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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Parallel physics-informed neural networks via domain decomposition

Here we develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs), which employ domain decomposition in space and in time-space, respectively. This domain decomposition endows cPINNs and XPINNs with several advantages over the vanilla PINNs, such as parallelization capacity, large representation capacity, efficient hyperparameter tuning, and is particularly effective for multi-scale and multi-physics problems. Here, we present a parallel algorithm for cPINNs and XPINNs constructed with a hybrid programming model described by MPI + X, where X ∈ {CPUs, GPUs}. The main advantage of cPINN and XPINN over the more classical data and model parallel approaches is the flexibility of optimizing all hyperparameters of each neural network separately in each subdomain. We compare the performance of distributed cPINNs and XPINNs for various forward problems, using both weak and strong scalings. Our results indicate that for space domain decomposition, cPINNs are more efficient in terms of communication cost but XPINNs provide greater flexibility as they can also handle time-domain decomposition for any differential equations, and can deal with any arbitrarily shaped complex subdomains. To this end, we also present an application of the parallel XPINN method for solving an inverse diffusion problem with variable conductivity on the United States map, using ten regions as subdomains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Chapter 3: Solar Optics and Principles

Solar irradiation originating from a finite-sized sun disc, as well as its various interactions with the atmosphere and reflective elements, results in the distribution of concentrated light at the receiver. The study of these interactions and the computation of the resulting flux distribution constitute the field of CSP optics. CSP optics is subdivided into two categories, namely, path-independent optics and geometric optics. This chapter deals primarily with the technology agnostic path-independent optics and attempts to convey some fundamental principles and to guide researchers. The path-independent optics introduced here includes sun shapes, the impact of surface microscopic and macroscopic structure, and atmospheric attenuation.

concentrating solar power↗
DOE OSTI2020

Tools Assessing Performance

For the distributed wind industry, it can be challenging to accurately predict the performance and annual energy production of projects prior to their installation. The U.S. Department of Energy’s Tools Assessing Performance (TAP) project aims to improve wind resource characterization, thereby reducing the uncertainty of project performance and financing costs, increasing consumer confidence, and lowering the levelized cost of distributed wind energy. A collaborative effort among DOE National Laboratories, TAP will create a computational framework that provides the distributed wind community with access to newly developed wind resource data and modeling capabilities. These capabilities will allow users to perform timely and accurate performance assessments for distributed wind projects at locations across the United States.

wind, distributed, tools, performance↗
DOE OSTI2019

Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overlapping, and potentially erroneous DNA segments from environmental samples into the accurate representation of the underlying microbiomes's genomes. State-of-the-art tools require big shared memory machines and cannot handle contemporary metagenome datasets that exceed Terabytes in size. In this paper, we introduce the MetaHipMer pipeline, a high-quality and high-performance metagenome assembler that employs an iterative de Bruijn graph approach. MetaHipMer leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is end-to-end parallelized using the Unified Parallel C language and therefore can run seamlessly on shared and distributed-memory systems. Experimental results show that MetaHipMer matches or outperforms the state-of-the-art tools in terms of accuracy. Ultimately, MetaHipMer scales efficiently to large concurrencies and is able to assemble previously intractable grand challenge metagenomes. We demonstrate the unprecedented capability of MetaHipMer by computing the first full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion reads - size 2.6 TBytes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Neutron star masses and radii from quiescent low-mass x-ray binaries

