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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-J-252: A Motion Algorithm to Extract Physical and Motion Parameters of a Mobile Target in Cone-Beam Computed Tomographic Imaging Retrospective to Image Reconstruction

Purpose: A motion algorithm was developed to extract actual length, CT-numbers and motion amplitude of a mobile target imaged with cone-beam-CT (CBCT) retrospective to image-reconstruction. Methods: The motion model considered a mobile target moving with a sinusoidal motion and employed three measurable parameters: apparent length, CT number level and gradient of a mobile target obtained from CBCT images to extract information about the actual length and CT number value of the stationary target and motion amplitude. The algorithm was verified experimentally with a mobile phantom setup that has three targets with different sizes manufactured from homogenous tissue-equivalent gel material embedded into a thorax phantom. The phantom moved sinusoidal in one-direction using eight amplitudes (0–20mm) and a frequency of 15-cycles-per-minute. The model required imaging parameters such as slice thickness, imaging time. Results: This motion algorithm extracted three unknown parameters: length of the target, CT-number-level, motion amplitude for a mobile target retrospective to CBCT image reconstruction. The algorithm relates three unknown parameters to measurable apparent length, CT-number-level and gradient for well-defined mobile targets obtained from CBCT images. The motion model agreed with measured apparent lengths which were dependent on actual length of the target and motion amplitude. The cumulative CT-number for a mobile target was dependent on CT-number-level of the stationary target and motion amplitude. The gradient of the CT-distribution of mobile target is dependent on the stationary CT-number-level, actual target length along the direction of motion, and motion amplitude. Motion frequency and phase did not affect the elongation and CT-number distributions of mobile targets when imaging time included several motion cycles. Conclusion: The motion algorithm developed in this study has potential applications in diagnostic CT imaging and radiotherapy to extract actual length, size and CT-numbers distorted by motion in CBCT imaging. The model provides further information about motion of the target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Visual investigation on the heat dissipation process of a heat sink by using digital holographic interferometry

We present a method for visually and quantitatively investigating the heat dissipation process of plate-fin heat sinks by using digital holographic interferometry. A series of phase change maps reflecting the temperature distribution and variation trend of the air field surrounding heat sink during the heat dissipation process are numerically reconstructed based on double-exposure holographic interferometry. According to the phase unwrapping algorithm and the derived relationship between temperature and phase change of the detection beam, the full-field temperature distributions are quantitatively obtained with a reasonably high measurement accuracy. And then the impact of heat sink's channel width on the heat dissipation performance in the case of natural convection is analyzed. In addition, a comparison between simulation and experiment results is given to verify the reliability of this method. The experiment results certify the feasibility and validity of the presented method in full-field, dynamical, and quantitative measurement of the air field temperature distribution, which provides a basis for analyzing the heat dissipation performance of plate-fin heat sinks.

42 ENGINEERING↗
DOE OSTI2026

GRIDAPPSD/distopf (33583-E)

DistOPF is an open-source Python package providing a three-phase, asymmetric optimal power flow (OPF) tool specifically designed for distribution systems. The key inventive features include: - Asymmetrical 3-phase OPF modeling for distribution systems with unbalanced phases - Comprehensive control optimization supporting both active (P) and reactive (Q) power control variables - Built-in visualization and validation tools - Standard test system benchmarking platform for algorithm development and comparison - Modular CSV-based input system using Pandas DataFrames for flexible model specification - Standard power distribution model importer enabling direct conversion from CIM and OpenDSS format to optimization-ready models - Multiple solve interface compatibility (PYOMO, CVXPY, SciPy) with automatic solver selection based on problem type

Gray, Nathan [Pacific Northwest National Laborator↗
DOE OSTI2021

An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The report highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

TU-C-17A-05: Dose Domain Optimization of MLC Leaf Patterns for Highly Complicated 4Ï€ IMRT Plans

