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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Correlation reflectometry at TEXTOR

In high temperature fusion plasmas the transport of energy and particles is commonly believed to be driven by turbulence. Turbulence quantities as correlation length and decorrelation time are important for the confinement properties of a plasma. Besides other diagnostics, correlation reflectometry has proven to be a suitable tool for the measurement of turbulence properties. At the medium sized Toroidal EXperiment for Technical Oriented Research (TEXTOR) the existing correlation reflectometry has been recently upgraded. A new reflectometer based on a microwave synthesizer has been developed and installed for the investigation of turbulence properties in a fusion plasma. Together with the existing reflectometer the measurement of radial correlation length and decorrelation time becomes available. Both reflectometers are computer controlled and allow to program individual frequency sequences and the duration of each frequency step. With the existing poloidal antenna array at {theta}=0 deg. and on top of the vacuum vessel, the system allows the measurement of radial correlation and poloidal correlations at the same time. First experiments have been performed and the results on the radial correlation length of density fluctuations in a fusion plasma are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Characterization of montmorillonite doped PVA/SA blends using X-ray diffraction

PVA films doped with Montmorillonite was prepared by slow evaporation technique. These films have been used to record X-ray patterns at room temperature. Correlation lengths and microstructural parameters were computed using in-house program employing X-ray data. Results show that correlation lengths as well as crystallite size increases with increase in the concentration of Montmorillonite which is inconformity with the conductivity studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Benchmarking characterization methods for noisy quantum circuits

Effective methods for characterizing the noise in quantum computing devices are essential for programming and debugging circuit performance. Existing approaches vary in the information obtained as well as the amount of quantum and classical resources required, with more information generally requiring more resources. Here we benchmark the characterization methods of gate set tomography, Pauli channel noise reconstruction, and empirical direct characterization for developing models that describe noisy quantum circuit performance on a 27-qubit superconducting transmon device. We evaluate these models by comparing the accuracy of noisy circuit simulations with the corresponding experimental observations. Here, we find that the agreement of noise model to experiment does not correlate with the information gained by characterization and that the underlying circuit strongly influences the best choice of characterization approach. Empirical direct characterization scales best of the methods we tested and produced the most accurate characterizations across our benchmarks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

DISP: Optimizations towards Scalable MPI Startup

Despite the popularity of MPI for high performance computing, the startup of MPI programs faces a scalability challenge as both the execution time and memory consumption increase drastically at scale. We have examined this problem using the collective modules of Cheetah and Tuned in Open MPI as representative implementations. Previous improvements for collectives have focused on algorithmic advances and hardware off-load. In this paper, we examine the startup cost of the collective module within a communicator and explore various techniques to improve its efficiency and scalability. Accordingly, we have developed a new scalable startup scheme with three internal techniques, namely Delayed Initialization, Module Sharing and Prediction-based Topology Setup (DISP). Our DISP scheme greatly benefits the collective initialization of the Cheetah module. At the same time, it helps boost the performance of non-collective initialization in the Tuned module. We evaluate the performance of our implementation on Titan supercomputer at ORNL with up to 4096 processes. The results show that our delayed initialization can speed up the startup of Tuned and Cheetah by an average of 32.0% and 29.2%, respectively, our module sharing can reduce the memory consumption of Tuned and Cheetah by up to 24.1% and 83.5%, respectively, and our prediction-based topology setup can speed up the startup of Cheetah by up to 80%.

