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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

IRIS: A Portable Runtime System Exploiting Multiple Heterogeneous Programming Systems

Across embedded, mobile, enterprise, and HPC systems, computer architectures are becoming more heterogeneous and complex. This complexity is causing a crisis in programming systems and performance portability. Several programming systems are working to address these challenges, but the increasing architectural diversity is forcing software stacks and applications to specialize for each architecture. As we show, all of these approaches critically depend on their runtime system for discovery, execution, scheduling, and data orchestration. To address this challenge, we believe that a more agile and proactive runtime system is essential to increase performance portability and improve user productivity. In this regard, we have designed and implemented IRIS: a portable runtime system exploiting multiple heterogeneous programming systems. IRIS can discover available resources, manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, OpenMP) simultaneously in the same execution, respect data dependencies, orchestrate data movement proactively, and provide for user-configurable scheduling. Our evaluation on three architectures, ranging from Qualcomm Snapdragon to a Summit supercomputer node, shows that IRIS improves portability across a wide range of diverse heterogeneous architectures with negligible overhead.

Kim, Jungwon↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
DOE OSTI2021

Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers

Neuromorphic computers offer the opportunity for low-power, efficient computation. Though they have been primarily applied to neural network tasks, there is also the opportunity to leverage the inherent characteristics of neuromorphic computers (low power, massive parallelism, collocated processing and memory) to perform non-neural network tasks. Here, we demonstrate how an approach for performing sparse binary matrix-vector multiplication on neuromorphic computers. We describe the approach, which relies on the connection between binary matrix-vector multiplication and breadth first search, and we introduce the algorithm for performing this calculation in a neuromorphic way. We validate the approach in simulation. Finally, we provide a discussion of the runtime of this algorithm and discuss where neuromorphic computers in the future may have a computational advantage when performing this computation.

Schuman, Catherine↗
DOE OSTI2016

Power Hardware-in-the-Loop Testing of Multiple Photovoltaic Inverters' Volt-Var Control with Real-Time Grid Model

Traditional testing methods fall short in evaluating interactions between multiple smart inverters providing advanced grid support functions due to the fact that such interactions largely depend on their placements on the electric distribution systems with impedances between them. Even though significant concerns have been raised by the utilities on the effects of such interactions, little effort has been made to evaluate them. In this paper, power hardware-in-the-loop (PHIL) based testing was utilized to evaluate autonomous volt-var operations of multiple smart photovoltaic (PV) inverters connected to a simple distribution feeder model. The results provided in this paper show that depending on volt-var control (VVC) parameters and grid parameters, interaction between inverters and between the inverter and the grid is possible in some extreme cases with very high VVC slopes, fast response times and large VVC response delays.

14 SOLAR ENERGY↗
DOE OSTI2018

Simultaneous and Constrained Calibration of Multiple Hyperspectral Images Through a New Generalized Empirical Line Model

The empirical line (EL) calibration method is commonly used for atmospheric correction of remotely sensed spectral images and recovery of surface reflectance. The current EL-based methods are applicable to calibrate only single images. Therefore, the use of the EL calibration is impractical for imaging campaigns, where many (partially overlapped) images are acquired to cover a large area. In addition, the EL results are unconstrained and an undesired reflectance with negative values or larger than 100% can be obtained. In this paper, we use the standard EL model to formulate a new generalized empirical line (GEL) model. Based on the GEL, we present a novel method for simultaneous and constrained calibration of multiple images. This new method allows for calibration through multiple image constrained empirical line (MIcEL) and three additional calibration modes. Given a set of images, we use the available ground targets and automatically extracted tie points between overlapping images to calibrate all the images in the set simultaneously. Quantitative and visual assessments of the proposed method were carried out relatively to the off-the-shelf method quick atmospheric correction (QUAC), using real hyperspectral images and field measurements. The results clearly show the superiority of MIcEL with respect to the minimization of the difference between the reflectance values of the same object in different overlapping images. An assessment of the absolute accuracy, with respect to 11 field measurement points, shows that the accuracy of MIcEL, with an average mean absolute error (MAE) of ~11%, is comparable with respect to the QUAC.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

Connecting automated vehicles to traffic lights can lead to significant energy savings by enabling them to pass through intersections in an energy-efficient way without unnecessary stops. A cellular-based communication system connecting multiple traffic lights can help realize the full potential of energy-efficient driving at intersections. Thus, we propose a hierarchical speed planner that can leverage information from multiple connected traffic lights. The proposed speed planner consists of two modules: a green window selector and a reference trajectory generator. The green window selector, based on Dijkstra’s algorithm, finds a series of "green windows" for connected traffic lights that builds an energy-optimal path for vehicles to follow. The reference trajectory generator finds optimal entering times, based on the selected green window at each intersection, and then computes reference trajectories. Deriving and using analytical optimal entering speeds as a function of entering times allows us to guarantee the computational simplicity suitable for real-time implementation. We also demonstrate how to balance energy and traffic flow perspectives in the reference trajectory generator. Lastly, a high-fidelity simulation framework is used to evaluate the proposed speed planner and quantify the extent to which it can save energy in various real-world urban route scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Performance of coincidence-based PSD on LiF/ZnS Detectors for Multiplicity Counting