Purpose: Highly conformal non-coplanar 4π radiotherapy plans typically require more than 20 intensity-modulated fields to deliver. A novel method to calculate multileaf collimator (MLC) leaf patterns is introduced to maximize delivery efficiency, accuracy and plan quality. Methods: 4 GBM patients, with a prescription dose of 59.4 Gy or 60 Gy, were evaluated using the 4π algorithm using 20 beams. The MLC calculation utilized a least square minimization of the dose distribution, with an anisotropic total variation regularization term to encourage piecewise continuity in the fluence maps. Transforming the fluence to the dose domain required multiplying the fluence with a sparse matrix. Exploiting this property made it feasible to solve the problem using CVX, a MATLAB-based convex modeling framework. The fluence was stratified into even step sizes, and the MLC segments, limited to 300, were calculated. The patients studied were replanned using Eclipse with the same beam angles. Results: Compared to the original 4π plan, the stratified 4π plan increased the maximum/mean dose for, in Gy, by 1.0/0.0 (brainstem), 0.5/0.2 (chiasm), 0.0/0.0 (spinal cord), 1.9/0.3 (L eye), 0.7/0.2 (R eye), 0.4/0.4 (L lens), 0.3/0.3 (R lens), 1.0/0.8 (L Optical Nerve), 0.5/0.3 (R Optical Nerve), 0.3/0.2 (L Cochlea), 0.1/0.1 (R Cochlea), 4.6/0.2 (brain), 2.4/0.1 (brain-PTV), 5.1/0.9 (PTV). Compared to Eclipse, which generated an average of 607 segments, the stratified plan reduced (−) or increased (+) the maximum/mean dose, in Gy, by −10.2/−4.1 (brainstem), −10.5/−8.9 (chiasm), +0.0/−0.1 (spinal cord), −4.9/−3.4 (L eye), −4.1/−2.5 (R eye), −2.8/−2.7 (L lens), −2.1/−1.9 (R lens), −7.6/−6.5 (L Optical Nerve), −8.9/−6.1 (R Optical Nerve), −1.3/−1.9 (L Cochlea), −1.8/−1.8 (R Cochlea), +1.7/−2.1 (brain), +3.2/−2.6 (brain-PTV), +1.8/+0.3 Gy (PTV. The stratified plan was also more homogeneous in the PTV. Conclusion: This novel solver can transform complicated fluence maps into significantly fewer deliverable MLC segments than the commercial system while achieving superior dosimetry. Funding support partially contributed by Varian.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-C-BRB-03: Cross-Institutional Validation of An Ultrafast Automatic Planning Platform for Breast Irradiation

Purpose: Recently an ultrafast automatic planning system for breast irradiation using tangential beams was developed by modeling relationships between patient anatomy and achieved dose distribution. This study evaluates the performance of this system when applied to a different patient population and dose calculation algorithm. Methods: The system and its anatomy-to-dose models was developed at institution A based on 20 cases, which were planned using manual fluence painting technique and calculated WITH heterogeneity correction. Institution B uses field-in-field planning technique and dose calculation WITHOUT heterogeneity correction. 11 breast cases treated at Institution B were randomly selected for retrospective study, including left and right sides, and different breast size (irradiated volumes defined by Jaw/MLC opening range from 875cc to 3516cc). Comparisons between plans generated automatically (Auto-Plans) and those used for treatment (Clinical-Plans) included: energy choice (single/mixed), volumes receiving 95%/100%/105%/110% Rx dose (V95%/V100%/V105%/V100%) relative to irradiated volume, D1cc, and LungV20Gy. Results: In 9 out of 11 cases single/mixed energy choice made by the software agreed with Clinical-Plans. For the remaining 2 cases software recommended using mixed energy and dosimetric improvements were observed. V100% were similar (p=0.223, Wilcoxon Signed-Rank test) between Auto-Plans and Clinical-Plans (57.6±8.9% vs. 54.8±9.5%). V95% is 2.3±3.0% higher for Auto-Plans (p=0.027), indicating reduced cold areas. Hot spot volume V105% were significantly reduced in Auto-Plan by 14.4±7.2% (p=0.004). Absolute V105% was reduced from 395.6±359.9cc for Clinical-Plans to 108.7±163cc for Auto-Plans. D1cc was 107.4±2.8% for Auto-Plans, and 109.2±2.4% for Clinical-Plans (p=0.056). LungV20Gy were 13.6±4.0% for Auto-Plan vs. 14.0±4.1% for Clinical-Plans (p=0.043). All optimizations were finished within 1.5min. Conclusion: The performance of this breast auto-planning system remained stable and satisfactory when applied to a different patient population and dose calculation algorithm. The auto-planning system was able to produce clinically similar Rx dose coverage with significantly improved homogeneity inside breast tissue, in less than 1.5min.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The Attraction Indian Buffet Distribution

