within the LSST Science Pipelines at the time of this technote. There is substantial technical value in attempting a difficult measurement prior to full implementation. Measuring the tangential shear around A360 will showcase the current abilities of these algorithms, as well as highlight where work is still needed. As seen from the resulting shear profile of A360, the cell-based coadds and Metadetection are able to work in tandem to produce a shear catalog and resulting reduced shear profile. This technote is one part of a series studying A360 in order to both stress test the commissioning camera and demonstrate the technical capabilities of the Vera Rubin Observatory. We study the quality of the PSF modeling and impact it can have on cluster WL in [Combet et al., 2025], implementation of cell-based coadds and subsequent use for Metadetect [Sheldon et al., 2023] in this technote, photometric calibration in (in prep), source selection and photometric redshifts in [Adari et al., 2025], use of Anacal [Li et al., 2024] to produce a cluster shear profile in [Li et al., 2025], and background subtraction in this field and Fornax in [Zhou et al., 2025].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Comparison of calculated dose distributions reported as dose-to-water and dose-to-medium for intensity-modulated radiotherapy of nasopharyngeal cancer patients

Advanced dose calculation algorithms for radiation therapy treatment planning can report external beam photon dose 2-sided, in terms of dose-to-medium (D{sub m}) and dose-to-water (D{sub w}). The purpose of our study was to determinate the effect of D{sub w} and D{sub m} reporting modes built in Elekta Monaco treatment planning system on intensity-modulated radiotherapy dose distributions for patients with nasopharyngeal cancer. For 13 patients involved in this retrospective study, 2 plans were created: 1 using D{sub w} and another according to D{sub m} reporting mode. Treatment plans were normalized such that 100% planning target volume should be covered by 95% of prescribed dose. Dose-volume constraints were assigned according to international standards. The comparison between dose distributions was performed evaluating quantities important for respective volumes of interest. For target volumes, heterogeneity index and conformity index methodology were used along with the maximum dose concept. Also, for the comparisons over particular organ at risk, maximum dose or mean dose as well as dose-volume concepts were used. For all target volumes and majority of organs at risk, the differences between 2 reporting modes are statistically insignificant, but this is not the case for bony structured organs at risks: mandible and cochlea. It was observed that D{sub w} is higher than D{sub m} with mean difference of 9.91% (p = 0.000009) of the mandible volume covered with 70 Gy. The same trend was observed for left and right cochlea with difference in mean dose of 8.74% (p = 0.037) and 6.87% (p = 0.029), respectively. The comparative analysis of dosimetric parameters in this study shows that the selection of reporting modes in Monaco treatment planning system can produce dose differences up to 15% in high-density volumes such as mandible and cochlea, which might have clinical consequences.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Optimization of Error-Bounded Lossy Compression for Hard-to-Compress HPC Data

Since today’s scientific applications are producing vast amounts of data, compressing them before storage/transmission is critical. Results of existing compressors show two types of HPC data sets: highly compressible and hard to compress. In this work, we carefully design and optimize the error-bounded lossy compression for hard-tocompress scientific data. We propose an optimized algorithm that can adaptively partition the HPC data into best-fit consecutive segments each having mutually close data values, such that the compression condition can be optimized. Another significant contribution is the optimization of shifting offset such that the XOR-leading-zero length between two consecutive unpredictable data points can be maximized. We finally devise an adaptive method to select the best-fit compressor at runtime for maximizing the compression factor. We evaluate our solution using 13 benchmarks based on real-world scientific problems, and we compare it with 9 other state-of-the-art compressors. Experiments show that our compressor can always guarantee the compression errors within the user-specified error bounds. Most importantly, our optimization can improve the compression factor effectively, by up to 49% for hard-tocompress data sets with similar compression/decompression time cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Identification of boosted Higgs bosons decaying into b -quark pairs with the ATLAS detector at 13 TeV

This study describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, $H → b\bar{b}$, for proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy $\sqrt{\bar{s}}$ = 13 TeV. These decays are reconstructed from calorimeter jets found with the anti-$k_t R$ = 1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b -tagging of $R$ = 0.2 track-jets matched to the large- R calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb -1 of data collected in 2015 and 2016 using $g → b\bar{b}$ and $Z(→ b\bar{b})γ$ event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