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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 1,027 records · Page 57

Evaluating an Earth system model from a water manager perspective

Abstract. The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.

Tye, Mari R. (ORCID:0000000324911020)↗

Direct Estimation of Kinetic Parametric Images for Dynamic PET

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed.

59 BASIC BIOLOGICAL SCIENCES↗

Equilibrium Reconstruction on the Large Helical Device

Equilibrium reconstruction is commonly applied to axisymmetric toroidal devices. Recent advances in computational power and equilibrium codes have allowed for reconstructions of three-dimensional fields in stellarators and heliotrons. We present the first reconstructions of finite beta discharges in the Large Helical Device (LHD). The plasma boundary and magnetic axis are constrained by the pressure profile from Thomson scattering. This results in a calculation of plasma beta without a-priori assumptions of the equipartition of energy between species. Saddle loop arrays place additional constraints on the equilibrium. These reconstruction utilize STELLOPT, which calls VMEC. The VMEC equilibrium code assumes good nested flux surfaces. Reconstructed magnetic fields are fed into the PIES code which relaxes this constraint allowing for the examination of the effect of islands and stochastic regions on the magnetic measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Review of multi-dimensional large-scale kinetic simulation and physics validation of ion acceleration in relativistic laser-matter interaction

Two new experimental technologies enabled realization of Break-out afterburner (BOA) - High quality Trident laser and free-standing C nm-targets. VPIC is an powerful tool for fundamental research of relativistic laser-matter interaction. Predictions from VPIC are validated - Novel BOA and Solitary ion acceleration mechanisms. VPIC is a fully explicit Particle In Cell (PIC) code: models plasma as billions of macro-particles moving on a computational mesh. VPIC particle advance (which typically dominates computation) has been optimized extensively for many different supercomputers. Laser-driven ions lead to realization promising applications - Ion-based fast ignition; active interrogation, hadron therapy.

ACCELERATION↗

2012 Gordon Research Conference on Defects in Semiconductors - Formal Schedule and Speaker/Poster Program

The meeting shall strive to develop and further the fundamental understanding of defects and their roles in the structural, electronic, optical, and magnetic properties of bulk, thin film, and nanoscale semiconductors and device structures. Point and extended defects will be addressed in a broad range of electronic materials of particular current interest, including wide bandgap semiconductors, metal-oxides, carbon-based semiconductors (e.g., diamond, graphene, etc.), organic semiconductors, photovoltaic/solar cell materials, and others of similar interest. This interest includes novel defect detection/imaging techniques and advanced defect computational methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Building Simulation Modelers are we big-data ready?

Recent advances in computing and sensor technologies have pushed the amount of data we collect or generate to limits previously unheard of. Sub-minute resolution data from dozens of channels is becoming increasingly common and is expected to increase with the prevalence of non-intrusive load monitoring. Experts are running larger building simulation experiments and are faced with an increasingly complex data set to analyze and derive meaningful insight. This paper focuses on the data management challenges that building modeling experts may face in data collected from a large array of sensors, or generated from running a large number of building energy/performance simulations. The paper highlights the technical difficulties that were encountered and overcome in order to run 3.5 million EnergyPlus simulations on supercomputers and generating over 200 TBs of simulation output. This extreme case involved development of technologies and insights that will be beneficial to modelers in the immediate future. The paper discusses different database technologies (including relational databases, columnar storage, and schema-less Hadoop) in order to contrast the advantages and disadvantages of employing each for storage of EnergyPlus output. Scalability, analysis requirements, and the adaptability of these database technologies are discussed. Additionally, unique attributes of EnergyPlus output are highlighted which make data-entry non-trivial for multiple simulations. Practical experience regarding cost-effective strategies for big-data storage is provided. The paper also discusses network performance issues when transferring large amounts of data across a network to different computing devices. Practical issues involving lag, bandwidth, and methods for synchronizing or transferring logical portions of the data are presented. A cornerstone of big-data is its use for analytics; data is useless unless information can be meaningfully derived from it. In addition to technical aspects of managing big data, the paper details design of experiments in anticipation of large volumes of data. The cost of re-reading output into an analysis program is elaborated and analysis techniques that perform analysis in-situ with the simulations as they are run are discussed. The paper concludes with an example and elaboration of the tipping point where it becomes more expensive to store the output than re-running a set of simulations.

Sanyal, Jibonananda↗

An Evaluation of Image Based Techniques for Early Wildfire Detection and Fuel Mapping

Few events can cause the catastrophic impact to ecology, infrastructure, and human safety of a wildland fire along the wildland urban interface. The suppression of natural wildland fires over the past decade has caused a buildup of dry, dead surface fuels: a condition that, coupled with the right weather conditions, can cause large destructive wildfires that are capable of threatening both ancient tree stands and manmade infrastructure. Firefighters use fire danger models to determine staffing needs on high fire risk days; however models are only as effective as the spatial and temporal density of their observations. OKFIRE, an Oklahoma initiative created by a partnership between Oklahoma State University and the University of Oklahoma, has proven that fire danger assessments close to the fire – both geographically and temporally – can give firefighters a significant increase in their situational awareness while fighting a wildland fire. This paper investigates several possible solutions for a small Unmanned Aerial System (UAS) which could gather information useful for detecting ground fires and constructing fire danger maps. Multiple fire detection and fuel mapping programs utilize satellites, manned aircraft, and large UAS equipped with hyperspectral sensors to gather useful information. Their success provides convincing proof of the utility that could be gained from low-altitude UAS gathering information at the exact time and place firefighters and land managers are interested in. Close proximity, both geographically and operationally, to the end can reduce latency times below what could ever be possible with satellite observation. This paper expands on recent advances in computer vision, photogrammetry, and infrared and color imagery to develop a framework for a next-generation UAS which can assess fire danger and aid firefighters in real time as they observe, contain, or extinguish wildland fires. It also investigates the impact information gained by this system could have on pre-fire risk assessments through the development of very high resolution fuel maps.

