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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

17 WIND ENERGY↗
DOE OSTI2017

Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations

With increasing wind power penetration in the electricity grid, system operators are recognizing the need for additional flexibility, and some are implementing new ramping products as a type of ancillary service. However, wind is generally thought of as causing the need for ramping services, not as being a potential source for the service. In this paper, a multi-timescale unit commitment and economic dispatch model is developed to consider the wind power ramping product (WPRP). An optimized swinging door algorithm with dynamic programming is applied to identify and forecast wind power ramps (WPRs). Designed as positive characteristics of WPRs, the WPRP is then integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. Numerical simulations on the modified IEEE 118-bus system show the potential effectiveness of WPRP in increasing the economic efficiency of power system operations with high levels of wind power penetration. It is found that WPRP not only reduces the production cost by using less ramping reserves scheduled by conventional generators, but also possibly enhances the reliability of power system operations. Moreover, wind power forecasts play an important role in providing high-quality WPRP service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Feed the fish: A review of aquaculture feeders and their strategic implementation

Automatic feeders are revolutionizing aquaculture by enabling precise and controlled feeding regimens that optimize growth, reduce feed waste, and enhance the overall efficiency of farming operations. Demand feeders provide some similar benefits but with feeding tailored to fish appetites and, in some cases, feed dispersal without electricity. This review explores the evolution, technological advancements, and applications of these feeders with a survey of recent research. It looks at common feeder designs and compares the pros and cons of five automatic feeder configurations in commercial use today. The article contrasts feeders and feed sizes for farmed species in different life stages and facilities, from hatcheries to ponds and offshore cages. It discusses the importance of species-tailored feeding strategies as well as impacts on fish behavior and health, nutrient delivery, and environmental sustainability. Incorporating sensors and on-site computing enables optimization in feeding algorithms that feeders then implement precisely and reliably. The review compiles results from multiple studies comparing growth rate, weight, and feed conversion ratio (FCR) outcomes from automatic feeders and demand feeders versus traditional hand-feeding regimens. The integration of feeders with real-time monitoring and data analytics is also discussed, highlighting future directions for research and adoption across the industry.

42 ENGINEERING↗
DOE OSTI2015

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗
DOE OSTI2024

RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing

Ensuring high-quality recommendations for newly onboarded users requires the continuous retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data. To serve the online DLRM retraining, existing solutions use hundreds of CPU computing nodes designated for input preprocessing, causing significant power consumption that surpasses even the power usage of GPU trainers. To this end, we propose RAP, an end-to-end DLRM training framework that supports Resource-aware Automated GPU sharing for DLRM input Preprocessing and Training. The core idea of RAP is to accurately capture the remaining GPU computing resources during DLRM training for input preprocessing, achieving superior training efficiency without requiring additional resources. Specifically, RAP utilizes a co-running cost model to efficiently assess the costs of various input preprocessing operations, and it implements a resource-aware horizontal fusion technique that adaptively merges smaller kernels according to GPU availability, circumventing any interference with DLRM training. In addition, RAP leverages a heuristic searching algorithm that jointly optimizes both the input preprocessing graph mapping and the co-running schedule to maximize the end-to-end DLRM training throughput. The comprehensive evaluation shows that RAP achieves 78.3× speedup on average over CPU-based DLRM input preprocessing frameworks. In addition, the end-to-end training throughput of RAP is only 2.04% lower than the ideal case, which has no input preprocessing overhead.

Wang, Zheng↗
DOE OSTI2016

Tycho 2, Version 0.1

Tycho 2 is a mini-app for testing performance of new algorithms and computational optimizations of neutral particle transport sweeps on 3D tetrahedral, unstructured grids.

Garrett, CharlesKris↗
DOE OSTI2026

BLING

The Bounded Linearized Iteration for Natural Gas (BLING) algorithm solves an optimization problem for gas pipeline flow scheduling that maximizes economic surplus and minimizes the energy used for gas compression subject to pressure, flow, and engineering constraints.

Zlotnik, Anatoly↗
DOE OSTI2026

FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]

The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.

