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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2019

Pronghorn

Pronghorn is designed as a multi-dimensional coarse-mesh conjugate heat transfer simulator (tightly coupled fluid and solid phase heat transfer) for nuclear reactor core applications. Pronghorn’s design philosophy of is that: 1. The tool will be agnostic of reactor concept, including VHTRs, SFRs, FHRs, and LWRs. 2. Must be higher fidelity than traditional nuclear systems/safety analysis (0D-1D). 3. Avoid lower-length scale phenomena, such as boundary layer theory, i.e., rely on closure relations. 4. Apply mixture theory (homogenization) wherever possible. 5. Conserve mass and energy (solve all physics on single mesh). 6. Must be easily coupled to both MOOSE-based and external physics applications. 7. Does not require a super computer. Thus, Pronghorn’s algorithmic philosophy is to resolve large scale flow features and model lower-length scale phenomena, such as boundary layers. The coarse mesh approach for lower-length scale modeling is to develop a multi-component multi-phase homogenization of solid and liquid phases into a mixture formulation inside a finite element. For example, a pebble bed core is represented as a two-phase mixture of coolant and fuel pebbles, where the fuel phase is stationary. Thus, the solid phase exerts a drag force on the fluid phase, composed of form and viscous drag, and both phases exchange internal energy through conduction and convective heat transfer. For Pronghorn, the volume fraction of fluid can vary in space, but not in time. Pronghorn has been validated against the VHTR PBMR400 benchmark problems, used for rod ejection simulations have been conducted for thermal-fluids/neutronics for both pebble-bed and prismatic gas-cooled reactors and simple thermal-neutronics coupled LWR benchmark problems. Pronghorn flow models cover a wide range of transient and steady flow conditions, including, porous fluid flow, compressible flow, and nearly incompressible, thermally expansive flow. The physics can be solved in three-dimensional Cartesian coordinates and two-dimensional cylindrical (r-z) coordinates.

Martineau, RichardC.↗
DOE OSTI2023

Graph-based machine learning improves just-in-time defect prediction

The increasing complexity of today’s software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required to build software. By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. We hypothesize that features extracted from the contribution graphs may be better predictors of defect-prone changes than intrinsic features derived from software characteristics. We corroborate our hypothesis using graph-based ML for classifying edges that represent defect-prone changes. This new framing of the JIT defect prediction problem leads to remarkably better results. We test our approach on 14 open-source projects and show that our best model can predict whether or not a code change will lead to a defect with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%. This represents a 152% higher F1 score and a 3% higher MCC over the state-of-the-art JIT defect prediction. We describe limitations, open challenges, and how this method can be used for operational JIT defect prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Preventing Run-Time Bugs at Compile-Time Using Advanced C++

When writing software, we develop algorithms that tell the computer what to do at run-time. Our solutions are easier to understand and debug when they are properly modeled using class hierarchies, enumerations, and a well-factored API. Unfortunately, even with these design tools, we end up having to debug our programs at run-time. Worse still, debugging an embedded system changes its dynamics, making it tough to find and fix concurrency issues. This paper describes techniques using C++ to detect run-time bugs *at compile time*. A concurrency library, developed at Fermilab, is used for examples in illustrating these techniques.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Optimizing Mu2e Spill Regulation System Algorithms

A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Grid-tied Multilevel Inverter with Phase-locked Loop Algorithm

A multilevel inverter is an electronic device capable of changing direct current energy to alternant current energy with a voltage and frequency established by the user. They are ideal for connecting renewable energy sources to the AC grid, energy plants, and smart grids. The voltage must be balanced and synchronized with the electrical network for adequate performance. This paper shows the voltage synchronization between an inverter output voltage and the AC grid using a phase-locked loop based on an adaptive observer. The proposed algorithm can perform under grid uncertainties such as noise and generates a reference signal for the modulation used in the inverter. The algorithm is robust and computationally efficient and can be implemented through basic elements such as operational amplifiers, resistors, and capacitors, reducing its difficulty in executing it in a system.

42 ENGINEERING↗
DOE OSTI2018

Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design

We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Economical and Engineering Aspects of Proactive Demand Participation: Centralized versus Bilateral Control Structure

The electric grid of the U.S. is evolving from a system characterized by large and centralized fossil-fueled power plants and passive customers to a network with high penetration of distributed energy resources and proactive customers. This project facilitated the evolution of the U.S. electric power grid by enabling proactive participation of demands in electricity market at the power distribution system level. This project has three major achievements. First, we designed a centralized distribution system operator managed electricity market and developed a three-phase alternating current optimal power flow algorithm to coordinate the operation of large-scale flexible loads and distributed energy resources. Second, we designed a decentralized bilateral coordinate framework for retail electricity market, where each participating customer is capable of making decisions regarding energy production and consumption based on information received from transactive neighbors. Third, the impacts of centralized and decentralized demand-side participation scheme on power system operations were evaluated with both large-scale simulations and hardware-in-the-loop demonstrations. The major activities and specific accomplishments of the technical tasks are summarized in Section II. The detailed design of centralized/hierarchical and decentralized/bilateral retail electricity markets and the comparison between the two markets are described in Section III. The accomplishments and conclusions of the project are presented in Section IV. The major findings of the project falls into three areas, hierarchical distribution system operator managed electricity market, bilateral electricity market and decentralized market coordination, and comparison of centralized and decentralized market coordination mechanisms. In the area of hierarchical distribution system operator managed electricity market design, an integrated wholesale and retail electricity market was shown to be highly effective in extracting the operational flexibility from distributed energy resources to provide services to the wholesale market. A chordal conversion based convex iteration algorithm is not only computationally efficiency but also achieves global optimal solution in coordinating the operations of a large number of heterogeneous distributed energy resources.In the area of decentralized market coordination approach, we showed that bilateral transactions among proactive customers and decentralized transaction clearing mechanism based on simultaneous second-price sealed-bid auction can encourage truthful bidding strategies from the market participations. In addition, the individual customers can rely on a portfolio optimization approach to maximize their returns while minimizing risks. In the area of comparison of centralized and decentralized market coordination mechanisms, we found that the centralized market design results in higher social welfare and lower wholesale market clearing price for electricity. In general, centralized market design favors distributed energy resources and fixed loads. The decentralized market design mostly benefits flexible loads and conventional generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Human-Constrained Indicators of Gatekeeping Behavior as a Role in Information Suppression: Finding Invisible Information and the Significant Unsaid