We perform a systematic analysis of neutron star radius constraints from five quiescent low-mass X-ray binaries and examine how they depend on measurements of their distances and amounts of intervening absorbing material, as well as their assumed atmospheric compositions. We construct and calibrate to published results a semi-analytic model of the neutron star atmosphere which approximates these effects for the predicted masses and radii. Starting from mass and radius probability distributions established from hydrogen-atmosphere spectral fits of quiescent sources, we apply this model to compute alternate sets of probability distributions. We perform Bayesian analyses to estimate neutron star mass-radius curves and equation of state (EOS) parameters that best-fit each set of distributions, assuming the existence of a known low-density neutron star crustal EOS, a simple model for the high-density EOS, causality, and the observation that the neutron star maximum mass exceeds 2 M {sub ☉}. We compute the posterior probabilities for each set of distance measurements and assumptions about absorption and composition. We find that, within the context of our assumptions and our parameterized EOS models, some absorption models are disfavored. We find that neutron stars composed of hadrons are favored relative to those with exotic matter with strong phase transitions. In addition, models in which all five stars have hydrogen atmospheres are found to be weakly disfavored. Our most likely models predict neutron star radii that are consistent with current experimental results concerning the nature of the nucleon-nucleon interaction near the nuclear saturation density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Calculation and use of pointwise effective cross-sections in the unresolved resonance region - 14605

The pointwise neutron elastic scattering, absorption, fission and total cross-sections in the Unresolved Resonance Region (URR) can be calculated with a code C-007 [1]. The volume of these data is large and estimated to make about 106 numbers for each of the reactions. These data volumes may cause time and memory problems in solving radiation transport by the Monte Carlo method. There are methods to thin the data. These are, for example, the widely used probability tables [2] and subgroups [3]. The paper offers another data thinning approach which was successfully used for the Resolved Resonance Region (RRR) [4,5]. It transforms the pointwise resonance cross-sections into the pointwise monotone ones within some energy intervals with the use of cross-section probability distribution functions P(σ). A similar approach was used to calculate pointwise cross-sections in the URR [6]. (authors)

42 ENGINEERING↗
DOE OSTI2021

Strong and Efficient Consistency with Consistency-aware Durability

We introduce consistency-aware durability or C ad , a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads , a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems; such a property can be particularly beneficial in geo-distributed and edge-computing scenarios. We build O rca , a modified version of ZooKeeper that implements C ad and cross-client monotonic reads. We experimentally show that O rca provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, O rca provides significantly higher throughput (1.8--3.3×) and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. We also implement C ad in Redis and show that the performance benefits are similar to that of C ad ’s implementation in ZooKeeper.

Computer Science↗
DOE OSTI2021

Quantum networks based on color centers in diamond

With the ability to transfer and process quantum information, large-scale quantum networks will enable a suite of fundamentally new applications, from quantum communications to distributed sensing, metrology, and computing. This Perspective reviews requirements for quantum network nodes and color centers in diamond as suitable node candidates. We give a brief overview of state-of-the-art quantum network experiments employing color centers in diamond and discuss future research directions, focusing, in particular, on the control and coherence of qubits that distribute and store entangled states, and on efficient spin–photon interfaces. We discuss a route toward large-scale integrated devices combining color centers in diamond with other photonic materials and give an outlook toward realistic future quantum network protocol implementations and applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Characterization of anisotropic mechanical properties of polymer composites from a hybrid additive manufacturing-compression molding process using x-ray computer tomography

In this study, anisotropic stiffness tensors were reconstructed based on fiber orientation distributions obtained from X-ray computer tomography (xCT). A preform was manufactured via a big area additive manufacturing (BAAM) system with carbon fiber (CF) filled acrylonitrile butadiene styrene (ABS). The tailored preform from additive manufacturing (AM) was used in the compression molding (CM) process to produce a low-void high-performance thermoplastic composite panel. An xCT technique was employed to detect the fiber orientations in CF/ABS composites manufactured via three different methods: AM from BAAM, extrusion compression molding (ECM), and AM-CM. The anisotropic stiffness tensor was obtained from the composite panel manufactured via the three manufacturing methods (AM, ECM, and AMCM). A micromechanics theory was used to obtain the orthotropic stiffness tensors of the composite panels and compared with the experimental values. The predicted stiffness tensors of AM and AM-CM composite panels were used to study the deformation characteristics of a steering wheel during airbag deployment by performing finite element analysis (FEA). The approach developed in this study can be utilized for evaluating high-performance composites.