Fu, Huansong↗
DOE OSTI2019

Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on four generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Zou, Pengfei↗
DOE OSTI2014

UPC++: A PGAS extension for C++

Partitioned Global Address Space (PGAS) languages are convenient for expressing algorithms with large, random-access data, and they have proven to provide high performance and scalability through lightweight one-sided communication and locality control. While very convenient for moving data around the system, PGAS languages have taken different views on the model of computation, with the static Single Program Multiple Data (SPMD) model providing the best scalability. In this paper we present UPC++, a PGAS extension for C++ that has three main objectives: 1) to provide an object-oriented PGAS programming model in the context of the popular C++ language, 2) to add useful parallel programming idioms unavailable in UPC, such as asynchronous remote function invocation and multidimensional arrays, to support complex scientific applications, 3) to offer an easy on-ramp to PGAS programming through interoperability with other existing parallel programming systems (e.g., MPI, OpenMP, CUDA). We implement UPC++ with a 'compiler-free' approach using C++ templates and runtime libraries. We borrow heavily from previous PGAS languages and describe the design decisions that led to this particular set of language features, providing significantly more expressiveness than UPC with very similar performance characteristics. We evaluate the programmability and performance of UPC++ using five benchmarks on two representative supercomputers, demonstrating that UPC++ can deliver excellent performance at large scale up to 32K cores while offering PGAS productivity features to C++ applications.

Zheng, Y↗
DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2015

Capacity Adequacy and Revenue Sufficiency in Electricity Markets With Wind Power

We present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, as well as periodic unit commitment and dispatch. The model is applied to analyze the impact of increasing wind power capacity on the optimal generation mix and the profitability of thermal generators. In a case study, we find that increasing wind penetration reduces energy prices while the prices for operating reserves increase. Moreover, scarcity pricing for operating reserves through reserve shortfall penalties significantly impacts the prices and profitability of thermal generators. Without scarcity pricing, no thermal units are profitable, however scarcity pricing can ensure profitability for peaking units at high wind penetration levels. Capacity payments can also ensure profitability, but the payments required for baseload units to break even increase with the amount of wind power. The results indicate that baseload units are most likely to experience revenue sufficiency problems when wind penetration increases and new baseload units are only developed when natural gas prices are high and wind penetration is low.

capacity adequacy↗
DOE OSTI2021

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2014

TH-C-12A-01: Develop a Patient-Specific QA Program for Radiation Therapy with On-Board MRI

Purpose: This work describes development of the first patient-specific quality assurance (QA) program for magnetic resonance imaging guided radiation therapy (MR-IGRT). Methods: The program consisted of following components: 1) multipoint ionization chamber (IC) measurement using a 15 cm3 cubic phantom, 2) 2D stacked radiographic film dosimetry using a 30×30×20 cm3 phantom with multiple inserted ICs, 3) 3D ArcCHECK measurement with a centrally inserted IC, 4) machine delivery file verification, 5) 3D Monte-Carlo dose re-calculation with machine delivery file and phantom CT, 6) 2-head mode delivery validation in case of a malfunctioning head, and 7) independent beam-on time calculation for non-IMRT fields. Both ADCL calibrated ICs and ArcCHECK were MRI compatible. Experimental data were analyzed for the first 10 patients treated at our institution. Results: The customized phantoms allowed measuring multiple points with ICs in one delivery. Absolute IC measurements were all within 3% in all phantom geometry/shape/material combinations. Despite known uncertainty associated with film dosimetry, passing rates greater than 90% were achieved in both absolute and composite modes using TG-129 criteria. Due to the simultaneous irradiation by three radiation sources, ArcCHECK was used as a 3D relative dosimeter with angular and energy dependences uncorrected. 95–100% passing rates were obtained and the centrally inserted IC measurement assured that the overall dose normalization was within 3%. Machine delivery file verification and MC recalculated dose to the phantom results showed 98–100% passing rates, providing opportunity of moving from gamma passing rates to patient DVHbased QA metrics. Same results were obtained for the 2-head delivery mode. Manual beam-on time calculation for non-IMRT fields showed better than 5% agreement. Conclusion: We have successfully developed the first MRIGRT patient specific QA program by adopting experimental and computational dosimetry methods that were developed in the past decade for other radiation therapy modalities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.

