Search NASA⌕ Search

SEARCH · Search NASA

Results for “data ordering”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

2nd and 3rd order spectral energy corrections with penumbral de-blurring methodology for opacity platform used on the National Ignition Facility

The Opacity Spectrometer (OpSpec) used in the National Ignition Facility’s opacity experiments measures x-ray spectra from 0.9 to 2.1 keV from the different experimental regions: the backlight source, emission source, and the absorption region with the transmission calculated from these regions. The OpSpec designs have gone through several iterations to help improve the signal-to-noise ratio, remove alternate crystal plane reflections, and improve spectral resolution, which helps to increase the validity of the opacity measurements. However, the source spans well outside the current working spectral range, and higher-order reflections are intrinsic to the crystal, which increases the overall signal seen in the data regions. The recorded data are the convolution of 1st order transmission, higher-order reflections, and the penumbra blurring. In conclusion, this work represents the details for deconvolving the 2nd and 3rd order spectral energy corrections with a penumbral de-blurring to correct the relative measurement of x-ray intensity of different spectral energies and further analysis of datasets relevant to the opacity experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Kilauea Magnetotelluric Dataset

In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the USGS Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kilauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kilauea and the East and Southwest Rift zones, which has implications for understanding Kilaueas plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone (ERZ), where electrical contact resistances are on the order of 100 kOhm. This data supports the continuing efforts to increase geothermal power on the island of Hawaii. Each of the 70 EDI files are the MT impedance tensors for 1 site. There is also a description of the processing of the data and a site map showing the locations of each site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Low Latency and High Data Rate (LLHD) Scheduler: A Multipath TCP Scheduler for Dynamic and Heterogeneous Networks

The scheduler is a crucial component of the multipath transmission control protocol (MPTCP) that dictates the path that a data packet takes. Schedulers are in charge of delivering data packets in the right order to prevent delays caused by head-of-line blocking. The modern Internet is a complicated network whose characteristics change in real-time. MPTCP schedulers are supposed to understand the real-time properties of the underlying network, such as latency, path loss, and capacity, in order to make appropriate scheduling decisions. However, the present scheduler does not take into account all of these characteristics together, resulting in lower performance. We present the low latency and high data rate (LLHD) scheduler, which successfully makes scheduling decisions based on real-time information on latency, path loss, and capacity, and achieves around 25% higher throughput and 45% lower data transmission delay than Linux’s default MPTCP scheduler.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Parametric model-order reduction for radiation transport using multi-resolution proper orthogonal decomposition

For parametric high-fidelity simulations, it is often desirable to utilize a reduced-order model (ROM) to emulate, at a reduced computational cost, parametric solutions of the governing partial differential equations (PDEs) for unseen parameter values. One commonly employed option is to utilize a data-driven, projection-based ROM supplemented with subspace identification via proper orthogonal decomposition (POD). POD discovers the ROM subspace by computing the singular value decomposition (SVD) of a set of training data from the full-order model (FOM). In streaming-dominated radiation transport simulations with localized sources, solutions often greatly vary over the spatial domain by many orders of magnitude. In such cases, machine-precision arithmetic can be insufficient to obtain an accurate SVD, resulting in a poorly performing ROM. We present a method called multiresolution POD (mrPOD) that mitigates these inaccuracies. The mrPOD method works by decomposing the spatial domain into regions and performing proper orthogonal decomposition on the training dataset separately in each region. In conclusion, mrPOD is tested on single energy group and multigroup atmospheric shielding transport problems and is shown to outperform classic POD.

42 ENGINEERING↗
DOE OSTI2023

PRESSURE-FILM OPEN SOURCE SCANNING AND MAPPING

SF-22-099 Pressure-film Open Source Scanning and Mapping (POSSM) is software that analyses images of pressure-film (scans or photographs). POSSM collects data from that analysis in order to produce a number of visual aides, including pressure maps, histograms, and 3d surface plots. POSSM is designed to be able to analyze an entire folder full of images, exporting numeric data as .csv files and visual graphics as .png files.

Grider, Patrick↗
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2020

FY 2020 Radionuclide Migration Project Status

The Waste Management Project provides safe, compliant, and cost-effective waste management services for the Hanford Site and the U.S. Department of Energy (DOE) complex. Part of these services includes safe disposal of low-level waste and mixed low-level waste at the Hanford Low-Level Waste Burial Grounds in accordance with the requirements of DOE Order 435.1, Radioactive Waste Management. To partially satisfy these requirements, performance assessment analyses were completed and approved. DOE Order 435.1 also requires continuing data collection to increase confidence in the critical assumptions used in these analyses to characterize the operational features of the disposal facility that are relied on to satisfy the performance objectives identified in the order.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Measured and projected beam backgrounds in the Belle II experiment at the SuperKEKB collider

The Belle II experiment at the SuperKEKB electron–positron collider aims to collect an unprecedented data set of 50 ab -1 to study CP -violation in the B -meson system and to search for Physics beyond the Standard Model. SuperKEKB is already the world’s highest-luminosity collider. In order to collect the planned data set within approximately one decade, the target is to reach a peak luminosity of 6 x 10 35 cm −2 s −1 by further increasing the beam currents and reducing the beam size at the interaction point by squeezing the betatron function down to β $^*_y$ = 0.3 mm. To ensure detector longevity and maintain good reconstruction performance, beam backgrounds must remain well controlled. We report on current background rates in Belle II and compare these against simulation. We find that a number of recent refinements have significantly improved the background simulation accuracy. Finally, we estimate the safety margins going forward. We predict that backgrounds should remain high but acceptable until a luminosity of at least 2.8 x 10 35 cm −2 s −1 is reached for β $^*_y$ = 0.6 mm. At this point, the most vulnerable Belle II detectors, the Time-of-Propagation (TOP) particle identification system and the Central Drift Chamber (CDC), have predicted background hit rates from single-beam and luminosity backgrounds that add up to approximately half of the maximum acceptable rates.