Pokkalla, Deepak↗
DOE OSTI2024

Distribution Integration Solution Cost Options (DISCO) [Slides]

DISCO is a Python-based NREL software tool for conducting scalable, repeatable distribution analyses. Although DISCO was originally developed to support photovoltaic impact analyses, it can also be used to understand the impact of other distributed energy resources and load changes on distribution systems. This presentation took place on April 9, 2024, and included NREL power grid researchers Sherin Ann Abraham and Shibani Ghosh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Reconstruction of Electron and Ion Distribution Functions in a Magnetotail Reconnection Diffusion Region

In the diffusion region of magnetotail reconnection, particle distributions are highly structured, exhibiting triangular shapes and multiple striations that deviate dramatically from the Maxwellian distribution. Fully kinetic simulations have been demonstrated to be capable of producing the essential structures of the observed distribution functions, yet are computationally not feasible for 3D global simulations. The fluid models used for large-scale simulations, on the other hand, do not have the kinetic physics necessary for describing reconnection accurately. Our study aims to bridge fully kinetic and fluid simulations by quantifying the information required to capture the non-Maxwellian features in the distributions underlying the closures used in the fluid code. Here, we compare the results of fully kinetic simulations with observed electron velocity distributions in a magnetotail reconnection diffusion region and use the maximum entropy model to reconstruct electron and ion distributions using various numbers of moments obtained from the simulation. Our results indicate that using only local moments, the maximum entropy model can reproduce many of the features of the distributions: (1) the electron outflow distribution with a tilted triangular structure is reproduced with 21 or more moments in agreement with Ng et al. and (2) counterstreaming distributions can be captured with the 35-moment model when the separation in velocity space between the populations is large.

58 GEOSCIENCES↗
DOE OSTI2021

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Solving nonlinear optimal power flow (OPF) problem is computationally expensive, and poses scalability challenges for power distribution networks. An alternative to solving the original nonlinear OPF is the linear approximated OPF models. Although, these linear approximated OPF models are fast, the resulting solutions may result in significant optimality gap. Lately, the application of machine learning (ML) methods in successfully solving the nonlinear OPF has been reported. These methods learn and estimate the nonlinear control policies using a purely data-driven approach. In this paper, we propose an approach to complements the ML based approach to solving OPF using solutions from known linearized OPF model. Specifically, we use supervised learning to map the solutions of linear OPF to nonlinear control variables. Unlike, the traditional ML based methods for OPF that approximate the full distribution feeder model using function approximation, our approach uses a two-node approximation of radial networks. The proposed approach is validated using IEEE 123 bus test system for OPF solutions obtained using the nonlinear OPF models.

optimal power flow, power distribution systems, su↗
DOE OSTI2022

ANTARES: a new IRSN computational chain for safety analysis

Since some years, the IRSN has developed a new project to simulate accurately the interaction between neutronic and thermal hydraulic phenomena. This project called ANTARES (Advanced Neutronics and Thermal-hydraulic for the Analysis of Reactor Safety) couples the thermal hydraulic code CATHARE-3 with the 3D neutronic nodal code PARCS. The purpose of this project is to take the advantage of the complementary of these 2 codes, by the knowledge of the 3D core power distribution in CATHARE-3 (computed by PARCS) and improving the thermal hydraulic boundaries conditions in PARCS. In this way, the IRSN diversifies and enlarges its available tools to perform safety analysis with improved accuracy. The current technique usually adopted in France for the safety demonstrations for Reactivity Insertion Accident (RIA) is the so-called 'conservative' approach, which consists of reducing all the feedback (Doppler and moderator effects) and in modifying some physical quantities in such a way to increase a power peak. For this reason, these facilities ('penalties') have been implemented in ANTARES. In this paper we will give two examples of accidental transients that can be simulated with ANTARES: a Main Steam Line Break (MSLB) and an inadvertent boron dilution event. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

A probabilistic model for the identification of confinement regimes and edge localized mode behavior, with implications to scaling laws

Pattern recognition is becoming an important tool in fusion data analysis. However, fusion diagnostic measurements are often affected by considerable statistical uncertainties, rendering the extraction of useful patterns a significant challenge. Therefore, we assume a probabilistic model for the data and perform pattern recognition in the space of probability distributions. We show the considerable advantage of our method for identifying confinement regimes and edge localized mode behavior, and we discuss the potential for scaling laws.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