14 SOLAR ENERGY↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗
DOE OSTI2020

A GPU-based 2D shallow water quality model

In this paper, a 2D shallow water flow solver integrated with a water quality model is presented. The interaction between the main water quality constituents included is based on the Water Quality Analysis Simulation Program. Efficiency is achieved by computing with a combination of a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU) device. This technique is intended to provide robust and accurate simulations with high computation speedups with respect to a single-core CPU in real events. The proposed numerical model is evaluated in cases that include the transport and reaction of water quality components over irregular bed topography and dry–wet fronts, verifying that the numerical solution in these situations conserves the required properties (C-property and positivity). The model can operate in any steady or unsteady form allowing an efficient assessment of the environmental impact of water flows. The field data from an unsteady river reach test case are used to show that the model is capable of predicting the measured temporal distribution of dissolved oxygen and water temperature, proving the robustness and computational efficiency of the model, even in the presence of noisy signals such as wind speed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

RELAP5-3D Developer Guidelines and Programming Practices

Our ultimate goal is to create and maintain RELAP5-3D as the best software tool available to analyze nuclear power plants. This begins with writing excellent programming and requires thorough testing. This document covers development of RELAP5-3D software, the behavior of the RELAP5-3D program that must be maintained, and code testing.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Final Project Report: Tools for the Development of High Performance Energy Applications and Systems

This project concentrated on various aspects of creating tool infrastructure to make it easier to program large-scale parallel computers. The research conducted during this project is summarized in this report. The complete details of the work are available in the ten publications listed at the end of the report. Many of the concepts created during this project have been incorporated into tools and made available as freely downloadable software (at www.dyninst.org). It also supported the Ph.D. studies of three students.

Hollingsworth, Jeff↗
DOE OSTI2019

Summing Corrections For La-140 On Counter 137 [Slides]

The counting of filters for gamma activity is often done with the sample situated very close or wrapped around the detector. This improves the counting statistics but can increase the probability of summing and affect the calculation of yield. An MCNP model of counter 137 (a 65% HPGe detector) was used to compute the total and peak efficiencies for two sample types, 40 and 20. The type 40 is a long filter strip wrapped in a Marinelli beaker which sits around the detector in an annular fashion. The type 20 is a 8.4 in radius filter, quarter-folded and placed flat on the detector face. A comparison of the peak efficiency computed in MCNP was compared to the measured efficiency to ensure consistency. Using the measured peak efficiency and MCNP-computed total efficiency, the TrueCoinc program was run to estimate the summing values for La-140 peaks. The results showed the summing out of two peaks (487 and 1596 keV) was around 6-8% in for both sample types.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Strengthening Nanotwinned Metals beyond the Hall-Petch Limit

A research partnership program was conducted to study the roles of trace element segregation as a fundamentally new mechanism of grain-boundary and twin-boundary strengthening in nanocrystalline-nanotwinned face-centered-cubic silver metals. The goals were to create new metals that break the decades-old theoretical Hall-Petch strength limit, and to study a new class of super-strong conducting materials. This program combined synergistic experimental and computational studies at the atomic scale, using a wide range of resources and scientific expertise at the University of Vermont, Ames Laboratory and Lawrence Livermore National Laboratory. This research led to a discovery that annealing of nanotwinned silver with trace concentrations of Cu solute atoms (<1.0 wt.%) results in grain sizes and twin spacings well below those previously obtained for silver. These new materials, termed as nanocrystalline-nanotwinned silver, showed a record hardness 42% higher than the Hall-Petch limit in pure nanotwinned silver or stronger metals like copper, with excellent retention of electrical conductivity and microstructure stability at elevated temperature. This research used large-scale hybrid Monte-Carlo/molecular-dynamics atomistic simulations and ab-initio calculations to predict new impurity-segregation behaviors and fundamentally new plastic deformation mechanisms in nanocrystalline-nanotwinned silver metals. A new interatomic potential for atomistic simulation of segregation of Ni impurity in Ag grain-boundaries and plasticity of Ag-Ni alloys was developed.

36 MATERIALS SCIENCE↗