54 ENVIRONMENTAL SCIENCES↗

mdtmFTP

To address the high-performance challenges of data transfer in big data era, we research, develop, and implement mdtmFTP: a High-performance Data Transfer Tool in Bigdata Era. DOE’s Advanced Scientific Computing Research (ASCR) office has funded Fermilab and Brookhaven National Laboratory to collaboratively work on the Multicore-Aware Data Transfer Middleware (MDTM) project. MDTM aims to accelerate data movement toolkits on multicore systems. mdtmFTP is the latest outcome of this continued research effort. mdtmFTP is a high-performance data transfer tool that builds upon the MDTM middleware. Initial tests show that mdtmFTP performs better than existing data transfer tools

Zhang, L.↗

The Multicore-Aware Data Transfer Middleware Project

Existing data movement tools are still bound by major inefficiencies when running on multicore systems. To address these inefficiencies and limitations, DOE’s Advanced Scientific Computing Research (ASCR) office has funded Fermilab and Brookhaven National Laboratory to collaboratively work on the Multicore-Aware Data Transfer Middleware (MDTM) project. MDTM aims to accelerate data movement toolkits on multicore systems. A prototype version of MDTM is currently undergoing evaluation and enhancement.

Wu, Wenji↗

From Long-distance Entanglement to Building a Nationwide Quantum Internet: Report of the DOE Quantum Internet Blueprint Workshop

A Quantum Path Forward. Today, many scientific experts recognize that building and scaling quantum-protected and enhanced communication networks are among the most important technological frontiers of the 21st century. The international research community perceives the construction of a first prototype global quantum network-the Quantum Internet-to be within reach over the next decade. In February 2020, the U.S. Department of Energy (DOE)'s Office of Advanced Scientific Computing Research hosted the Quantum Internet Blueprint workshop to define a potential roadmap toward building the first nationwide quantum Internet. The workshop participants included representatives from DOE national laboratories, universities, industry, and other U.S. agencies with serious interests in quantum networking. The goal was to provide an outline of the essential research needed, detail any engineering and design barriers, and suggest a path forward to move from today's limited local network experiments to a viable, secure quantum Internet.

Peters, Nicholas↗

Review of Shape and Topology Optimization for Design of Air-to-Refrigerant Heat Exchangers

Air-to-refrigerant heat exchangers (HXs) have been the topic of exhaustive research as they are fundamental components of HVAC&R systems. It has been well-established that the large airside thermal resistance dominates the HX thermal resistance, and thus significant research efforts have focused on improving the air-side performance of these heat exchangers. As HXs continue to become more compact, thermal resistance reduction is typically realized through the utilization of extended secondary heat transfer surfaces such as fins. However, past research has shown that the thermal-hydraulic trade-offs provided by fins are often not attractive enough to warrant their use, especially for small diameter tubes. Yet, the inadequate primary surface area provided by compact HXs essentially mandate the necessity of fins to meet thermal resistance requirements. In recent years, advancements in computational tools such as Computational Fluid Dynamics (CFD) and optimization algorithms, coupled with the advent of additive manufacturing technologies, have allowed engineers to expand conventional HX design ideologies to include such concepts as shape and topology optimization. This lends itself directly to primary heat transfer surface optimization and even the potential removal of finned surfaces altogether. This paper presents a comprehensive literature review investigating air-to-refrigerant HX shape and topology optimization. The fundamentals of both shape and topology optimization, model development, and experimental validations are all separately discussed. Studies featuring manufactured prototypes and/or experimentally validated optimal designs are treated with additional emphasis. This paper concludes by identifying key research gaps and proposing future research directions for HX shape and topology optimization.

42 ENGINEERING↗

Application-driven Privacy-preserving Data Publishing with Correlated Attributes

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes - which may be hidden and can be inferred by machine learning algorithms - while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

97 MATHEMATICS AND COMPUTING↗

Model Predictive Control for a Grid-interactive Efficient Thermal Storage-integrated Heat Pump System

Building heating and cooling systems can be used to overcome the mismatch between the intermittent supply of renewable power and the fluctuating demand for electricity. A novel underground thermal energy storage integrated with a dual-source heat pump has been proposed to mitigate the mismatch while meeting the thermal demand of buildings efficiently. Conventional thermostat control with heuristic rules cannot provide intelligent decisions to maximize the thermal efficiency and flexibility of the proposed system. Advanced control strategies like model predictive control (MPC) have provided a new paradigm for grid-interactive efficient building operation with the advancement of computation and sensing. This study developed an MPC for the proposed system to provide grid service for Demand Side Management and minimize the operating cost of building owners. A control-oriented dynamic model of the proposed system has been developed. Given an objective function and proper constraints, an optimization problem is formulated to determine the optimal control strategy of the system. Dynamic Programming is adopted to solve the optimization problem. A rule-based control (RBC) is also developed to achieve similar goals. Short-term simulations are conducted to compare the system performance resulting from the two controls. The simulation results indicate that the MPC performs more intelligently than the RBC in charging thermal energy storage and selecting heat pump sources by taking advantage of the predicted cooling demands of the building and the performance of the integrated system. As a result, the MPC could save energy and reduce operating costs compared with the RBC. A case study shows that, for a 3-day operation, the MPC saves 36.9% energy and reduces 38.5% operating cost compared with the RBC.

Shi, Liang↗