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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Resistive Network Optimal Power Flow: Uniqueness and Algorithms

The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.

Tan, CW↗
DOE OSTI2019

Efficient Projection onto the $\ell_{\infty,1}$ Mixed-Norm Ball Using a Newton Root Search Method

Mixed norms that promote structured sparsity have numerous applications in signal processing and machine learning problems. Here, we present a new algorithm, based on a Newton root search technique, for computing the projection onto the $\ell_{\infty,1}$ ball, which has found application in cognitive neuroscience and classification tasks. Numerical simulations show that our proposed method is between 8 and 10 times faster on average, and up to 20 times faster for very sparse solutions, than the previous state of the art. Lastly, tests on real functional magnetic resonance image data show that, for some data distributions, our algorithm can obtain speed improvements by a factor of between 10 and 100, depending on the implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhanced Control, Optimization, and Integration of Distributed Energy Applications (ECO-IDEA)

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. Here, in this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Work stealing for GPU-accelerated parallel programs in a global address space framework

Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a function of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Final Report on HOLODEC 2 Technology Readiness Level

During the period of this project, the Holographic Detector for Clouds 2 (HOLODEC 2) instrument has advanced from a laboratory-proven instrument with some initial field testing to a fully flight-tested instrument capable of providing useful cloud microphysics measurements. This can be summarized as 'Technology Readiness Level 8: Technology is proven to work - Actual technology completed and qualified through test and demonstration.' As part of this project, improvements and upgrades have been made to the optical system, the instrument power control system, the data acquisition computer, the instrument control software, the data reconstruction and analysis software, and some of the basic algorithms for estimating basic microphysical variables like droplet diameter. Near the end of the project, the instrument flew on several research flights as part of the IDEAS 2011 project, and a small sample of data from the project is included as an example. There is one caveat in the technology readiness level stated above: the upgrades to the instrument power system were made after the flight testing, so they are not fully field proven. We anticipate that there will be an opportunity to fly the instrument as part of the IDEAS project in fall 2012.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2016

PIPS-SBB: A Parallel Distributed-Memory Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve further as more functionality is added in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

TASK ALLOCATION IN GEO-DISTRIBUTED CYBER-PHYSICAL SYSTEMS

This paper studies the task allocation algorithm for a distributed test facility (DTF), which aims to assemble geo-distributed cyber (software) and physical (hardware in the loop components into a prototype cyber-physical system (CPS). This allows low cost testing on an early conceptual prototype (ECP) of the ultimate CPS (UCPS) to be developed. The DTF provides an instrumentation interface for carrying out reliability experiments remotely such as fault propagation analysis and in-situ testing of hardware and software components in a simulated environment. Unfortunately, the geo-distribution introduces an overhead that is not inherent to the UCPS, i.e. a significant time delay in communication that threatens the stability of the ECP and is not an appropriate representation of the behavior of the UCPS. This can be mitigated by implementing a task allocation algorithm to find a suitable configuration and assign the software components to appropriate computational locations, dynamically. This would allow the ECP to operate more efficiently with less probability of being unstable due to the delays introduced by geo-distribution. The task allocation algorithm proposed in this work uses a Monte Carlo approach along with Dynamic Programming to identify the optimal network configuration to keep the time delays to a minimum.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Monitoring deformation at the Geysers Geothermal Field, California using C-band and X-band interferometric synthetic aperture radar

In this study, we resolve deformation at The Geysers Geothermal Field using two distinct sets of interferometric synthetic aperture radar (InSAR) data. The first set of observations utilize archived European Space Agency C-band synthetic aperture radar data from 1992 through 1999 to image the long-term and large-scale subsidence at The Geysers. The peak range velocity of approximately 50 mm/year agrees with previous estimates from leveling and global positioning system observations. Data from a second set of measurements, acquired by TerraSAR-X satellites, extend from May 2011 until April 2012 and overlap the C-band data spatially but not temporally. These X-band data, analyzed using a combined permanent and distributed scatterer algorithm, provide a higher density of scatterers (1122 per square kilometer) than do the C-band data (12 per square kilometer). The TerraSAR-X observations resolve 1 to 2 cm of deformation due to water injection into a Northwest Geysers enhanced geothermal system well, initiated on October 2011. Lastly, the temporal variation of the deformation is compatible with estimates from coupled numerical modeling.

58 GEOSCIENCES↗
DOE OSTI2023

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. Here we explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

A second-order distributed memory parallel fast sweeping method for the Eikonal equation

The Eikonal equation is used to calculate wave propagation and distance fields, and due to its complexity requires numerical treatment for its solution. In this work, we present a second-order distributed memory parallel fast sweeping method. The second-order solution switches on a two-point stencil when two upwind points are available, and reverts to first-order otherwise. In all examples, the second-order method improves the solution over the first-order, allowing for significant savings in memory while achieving the same accuracy. Parallelization over distributed memory saw good weak scaling with optimal convergence. The computational time for second-order was approximately 2.5 times slower than first-order, where the largest amount of mesh points ran on 144 cores (512 GB) was ≈20 billion. The savings in memory from the second-order method combined with the distributed memory algorithm result in the ability to solve problems much larger than are possible with the serial first-order method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Massively-parallel Lagrangian particle code and applications