Abstract: Mass accountancy measurement is a nuclear nonproliferation application which utilizes coincidence and multiplicity counters to verify special nuclear material declarations. With a well-designed and efficient detector system, several relevant parameters of the material can be verified simultaneously. 6LiF/ZnS scintillating sheets may be used for this purpose due to a combination of high efficiency and short die-away times in systems designed with this material, but involve choices of detector geometry and exact material composition (e.g., the addition of Ni-quenching in the material) that must be optimized for the application. Multiplicity counting for verification of declared nuclear fuel mass involves neutron detection in conditions where several neutrons arrive in a short time window, with confounding gamma rays. This paper considers coincidence-based Pulse-Shape Discrimination (PSD) techniques developed to work under conditions of high pileup, and the performance of these algorithms with different detection materials. Simulated and real data from modern LiF/ZnS scintillator systems are evaluated with these techniques and the relationship between the performance under pileup and material characteristics (e.g., neutron peak width and total light collection efficiency) are determined, to allow for an optimal choice of detector and material.

Robinson, Sean M.↗
DOE OSTI2016

Measurement and Comparison of the Light Output of Ni-doped 6LiF/ZnS for Use in Neutron Multiplicity Counting

Abstract–Alternatives to 3He for neutron detection have recently become attractive for safeguards applications. Pacific Northwest National Laboratory is developing a neutron multiplicity counter that is based on 6LiF/ZnS. Some of the properties of this material, such as the scintillation light decay time, can be tuned by doping the material with a small amount of nickel. This doping affects other properties of the material, in particular the time dependence of the scintillation light output. To determine whether the nickel-doped or undoped 6LiF/ZnS material would better suit the neutron multiplicity counter system, a series of experiments compared the use of undoped 6LiF/ZnS and a Ni-doped variant using both small samples and full-scale detectors made using the two materials. Both materials were manufactured by Eljen Technology of Sweetwater, Texas. The Ni-doped variant produced less light than the undoped material and had a shorter decay time. For all choices of detection threshold above the electronic noise, the decrease in light output did not affect the detection efficiency of the system.

Behling, Richard S.↗
DOE OSTI2015

Evaluation of Multiple Inverter Volt-VAR Control Interactions with Realistic Grid Impedances

Integration of large numbers of distributed photovoltaic (PV) systems in electric distribution circuits often requires advanced functions (e.g. volt-VAR, frequency-Watt etc.). However, significant concerns have been raised about potential for PV inverters with such controls to interact with one another in a way that could cause grid instability. The lack of standardized inverter models makes it hard to simulate such transient interactions in software. Similarly it is very hard to test these dynamic inverter interactions in the laboratory. In this paper, unique Power Hardware-in-the-Loop (PHIL) techniques are presented to experimentally test for interactions of multiple PV inverters connected to multiple points-of-common-coupling (PCCs) with grid impedances between them. Sample test results are provided from simulation-only scenarios and PHIL testing. Though simulation results indicated possible harmful interactions between inverters' volt-VAR controllers; no such interactions were found in the limited hardware testing.

14 SOLAR ENERGY↗
DOE OSTI2024

Optimal Managed Fast-Charging Model for Electric Vehicle Fleets with High Utilization and Multiple Charge-Acceptance Curves

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet - under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Distribution System State Estimation Using a Multiple Iteration Extended Kalman Filter Approach

To support the operation of modern distribution systems, operators require real-time visibility into system states. Due to a lack of measurements and unbalanced operation, the state estimation in distribution systems is challenging as compared to transmission systems. This paper proposes the utilization of a Multiple Iteration - Extended Kalman Filter based approach for the distribution system state estimation. This modified version of the baseline extended Kalman filter iterates over the update step multiple times thereby reducing the estimation error. The proposed algorithm along with the auxiliary algorithms such as bad data detection is integrated into a co-simulation environment. Case studies show that the proposed state estimation method can result in a lesser estimation error as compared to the baseline approach.