Detector background↗
DOE OSTI2021

Unexpected Hydride: Ce 4 B 2 C 2 H 2.42 , a Stuffed Variant of the Nd 2 BC Structure Type

Ce 4 B 2 C 2 H 2.42 was grown as large crystals from a cerium/copper eutectic flux. The structure was characterized by single-crystal X-ray and neutron diffraction and was found to be a stuffed variant of Nd 2 BC with the addition of two interstitial hydrogen positions. The tetrahedral hydrogen position is fully occupied, while the octahedral position has an occupancy of 42(3)%. Initial synthesis was due to hydrogen contamination of the cerium metal but has been successfully repeated using anthracene as a carbon and hydrogen source. Density of states calculations suggest that the incorporation of hydrogen stabilizes the compound with respect to the nonhydrided model. Magnetic susceptibility data show a complex magnetic ordering at 7.7 K that originates from the localized electron on the Ce 3+ in the structure. The trivalent state is also supported by X-ray photoelectron spectroscopy measurements. Heat capacity and electrical resistivity data show that the phase transition is broad in temperature, which may be due to structural disorder. Furthermore, the large low temperature value of C/T also indicates possible heavy fermion behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A purely kinetic description of the evaporation of water droplets

The process of water evaporation, although deeply studied, does not enjoy a kinetic description that captures known physics and can be integrated with other detailed processes such as drying of catalytic membranes embedded in vapor-fed devices and chemical reactions in aerosol whose volumes are changing dynamically. In this work, we present a simple, three-step kinetic model for water evaporation that is based on theory and validated by using well-established thermodynamic models of droplet size as a function of time, temperature, and relative humidity as well as data from time-resolved measurements of evaporating droplet size. Additionally, the kinetic mechanism for evaporation is a combination of two limiting processes occurring in the highly dynamic liquid-vapor interfacial region: direct first order desorption of a single water molecule and desorption resulting from a local fluctuation, described using third order kinetics. The model reproduces data over a range of relative humidities and temperatures only if the interface that separates bulk water from gas phase water has a finite width, consistent with previous experimental and theoretical studies. The influence of droplet cooling during rapid evaporation on the kinetics is discussed; discrepancies between the various models point to the need for additional experimental data to identify their origin.

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

Bayesian inference of anisotropic 2D small-angle scattering from sparse measurement

Here, we present a Bayesian inference framework for reconstructing anisotropic two-dimensional small-angle scattering (2D SAS) patterns from sparse, noisy, or partially missing data. The method combines a symmetry-aware angular basis with radial Gaussian process priors to enable accurate, training-free interpolation and denoising. Computational benchmarks demonstrate reliable recovery of both isotropic and high-order anisotropic features under severe data reduction. Experimental validations on stretched polymers, sheared wormlike micelles, and carbon fibers show improved fidelity and resolution compared to raw measurements, achieving comparable accuracy with up to 50-fold fewer detected neutrons. This approach enables quantitative structural analysis under low-flux, time-limited, or single-shot conditions, extending the applicability of 2D SAS techniques to compact neutron sources and mechanically driven soft matter systems undergoing transient structural changes.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2021

Finite-temperature vibrational full configuration interaction

Thermodynamic functions of an ideal molecular gas due to its anharmonic vibrations are evaluated in a wide range of temperature (T) by the vibrational full-configuration-interaction (FCI) method using a quartic force field and a finite number (N) of harmonic-oscillator basis functions along each normal mode. The thermodynamic functions considered are the grand potential (Ω), internal energy (U), and entropy (S). They are compared with those obtained from the Bose–Einstein theory with or without truncation of the harmonic-oscillator basis functions after quantum number N–1. The comparison reveals that the finite-basis-set errors in Ω and U are, respectively, k B Tln(k B T/Nℏω) and k B T per mode in the high-T limit, obscuring anharmonic effects when k B T > ℏω, where ω is the lowest mode frequency. Here, the benchmark data for several low-order perturbation corrections to Ω, U, and S are also obtained as the numerical derivatives of their FCI values with respect to dimensionless perturbation strength, and the domain of T and N in which these data are reliable (for the N → ∞ limits) is discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
DOE OSTI2020

Requirements Document for a Replacement Data Acquisition System

A flight vehicle is in the developmental stages. While in development flight testing is required by both the manufacturer and customer to rate design performance. The performance of the vehicle must fulfill the agreed upon requirements. Testing of a flight vehicle during operation requires various sensors in order to record and monitor performance for evaluation. A Data Acquisition System (DAQ) is needed in order to control and capture data from various sensors placed throughout the vehicle during flight. This DAQ will receive the analog data from the suite of sensors and convert them to a digital signal that can be transmitted to a recording station where the data will later be analyzed by test experts. Some of the key features to be included in the DAQ will be a self-check operation to verify the health of all sensors including the DAQ. The DAQ will also have the capability to store sensor calibration data files loaded during assembly and transmit those files to the recording station during flight. Breakthroughs in new technologies has allowed for higher resolution recording systems and faster sampling rates. Improved sensor data using a new Data Acquisition System (DAQ) can provide important answers to questions discovered during the last few flight tests. These questions arose when the flight system expressed unexpected behaviors. Having the capability of the new DAQ in flight tests would allow for more high-fidelity data to be captured from the system during flight tests and ultimately reducing the number of required flight test. Reducing the number of flight tests also reduce developmental cost and schedule.

42 ENGINEERING↗