Sirkis, Leah [National Laboratory of the Rockies (↗
DOE OSTI2016

Microhole Test Data

Drilling results from the microhole project at the Sandia High Operating Temperature test facility. The project is seeking to help reduce the cost of exploration and monitoring of geothermal wells and formations by drilling smaller holes. The tests were part of a control algorithm development to optimize the weight-on-bit (WOB) used during drilling with a percussive hammer.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Effect of Time Window and Spectral Measurement Options on Empirical Green’s Function Analysis Using DAS Array and Seismic Stations

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. Here, we focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

58 GEOSCIENCES↗
DOE OSTI2025

Twinac: initiation of a community-driven accelerator digital twin framework

We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.

Miceli, Tia [Fermilab]↗
DOE OSTI2016

Interface Control Document for the EMPACT Module that Estimates Electric Power Transmission System Response to EMP-Caused Damage

The EPREP code is designed to evaluate the effects of an Electro-Magnetic Pulse (EMP) on the electric power transmission system. The EPREP code embodies an umbrella framework that allows a user to set up analysis conditions and to examine analysis results. The code links to three major physics/engineering modules. The first module describes the EM wave in space and time. The second module evaluates the damage caused by the wave on specific electric power (EP) transmission system components. The third module evaluates the consequence of the damaged network on its (reduced) ability to provide electric power to meet demand. This third module is the focus of the present paper. The EMPACT code serves as the third module. The EMPACT name denotes EMP effects on Alternating Current Transmission systems. The EMPACT algorithms compute electric power transmission network flow solutions under severely damaged network conditions. Initial solutions are often characterized by unacceptible network conditions including line overloads and bad voltages. The EMPACT code contains algorithms to adjust optimally network parameters to eliminate network problems while minimizing outages. System adjustments include automatically adjusting control equipment (generator V control, variable transformers, and variable shunts), as well as non-automatic control of generator power settings and minimal load shedding. The goal is to evaluate the minimal loss of customer load under equilibrium (steady-state) conditions during peak demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Triggering on New Physics with the CMS Detector

The BU CMS group led by PI Tulika Bose has made several significant contributions to the CMS trigger and to the analysis of the data collected by the CMS experiment. Group members have played a leading role in the optimization of trigger algorithms, the development of trigger menus, and the online operation of the CMS High-Level Trigger. The group’s data analysis projects have concentrated on a broad spectrum of topics that take full advantage of their strengths in jets and calorimetry, trigger, lepton identification as well as their considerable experience in hadron collider physics. Their publications cover several searches for new heavy gauge bosons, vector-like quarks as well as diboson resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Ion bunch formation strategies for the JLEIC collider

An Electron-Ion Collider (EIC) has been proposed and supported as the next major nuclear physics facility in the United States, with very high design luminosities of 10^33 - 10^34 cm^-2 s^-1. The Jefferson Lab EIC concept, JLEIC, includes construction of a new ion beam accelerator complex. To achieve the required luminosity, the JLEIC ion collider requires unprecedentedly short ion bunches of 1 cm length at a high repetition frequency of 476 MHz. However, only much longer ion bunches are formed by the ion injector complex producing beams for the collider ring. Formation of short ion bunches from much longer ion bunches in this collider while limiting beam size growth is a fundamental challenge. This dissertation investigates several methods using longitudinal RF gymnastics to address this bunch formation challenge, including debunch/rebunch with barrier bucket manipulations and adiabatic bunch splitting. We discuss background of both methods, and show results of simulations that indicate that multiple adiabatic bunch splits are best suited to produce ion beam that satisfies the current JLEIC requirements. Adiabatic bunch splitting is also further optimized using genetic algorithms to find the optimum tradeoff between acceptable emittance growth and accelerator time necessary to perform the bunch formation process.