Bhatti, Bilal Ahmad↗
DOE OSTI2017

Modeling and Simulating Multiple Failure Masking enabled by Local Recovery for Stencil-based Applications at Extreme Scales

By obtaining multi-process hard failure resilience at the application level is a key challenge that must be overcome before the promise of exascale can be fully realized. Some previous work has shown that online global recovery can dramatically reduce the overhead of failures when compared to the more traditional approach of terminating the job and restarting it from the last stored checkpoint. If online recovery is performed in a local manner further scalability is enabled, not only due to the intrinsic lower costs of recovering locally, but also due to derived effects when using some application types. In this paper we model one such effect, namely multiple failure masking, that manifests when running Stencil parallel computations on an environment when failures are recovered locally. First, the delay propagation shape of one or multiple failures recovered locally is modeled to enable several analyses of the probability of different levels of failure masking under certain Stencil application behaviors. These results indicate that failure masking is an extremely desirable effect at scale which manifestation is more evident and beneficial as the machine size or the failure rate increase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Defining and Enabling Resiliency of Electric Distribution Systems With Multiple Microgrids

This paper presents a method for quantifying and enabling the resiliency of a power distribution system (PDS) using analytical hierarchical process and percolation theory. Using this metric, quantitative analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, develop proactive control actions to avert power system outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Furthermore, simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Microbial processing of plant remains is co-limited by multiple nutrients in global grasslands

Microbial processing of aggregate-unprotected organic matter inputs is key for soil fertility, long-term ecosystem carbon and nutrient sequestration and sustainable agriculture. In this work, we investigated the effects of adding multiple nutrients (nitrogen, phosphorus and potassium plus nine essential macro- and micro-nutrients) on decomposition and biochemical transformation of standard plant materials buried in 21 grasslands from four continents. Addition of multiple nutrients weakly but consistently increased decomposition and biochemical transformation of plant remains during the peak-season, concurrent with changes in microbial exoenzymatic activity. Higher mean annual precipitation and lower mean annual temperature were the main climatic drivers of higher decomposition rates, while biochemical transformation of plant remains was negatively related to temperature of the wettest quarter. Nutrients enhanced decomposition most at cool, high rainfall sites, indicating that in a warmer and drier future fertilized grassland soils will have an even more limited potential for microbial processing of plant remains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Call Center Arrivals: When to Jointly Forecast Multiple Streams?

We consider call centers that have multiple (potentially inter‐dependent) demand arrival streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly forecast future arrivals of the multiple streams. We first develop a general statistical model to simultaneously forecast multi‐stream arrival rates. The model takes into account three types of inter‐stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi‐stream forecasting model vary by the type, direction, and strength of inter‐stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi‐stream arrivals (instead of separately forecasting each stream), when there exists inter‐stream lag dependence among daily arrival rates. Empirical studies, using two real call center datasets further demonstrate our findings, and provide operational insights into how one chooses forecasting models for multi‐stream arrivals.

Ye, Han↗
DOE OSTI2020

Optimizing Piezoelectric Material Location and Size for Multiple-Mode Vibration Reduction of Turbomachinery Blades

Modern turbomachinery blades have extremely low inherent damping, which can lead to high transient vibrations and failure through high-cycle fatigue. Smart materials enable vibration reduction while meeting strict blade requirements such as weight and aerodynamic efficiency. In particular, piezoelectric-based vibration reduction offers the potential to reduce vibration semi-actively while simultaneously harvesting sufficient energy to power the implementation. The placement and the size of the piezoelectric material is critical to the vibration reduction capabilities of the system. Furthermore, the implementation should target multiple vibration modes. In this study, we develop a procedure to optimize electromechanical coupling across multiple vibration modes for a representative turbomachinery blade with surface-mounted piezoelectric patches. Experimental validation demonstrates good coupling across three targeted modes with a single piezoelectric patch. Placing the piezoelectric material in regions of high signed strain energy for all targeted modes enables vibration reduction across all of the targeted modes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synergistic Flow-Induced Oscillation of Multiple Cylinders in Harvesting Marine Hydrokinetic Energy

Abstract Flow-induced oscillations/vibrations (FIO/V) of cylinders in tandem can be enhanced by proper in-flow spacing to increase hydrokinetic energy harnessing. In a farm of multiple cylinders in tandem, the effect of interference on harnessing efficiency arises. Three years of systematic experiments in the Marine Renewable Laboratory (MRELab) of the University of Michigan, on an isolated cylinder, and two and three cylinders in tandem have revealed that synergistic FIO can enhance oscillations of cylinders in close proximity. Two cylinders in tandem can harness 2.5–13.5 times the hydrokinetic power of one isolated cylinder. Three cylinders in tandem can harness 3.4–26.4 times the hydrokinetic power of one isolated cylinder. Negative impact on the harnessed energy by multiple cylinders, such as the shielding effect for the downstream cylinder/s, is possible. Specifically for the three-cylinder configuration, at a certain flow speed, the decrease in the power of the middle cylinder can be overcome by adjusting its stiffness and/or damping.

Engineering↗