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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TU-H-CAMPUS-JeP3-05: Adaptive Determination of Needle Sequence HDR Prostate Brachytherapy with Divergent Needle-By-Needle Delivery

Purpose: To develop a new method which adaptively determines the optimal needle insertion sequence for HDR prostate brachytherapy involving divergent needle-by-needle dose delivery by e.g. a robotic device. A needle insertion sequence is calculated at the beginning of the intervention and updated after each needle insertion with feedback on needle positioning errors. Methods: Needle positioning errors and anatomy changes may occur during HDR brachytherapy which can lead to errors in the delivered dose. A novel strategy was developed to calculate and update the needle sequence and the dose plan after each needle insertion with feedback on needle positioning errors. The dose plan optimization was performed by numerical simulations. The proposed needle sequence determination optimizes the final dose distribution based on the dose coverage impact of each needle. This impact is predicted stochastically by needle insertion simulations. HDR procedures were simulated with varying number of needle insertions (4 to 12) using 11 patient MR data-sets with PTV, prostate, urethra, bladder and rectum delineated. Needle positioning errors were modeled by random normally distributed angulation errors (standard deviation of 3 mm at the needle’s tip). The final dose parameters were compared in the situations where the needle with the largest vs. the smallest dose coverage impact was selected at each insertion. Results: Over all scenarios, the percentage of clinically acceptable final dose distribution improved when the needle selected had the largest dose coverage impact (91%) compared to the smallest (88%). The differences were larger for few (4 to 6) needle insertions (maximum difference scenario: 79% vs. 60%). The computation time of the needle sequence optimization was below 60s. Conclusion: A new adaptive needle sequence determination for HDR prostate brachytherapy was developed. Coupled to adaptive planning, the selection of the needle with the largest dose coverage impact increases chances of reaching the clinical constraints. M. Borot de Battisti is funded by Philips Medical Systems Nederland B.V.; M. Moerland is principal investigator on a contract funded by Philips Medical Systems Nederland B.V.; G. Hautvast and D. Binnekamp are fulltime employees of Philips Medical Systems Nederland B.V.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Elephants Sharing the Highway: Studying TCP Fairness in Large Transfers over High Throughput Links

Escalating bandwidth demand strains high-performance data networks, posing potential performance risks. TCP congestion control algorithms enhance reliability and optimize bandwidth usage. Network performance is influenced by factors such as AQM algorithms and router buffer size. In the context of constrained network resources, understanding how TCP flows share networks and the resulting performance impact is essential. This paper introduces insights into TCP fairness and performance involving a comparison of TCP CUBIC, Reno, Hamilton, and BBR versions 1 and 2 across real-world networks supporting high bandwidths of up to 25 Gbps. The research explores TCP behaviors with AQM algorithms like FIFO, FQ_CODEL, and RED, alongside diverse buffer sizes. Notably, findings reveal that manipulating buffers and queuing methods yields contrasting outcomes based on bandwidth. BBRv2 emerges as a superior fair algorithm, pivotal for swift transfers, particularly in scientific data scenarios. These results provide crucial guidance for future network design, ensuring equitable performance optimization.

Kiran, Mariam↗
DOE OSTI2019

Coramin v. 0.1 Beta

Coramin is a package that provides tools for developing tailored algorithms for mixed-integer nonlinear programming problems. This software includes the ability to: - Generate convex or piecewise relaxations of nonlinear optimization problems, addressing bilinear, quadratic, and convex/concave univariate functions - Perform optimization-based bounds tightening on all or subsets of the problem variables - Generate polyhedral envelopes for multi-linear functions

Bynum, Michael↗
DOE OSTI2017

Market-Based and System-Wide Fuel Cycle Optimization

This work introduces automated optimization into fuel cycle simulations in the Cyclus platform. This includes system-level optimizations, seeking a deployment plan that optimizes the performance over the entire transition, and market-level optimization, seeking an optimal set of material trades at each time step. These concepts were introduced in a way that preserves the flexibility of the Cyclus fuel cycle framework, one of its most important design principles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Scale-Up of Electrode Coating and Flow-Field for Commercial Hydrogen Peroxide Electrolyzer: Cooperative Research and Development Final Report, CRADA Number CRD-17-00687

Hydrogen peroxide is currently produced at central chemical plants via the anthraquinone oxidation process. This process produces environmental pollutants that are costly to remediate, requires hazardous long distance shipping of highly concentrated peroxide (50% or 70%), and necessitates extra handling costs related to storage and dilution. Peroxygen Systems, Inc. (PSi) is developing breakthrough technology for on-site hydrogen peroxide production. PSi’s on-site on-demand electrolyzer can reduce the cost of producing hydrogen peroxide by 50%, while also completely eliminating the cost and safety issues associated with shipping and handling of high concentration hydrogen peroxide. The challenge for PSi is scaling. To support the next step toward commercialization (customer pilot tests), scaling the prototype into larger single cells and 20-40 cell stacks is required. In addition to internal hardware and flow-field design efforts at PSi, NREL will address three critical problems for this scale-up effort: (1) demonstrating a large scale roll-to-roll (R2R) process to coat uniform electrode materials for 100 cm2 and 500 cm2 stack testing, (2) demonstrating an in-line diagnostic to achieve better electrode quality control, and (3) performing in situ cell/stack testing to better understand and optimize the performance of the flow field design.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2014