To date, disinformation research has focused largely on the production of false information ignoring the suppression of select information. We term this alternative form of disinformation information suppression. Information suppression occurs when facts are withheld with the intent to mislead. In order to detect information suppression, we focus on understanding the actors who withhold information. In this research, we use knowledge of human behavior to find signatures of different gatekeeping behaviors found in text. Specifically, we build a model to classify the different types of edits on Wikipedia using the added text alone and compare a human-informed feature engineering approach to a featureless algorithm. Being able to computationally distinguish gatekeeping behaviors is a first step towards identifying when information suppression is occurring.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

An Assessment of Machine Learning Applied to Ultrasonic Nondestructive Evaluation

In the United States, the nuclear industry performs inservice inspection (ISI) through nondestructive examination (NDE) methods in accordance with guidelines specified in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section XI, Rules for Inservice Inspection of Nuclear Power Plant Components. Ultrasonic nondestructive testing and evaluation (NDT&E) is one of the more commonly used techniques for inspecting Class 1 structural components in nuclear power systems. As the number of qualified NDE inspectors declines, the nuclear industry is looking to take advantage of advances in automation to enhance inspection capabilities. Advances in computational power, cloud-based computing, and machine learning algorithms make automated data analysis possible. Machine learning (ML) has shown huge potential in automated data analyses for ultrasonic NDE in the context of weld inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The battle of clean and dirty qubits in the era of partial error correction

When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work, we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the “clean and dirty” setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in the concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling Workloads of a Linear Electromagnetic Code for Load Balancing Matrix Assembly

This report presents our work to model the workloads of a linear electromagnetic application based on the method of moments in the frequency domain to effectively load balance the matrix assembly. This application is particularly challenging to load balance due to its lack of persistent iterative behavior, its operation under tight memory constraint (where the matrix may fill 80% of memory on each node), and the algorithmic complexity of the computational method. This report describes the first step in our work to apply an inspector-executor approach for load balancing workloads where key parameters are exposed during the inspector phase and a pre-trained model is applied to predict relative task weights for the load balancer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Simulations of Radiation Hydrodynamics in Strongly Coupled Plasmas (Final Report)

Radiation propagates throughout the Universe and is often the only information we have from distant astronomical objects. Additionally, radiation plays a key role in systems ranging from the interior of stars to the establishment of shocks at the edges of forming galaxies to neutron star mergers. Here radiative processes, absorption, emission, and scattering, are fundamental to transport of momentum and energy, as well as, the overall evolution of these and other astrophysical systems. In some cases, radiation-dominant systems are also sufficiently dense and ionized, such that, the ions are strongly coupled (ie electrostatic energy >> thermal energy or Γ >> 1), which will affect radiative processes and the transport of radiation. During this project, we developed a computational ray-tracing algorithm to study radiation transport through a simulated neutron star capsule implosion under conditions relevant to Omega-60 laser experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The battle of clean and dirty qubits in the era of partial error correction

When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the "clean and dirty" setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Localized Exponential Time Differencing Method for Shallow Water Equations: Algorithms and Numerical Study

Here, we explore the performance of the exponential time differencing (ETD) method applied to the rotating shallow water equations. Comparing with explicit time stepping of the same order accuracy in time, the ETD algorithms could reduce the computational time in many cases by allowing the use of large time step sizes while still maintaining numerical stability. To accelerate the ETD simulations, we propose a localized approach that synthesizes the ETD method and overlapping domain decomposition. By dividing the original problem into many subdomain problems of smaller sizes and solving them locally, the proposed approach could speed up the calculation of matrix exponential vector products. Several standard test cases for shallow water equations of one or multiple layers are considered. The results show great potential of the localized ETD method for high-performance computing because each subdomain problem can be naturally solved in parallel at every time step.

58 GEOSCIENCES↗
DOE OSTI2022

Multifidelity multiobjective optimization for wake-steering strategies

Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

17 WIND ENERGY↗
DOE OSTI2017

Computational and compressive imaging via additively manufactured random optical element

Here, the ubiquity of computer processing power has enabled new imaging architectures that significantly depart from traditional imaging devices. Imaging systems with deeply enmeshed algorithmic elements are known as computational imaging systems, and can take unconventional physical forms compared to photographic systems. We report on the development and evaluation of a lensless imaging system utilizing an additively manufactured transparent optical element capable of producing image reconstructions after a calibration process. A generalized metric for image quality is presented and used to evaluate system performance as a function of total samples used to calibrate and test.

97 MATHEMATICS AND COMPUTING↗