We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in Bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular exchangeable prior distribution for latent feature matrices. In the presence of additional information, however, the exchangeability assumption is not reasonable or desirable. The AIBD can incorporate pairwise similarity information, yet it preserves many properties of the IBP, including the distribution of the total number of features. Thus, much of the interpretation and intuition that one has for the IBP directly carries over to the AIBD. A temperature parameter controls the degree to which the similarity information affects feature-sharing between observations. Unlike other nonexchangeable distributions for feature allocations, the probability mass function of the AIBD has a tractable normalizing constant, making posterior inference on hyperparameters straight-forward using standard MCMC methods. A novel posterior sampling algorithm is proposed for the IBP and the AIBD. We demonstrate the feasibility of the AIBD as a prior distribution in feature allocation models and compare the performance of competing methods in simulations and an application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2018

Dynamic ADMM for Real-Time Optimal Power Flow

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearization of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation of the AC power flows, and it avoids ubiquitous metering to gather the state of noncontrollable resources. Optimality and convergence of the proposed algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Dynamic ADMM for Real-Time Optimal Power Flow: Preprint

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation of the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Moment preserving adaptive particle weights using octree velocity distributions for PIC simulations

The ratio of computational to physical particles is of primary concern to statistical particle based simulations such as DSMC and PIC. An adaptive computational particle weight algorithm is presented that conserves mass, momentum, and energy. This algorithm is then enhanced with an octree adaptive mesh in velocity space to mitigate artificial thermalization. The new octree merge is compared to a merge that randomly selects merge partners for a bi-Maxwellian velocity distribution. Results for crossing beams in a fixed potential well along with an electrostatic PIC version with and without MCC collisions based ionizing breakdown show the advantages of the merge algorithm to both fixed particle weights and randomly selected merge partners.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

On the Feasibility of Simulation-Driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems

Runtime systems that automate the execution of applications on distributed cyberinfrastructures need to make scheduling decisions. Researchers have proposed many scheduling algorithms, but most of them are designed based on analytical models and assumptions that may not hold in practice. The literature is thus rife with algorithms that have been evaluated only within the scope of their underlying assumptions but whose practical effectiveness is unclear. It is thus difficult for developers to decide which algorithm to implement in their runtime systems.To obviate the above difficulty, we propose an approach by which the runtime system executes, throughout application execution, simulations of this very execution. Each simulation is for a different algorithm in a scheduling algorithm portfolio, and the best algorithm is selected based on simulation results. The main objective of this work is to evaluate the feasibility and potential merit of this portfolio scheduling approach, even in the presence of simulation inaccuracy, when compared to the traditional one-algorithm approach. We perform this evaluation via a case study in the context of scientific workflows. Our main finding is that portfolio scheduling can outperform the best one-algorithm approach even in the presence of relatively large simulation inaccuracies.

Casanova, Henri↗
DOE OSTI2018

Scanning the skeleton of the 4D F-theory landscape

Using a one-way Monte Carlo algorithm from several different starting points, we get an approximation to the distribution of toric threefold bases that can be used in four-dimensional F-theory compactification. We separate the threefold bases into “resolvable” ones where the Weierstrass polynomials (f, g) can vanish to order (4, 6) or higher on codimension-two loci and the “good” bases where these (4, 6) loci are not allowed. A simple estimate suggests that the number of distinct resolvable base geometries exceeds 10 3000 , with over 10 250 “good” bases, though the actual numbers are likely much larger. We find that the good bases are concentrated at specific “end points” with special isolated values of h 1,1 that are bigger than 1,000. These end point bases give Calabi-Yau fourfolds with specific Hodge numbers mirror to elliptic fibrations over simple threefolds. The non-Higgsable gauge groups on the end point bases are almost entirely made of products of E 8 , F 4 , G 2 and SU(2). Nonetheless, we find a large class of good bases with a single non-Higgsable SU(3). Moreover, by randomly contracting the end point bases, we find many resolvable bases with h 1,1 (B) ~ 50-200 that cannot be contracted to another smooth threefold base.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Data-Driven Mathematical Modeling of the Effect of Particle Size Distribution on the Transitory Reaction Kinetics of Hot Metal Desulfurization

The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