Cross-Layer Self-Adaptive/Self-Aware System Software for Exascale Systems

The extreme level of parallelism coupled with the limited available power budget expected in the exascale era brings unprecedented challenges that demand optimization of performance, power and resiliency in unison. Scalability on such systems is of paramount importance, while power and reliability issues may change the execution environment in which a parallel application runs. To solve these challenges exascale systems will require an introspective system software that combines system and application observations across all system stack layers with online feedback and adaptation mechanisms. In this paper we propose the design of a novel self-aware, self-adaptive system software in which a kernel-level Monitor, which continuously inspects the evolution of the target system through observation of Sensors, is combined with a user-level Controller, which reacts to changes in the execution environment, explores opportunities to increase performance, save power and adapts applications to new execution scenarios. We show that the monitoring system accurately monitors the evolution of parallel applications with a runtime overhead below 1-2%. As a test case, we design and implement a user-runtime system that aims at optimizing application’s performance and system power consumption on complex hierarchical architectures. Our results show that our adaptive system reaches 98% of performance efficiency of manually-tuned applications.

High Performance computing, Self-aware/self-adapti↗
DOE OSTI2018

Cyber Network Design for Secondary Frequency Regulation: A Spectral Approach

We present a preliminary theoretical framework based on spectral graph theory that captures how the cyber topology of a distributed secondary frequency control scheme impacts the stability, optimality, and transient performance of our power system as a cyber-physical network. We show that a collection of polynomials defined in terms of the cyber and physical Laplacian eigenvalues encode information on the interplay between cyber and physical networks. It is demonstrated that to understand the impact of adding cyber connectivity, one should separate the low-damping and high-damping regimes. Although adding cyber connectivity always improves the performance for high-damping systems, it is not the case for low-damping scenarios. Based on the theoretical study, we discuss how a good cyber network should be designed. Our empirical study shows that for practical systems, the number of communication channels that is needed to achieve near-optimal performance is usually less than twice the number of buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Field Programmable Gate Arrays for Enhancing the Speed and Energy Efficiency of Quantum Dynamics Simulations

We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures for carrying out fast and energy-efficient quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware, which is the typical case for writing quantum chemistry code for central processing units (CPUs) and graphics processing units (GPUs), FPGAs allow us to directly customize the underlying hardware (even at the level of specific electrical signals in the circuit) to give a truly optimized computational performance for quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (many of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Average X-Ray Energy from Optimized Tantalum / Carbon Bremsstrahlung Converters

A set of coupled electron/photon radiation transport calculations were performed on optimized Ta / C converters due to questions about previous work at Sandia National Laboratories by Halbleib and Sanford. Generally, the results of the previous calculations were confirmed. However, new relationships between the incident electron beam energy and the average energy of a bremsstrahlung x-ray spectrum for the converters have been defined for the incident electron energy range of 50 keV to 15 MeV as well as for the narrower range of 50 keV to 1 MeV. The relationships were developed by bracketing the results of radiation transport calculations rather than by a rigorous mathematical fit to the data. Additional data such as the total x-ray or the energy spectra of the x-ray fluence exiting the Ta / C converters are available upon request.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Python in the NERSC Exascale Science Applications Program for Data

We describe a new effort at the National Energy Re- search Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) many- core architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental/observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codes vary in terms of “Python purity” from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming lan- guages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices

Ronaghi, Zahra↗
DOE OSTI2018

A systematic study of minima in alanine dipeptide

The alanine dipeptide is a standard system to model dihedral angles in proteins. It is shown that obtaining the Ramachandran plot accurately is a hard problem because of many local minima; depending on the details of geometry optimizations, different Ramachandran plots can be obtained. To locate all energy minima, starting from geometries from MD simulations, 250,000 geometry optimizations were performed at the level of RHF/6-31G*, followed by re-optimizations of the located 827 minima at the level of MP2/6-311++G**, yielding 30 unique minima, most of which were not previously reported in literature. Both in vacuo and solvated structures are discussed. The minima are systematically categorized based on four backbone dihedral angles. The Gibbs energies are evaluated and the structural factors determining the relative stabilities of conformers are discussed. (c) 2018 Wiley Periodicals, Inc.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Computing single step operators of logic programming in radial basis function neural networks

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Two Dimensional Topology Optimization of Heat Exchangers with the Density and Level-Set Methods