Gamage, Randika P.↗
DOE OSTI2018

Innovative Technologies for Optical Detection of Stress Corrosion Cracks

Stress corrosion cracks (SCC) represent a major concern for the structural integrity of engineered metal structures. In hazardous or restricted-access environments, remote detection of corrosion or SCC frequently relies on visual methods; however, with standard VT-1 visual inspection techniques, probabilities of SCC detection are low. Here, we develop and evaluate an improved optical sensor for SCC in restricted access-environments by combining a robotically controlled camera/fiber-optic based probe with software-based super-resolution imaging (SRI) techniques to increase image quality and detection of SCC. SRI techniques combine multiple images taken at different viewing angles, locations, or rotations, to produce a single higher- resolution composite image. We have created and tested an imaging system and algorithms for combining optimized, controlled camera movements and super- resolution imaging, improving SCC detection probabilities, and potentially revolutionizing techniques for remote visual inspections of any type.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Case Study: Nuclear-Renewable-Water Integration in Arizona

This document reports the application of the N-R HES software framework to a case study for Arizona Public Service (APS); the manager and part owner of the Palo Verde (PV) nuclear power plant. The case study is a work in progress of which this report presents a detailed description of the current model input data, assumptions and the corresponding results produced by the developed software framework. The goal of the report is, that with this information APS (together with INL) should be able to identify where more detailed data and more accurate model assumptions are necessary to reproduce all driving physical and economic phenomena in order to capture enough complexity of the reality to allow APS to use the results for their strategic decisions. The N-R HES software framework started being developed at INL two years ago [ref]. The framework has reached some level of maturity so that it can be applied to more than simple demonstration cases, e.g. real industry problems. Nevertheless, more capabilities are constantly added to accommodate the special needs of these challenging real life problems. The N-R HES framework is built on top of the RAVEN code [ref] and uses it as driver and workflow manager for all calculations. The framework has specifically been developed for the economic assessment of N-R HES systems. There are four main cornerstones of the N-R HES simulation framework: 1) generation of stochastic time series, 2) a probabilistic analysis and optimization set of algorithms available in RAVEN, 3) a set of models for representation of the physical behavior of N-R HES, and 4) a RAVEN plug-in that maps physical performance into economic performance called CashFlow. Within this framework, a broad spectrum of questions related to N-R HES can be addressed. One of the challenges currently of high interest is that the increasing penetration of variable renewables is altering the profile of the net demand (demand after removing all non curtailable renewable energy sources), with which the other generators on the grid have to cope. The N-R HES software framework is capable to analyze the potential feasibility of mitigating the resultant volatility in the net electricity demand. One possible solution to that problem currently intensively studied by the energy industry is adding stabilizing loads to the grid. These loads can be external industrial processes that are able to ramp production up and down quickly or adding variable internal loads that will lift the base load of the power plant. The latter is a possible solution currently studied by APS. APS if currently anticipating several challenges that will likely arise in the near future. One of them is, as mentioned, coping with the rapid growth of variable renewable energy (VRE) sources put on the grid. Although APS can sell its electricity for a fixed retail price to cover the local demand first, it also sells electricity in excess of the local demand at the Palo Verde energy hub. Due to the growing VRE penetration, the Palo Verde hub electricity spot price is negative more and more frequently. To mitigate this price volatility, APS is seeking to add more base load, so that the quantity of excess energy to be sold for prices potentially less than the internal retail price or even negative prices at the hub is reduced. A second challenge APS is facing is that their cooling water acquisition contract with the Sub Regional Operating Group (SROG) will expire soon and a renewal can only be done for a significantly higher price of the water. Therefore, APS is also seeking for alternative sources for their cooling water. An opportunity currently under investigation is to pump a limited quantity of brackish water from the regional ground water. Although much cheaper than the water from the new SROG contract, the salinity of the brackish water is so high that a blend of brackish and SROG water would need additional treatment to improve its quality for use in the cooling towers. An on-site reverse osmosis (RO) desalination plant is envisaged to reduce the salinity to an acceptable level. This report investigates the economic impact of such an RO plant. As one can see, the RO plant would help with both problems APS is facing: First, it would rise the APS base load helping to mitigate VRE induced hub price volatility. From this perspective, the RO plant can be seen as a stabilizing load on the grid as would be any other industrial process. Second, the RO’s goods, e.g. the clean water produced can be used by APS itself for a (hopefully) lower cost than 100% SROG water would be. The report will first describe the APS water procurement strategy in detail and lay out the possible alternative scenarios studied in this document. This is covered in Chapter 2. Next, in Chapter 3, the APS case model developed within the N-R HES software framework is described. This chapter includes also a description of the needed framework developments to accommodate the APS study.

97 MATHEMATICS AND COMPUTING↗