Massively-parallel, distributed-memory algorithms for the Lagrangian particle hydrodynamic method (Samulyak et al., 2018) have been developed, verified, and implemented. The key component of parallel algorithms is a particle management module that includes a parallel construction of octree databases, dynamic adaptation and refinement of octrees, and particle migration between parallel subdomains. The particle management module is based on the p4est (parallel forest of k-trees) library. The massively-parallel Lagrangian particle code has been applied to a variety of fundamental science and applied problems. A summary of Lagrangian particle code applications to the injection of impurities into thermonuclear fusion devices and to the simulation of supersonic hydrogen jets in support of laser-plasma wakefield acceleration research has also been presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Path-accelerated stochastic molecular dynamics: Parallel-in-time integration using path integrals

Massively parallel computer architectures create new opportunities for the performance of long-time scale molecular dynamics (MD) simulations. Here, we introduce the path-accelerated molecular dynamics method that takes advantage of distributed computing to reduce the wall-clock time of MD simulation via parallelization with respect to stochastic MD time steps. The marginal distribution for the time evolution of a system is expressed in terms of a path integral, enabling the use of path sampling techniques to numerically integrate MD trajectories. By parallelizing the evaluation of the path action with respect to time and by initializing the path configurations from a nonequilibrium distribution, the algorithm enables significant speedups in terms of the length of MD trajectories that can be integrated in a given amount of wall-clock time. The method is demonstrated for Brownian dynamics, although it is generalizable to other stochastic equations of motion including open systems. We apply the method to two simple systems, a harmonic oscillator and a Lennard-Jones liquid, and we show that in comparison to the conventional Euler integration scheme for Brownian dynamics, the new method can reduce the wall-clock time for integrating trajectories of a given length by more than three orders of magnitude in the former system and more than two in the latter. This new method for parallelizing MD in the dimension of time can be trivially combined with algorithms for parallelizing the MD force evaluation to achieve further speedup.

Rosa-Raíces, Jorge L.↗
DOE OSTI2014

The solar neighborhood. XXXII. The hydrogen burning limit

We construct a Hertzsprung-Russell diagram for the stellar/substellar boundary based on a sample of 63 objects ranging in spectral type from M6V to L4. We report newly observed VRI photometry for all 63 objects and new trigonometric parallaxes for 37 objects. The remaining 26 objects have trigonometric parallaxes from the literature. We combine our optical photometry and trigonometric parallaxes with 2MASS and WISE photometry and employ a novel spectral energy distribution fitting algorithm to determine effective temperatures, bolometric luminosities, and radii. Our uncertainties range from ∼20 K to ∼150 K in temperature, ∼0.01 to ∼0.06 in log (L/L {sub ☉}) and ∼3% to ∼10% in radius. We check our methodology by comparing our calculated radii to radii directly measured via long baseline optical interferometry. We find evidence for the local minimum in the radius-temperature and radius-luminosity trends that signals the end of the stellar main sequence and the start of the brown dwarf sequence at T {sub eff} ∼ 2075 K, log (L/L {sub ☉}) ∼ –3.9, and (R/R {sub ☉}) ∼ 0.086. The existence of this local minimum is predicted by evolutionary models, but at temperatures ∼400 K cooler. The minimum radius happens near the locus of 2MASS J0523–1403, an L2.5 dwarf with V – K = 9.42. We make qualitative arguments as to why the effects of the recent revision in solar abundances accounts for the discrepancy between our findings and the evolutionary models. We also report new color-absolute magnitude relations for optical and infrared colors which are useful for estimating photometric distances. We study the optical variability of all 63 targets and find an overall variability fraction of 36{sub −7}{sup +9}% at a threshold of 15 mmag in the I band, which is in agreement with previous studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Cooperative Management of a Lithium-Ion Battery Energy Storage Network: A Distributed MPC Approach

This paper presents a study of cooperative power supply and storage for a network of Lithium-ion energy storage systems (LiBESSs). We propose to develop a distributed model predictive control (MPC) approach for two reasons. First, able to account for the practical constraints of a LiBESS, the MPC can enable a constraint-aware operation. Second, a distributed management can cope with a complex network that integrates a large number of LiBESSs over a complex communication topology. With this motivation, we then build a fully distributed MPC algorithm from an optimization perspective, which is based on an extension of the alternating direction method of multipliers (ADMM) method. A simulation example is provided to demonstrate the effectiveness of the proposed algorithm.

Fang, Huazhen↗
DOE OSTI2013

EDR: An Energy-Aware Runtime Load Distribution System for Data-Intensive Applications in the Cloud

Data centers account for a growing percentage of US power consumption. Energy e_ciency is now a first-class design constraint for the data centers that support cloud services. Service providers must distribute their data e_ciently across multiple data centers. This includes creation of data replicas that provide multiple copies of data for e_cient access. However, selecting replicas to maximize performance while minimizing energy waste is an open problem. State of the art replica selection approaches either do not address energy, lack scalability and/or are vulnerable to crashes due to use of a centralized coordinator. Therefore, we propose, develop and evaluate a simple cost-oriented decentralized replica selection system named EDR, implemented with two distributed optimization algorithms

Cloud software↗