We design heat exchangers using two topology optimization approaches: the density, i.e. volume fraction and level set methods. Our goal is to maximize the heat exchange between two fluids in separate channels while constraining the pressure drop across each channel. The heat exchanger is modeled with a coupled thermal-flow formulation. The flow is governed by an isothermal and incompressible Stokes-Brinkman equation and the heat transfer is governed by a convection-diffusion equation with high Peclet number. We solve one set of Stokes-Brinkman equations per fluid. Each Brinkman term in the flow equation serves to model the other phase as a solid, thereby preventing mixing. We first represent the solid and fluid phases using a volume fraction variable and apply a SIMP-like penalization in the Brinkman term to drive the optimization to a discrete design. The cost and constraint function derivatives are automatically calculated with the library pyadjoint and the optimization is performed by the Method of Moving Asymptotes. In a second optimization formulation, we use the level set approach to define the interface that separates the two fluids. Pyadjoint calculates the shape derivatives of the cost and constraint functions and the Hamilton-Jacobi advects the interface, allowing for topological changes. We present results in two dimensions and discuss the advantages and disadvantages of each approach.

42 ENGINEERING↗
DOE OSTI2024

A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Analysis of Cygnus Electrical Signals

The United States initiated the Subcritical Experiment (SCE) program to support a stockpile stewardship mission shortly after the 1992 moratorium on underground nuclear testing was established. Many SCE’s are conducted at the Nevada National Security Site (NNSS) in Nevada. Cygnus is a high-energy radiation generating device (RGD) located and operated at the NNSS and is a primary diagnostic for the SCE program. The Cygnus Dual Beam Radiographic Facility consists of two identical radiographic sources, Cygnus 1 and Cygnus 2. From creation of the high power V-I drive to energy transport and X-ray conversion at the rod-pinch diode, the Cygnus machines utilize the following components: oil-filled Marx generator, water-filled pulse-forming line (PFL), water-filled coaxial transmission line (CTL), three-cell vacuum induction voltage adder (IVA), and rod-pinch diode. The diode pulse has the following electrical specifications: 2.25 MV, 60 kA, 60 ns. Each source has the following X-ray specifications: 1 mm diameter, 4 rad at 1 m, and 50 ns radiation pulse. SCEs are both single-event and high-value; therefore, a high level of performance in reliability and reproducibility are key issues of Cygnus. Prior to executing such a SCE, there are a formidable number of shots (e.g., each of the two Cygnus RGDs charging and discharging properly into the rod-pinch diode load) that must be executed to determine reliability and reproducibility of the Cygnus RGDs. For every shot on Cygnus, voltages and currents along the machine are recorded and analyzed. In this paper we summarize attributes of the voltage and current waveforms at different locations using distribution plots. These distribution plots are used to quantify the reliability and reproducibility for Cygnus. This analysis methodology will provide a standard to trouble shoot results of individual Cygnus shots, and also to monitor long term Cygnus performance. Accordingly, this type of extensive analysis will contribute to optimized Cygnus performance on SCEs.

42 ENGINEERING↗
DOE OSTI2017

Study on Neutronics Calculation Method of SCWR with Annular Fuel Assembly

Supercritical Water-Cooled Reactor (SCWR) is one of Gen-IV reactor systems for its advantages over LWRs, such as high thermal efficiency, simplified reactor system and economics. Annular fuel was proposed by MIT to supplant conventional solid fuel and improve power density in LWRs. Annular fuel is cooled from both inside and outside of the fuel. Thus a lower fuel temperature and cladding surface temperature can be achieved with higher power density. SCWRs will benefit from this advantage because high cladding temperature (usually over 650 degree C) is one challenge of SCWRs. Using annular fuel in SCWRs is very promising. This is also because the inner hole of the annual fuel can provide extra neutron moderation. Supercritical water density is too low to provide enough moderation. Different analysis codes have been developed all over the world to carry out SCWR pre-conceptual core design. Yamaji et al. developed SRAC/ASMBURN/COREBN to study Japan version of SCWR. Monti et al. used ERANOS to analyze HPLWR (High Performance Light Water Reactor). WIMS-D4/CITATION was also used to calculate HPLWR by Tashakor et al.. These codes employ a two-step method used in PWRs calculation. Lattice/Assembly calculation is carried out as the first step. In the second step, few group constant of different fuel assembly types is then used in core calculation. MCNP had also been used to study SCWR by Watta et al.. Due to large computation time cost, it's used mostly to analyze fuel assembly or core performance rather than pre-conceptual design, in which much optimization is needed. In this research, a neutronics calculation code for SCWR is developed using two-step method. This code is verified by comparing with Monte-Carlo code. Fuel assembly homogenization method is studied to improve the code accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗
DOE OSTI2022

ML4Geo: Machine Learning based Well Design to Enhance Unconventional Energy Production

This project aimed at using artificial intelligence and machine learning methods to improve oil and gas operations. On the machine learning front, this focused on the development of differentiable programming methods that are relevant in this application area, such as incorporating physical simulations into machine learning workflows. On the oil and gas front, the focus was on optimizing well performance.

02 PETROLEUM↗