The Dosimetric Effect of Zipper Artifacts on TomoTherapy Adaptive Dose Calculation-A Phantom Study

Tomotherapy adaptive dose calculation offers the ability to verify and adjust the therapeutic plan during the treatment. Using tomotherapy adaptive dose calculation, the planned fluence pattern can be used to recalculate the dose distribution on pretreatment megavoltage computed tomography (MVCT) images. Zipper artifacts, which appear as increased density in the central region of MVCT images, may affect the accuracy of adaptive dose recalculation. The purpose of this study was to evaluate the dosimetric effects of zipper artifacts on tomotherapy adaptive dose calculation. MVCT images of a cylindrical water phantom of 22-cm diameter were acquired on a tomotherapy system. The zipper artifacts were enclosed by a cylindrical planning target volume (PTV) contoured on these images. For comparison, artifact-free images were created by replacing the computed tomography (CT) numbers of zipper artifacts with the mean CT number of water. Treatment plans were generated by giving a uniform dose of 2 Gy to the PTV based on these modified images; it was then applied to the images that have the zipper artifacts. The impacts of different pitch ratios on the artifacts were assessed. The dose distribution differences between the 2 sets of images were compared. The absorbed dose that covered 95% volume of PTV and maximum dose, minimum dose, and mean dose of the PTV were also calculated and compared. The water phantom was scanned on the tomotherapy system twice per week for 12 consecutive weeks. The mean CT number of zipper artifacts (101 HU) was three times higher than that of water (34 HU). The CT number value and location of zipper artifacts were not affected by the pitch ratio. Gamma analysis was performed between the original and recalculated dose distributions. The discrepancies between the isodose distributions calculated by two sets of images were within 1%/1-mm tolerance. The dosimetric impact from zipper artifacts was found insignificant such that the recalculated dose was underestimated by less than 0.5%.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

A Framework for Neural Network Inference on FPGA-Centric SmartNICs

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication, thereby mitigating the von Neumann bottleneck. Existing work, however, has been generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs, but most control tasks are left to the CPUs. This leads to frequent waiting, inferior performance, and scaling challenges. In this work, we propose a new distributive data-centric computing framework, named FCsN, for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture, FCsN allows the complete detaching of kernel execution, control logic, system scheduling, and network communication to the SmarNICs. This boosts performance by: (i) avoiding the control dependency with CPUs and (ii) supporting streaming kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network applications including graph neural networks; as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with the support of FCsN can achieve, on average, 144 speedups over the MPI-based standard CPU baselines.

Guo, Anqi↗
DOE OSTI2025

CFD modeling of natural circulation in LiCl-KCl molten salt closed loop

Characterizing flow within a molten salt closed-loop system is crucial for assessing system requirements, evaluating performance, and identifying potential flaws. Direct flow measurement using instrumentation is challenging due to extreme environmental conditions and the limitations associated with measuring molten salt flow under natural convection. Here, this study aims to provide comprehensive insights into the thermal-hydraulic behavior of a closed loop, with a particular focus on temperature distribution and velocity prediction. The Computational Fluid Dynamics (CFD) model demonstrated the capability to effectively simulate and predict both temperature distributions and flow velocities within the molten salt loop. The CFD model's predictive capability was validated by its ability to replicate temperature measurements under varying boundary conditions. The analysis revealed that the CFD model tends to underpredict temperatures in the cold leg and overpredict them in the hot leg, highlighting the need for continuous model refinement and acknowledging the limitations of using a steady-state approach. Furthermore, the potential of using external temperature measurements to estimate internal molten salt temperatures and predict flow velocity was explored, revealing that this approach could introduce up to a 5.5% error in flow velocity calculations. Line probes mapping temperature distributions across the tube's cross-section and molten salt provided valuable insights into temperature gradients, emphasizing the need for a thermal conductivity equation for molten salt with lower uncertainty to achieve more accurate temperature predictions of the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