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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-Driven Approaches for Bead Geometry Prediction Via Melt Pool Monitoring

In the realm of additive manufacturing, the selection of process parameters to avoid over and under deposition entails a time-consuming and resource-intensive trial-and-error approach. Given the distinct characteristics of each part geometry, there is a pressing need for advancing real-time process monitoring and control to ensure consistent and reliable part dimensional accuracy. Here, this research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models’ abilities to recognize complex, non-linear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single-layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Nucleon Axial Radius and Muonic Hydrogen — A New Analysis and Review

Weak capture in muonic hydrogen (μH) as a probe of the chiral properties and nucleon structure predictions of quantum chromodynamics (QCD) is reviewed. A recent determination of the axial-vector charge radius squared, , from a model independent z expansion analysis of neutrino-nucleon scattering data is employed in conjunction with the MuCap measurement of the singlet muonic hydrogen capture rate, , to update the induced pseudoscalar nucleon coupling derived from experiment, and predicted by chiral perturbation theory. Accounting for correlated errors this implies , confirming theory at the 8% level. If instead, the predicted expression for is employed as input, then the capture rate alone determines , or together with the independent z expansion neutrino scattering result, a weighted average . Sources of theoretical uncertainty are critically examined and potential experimental improvements are described that can reduce the capture rate error by about a factor of 3. Muonic hydrogen can thus provide a precise and independent value which may be compared with other determinations, such as ongoing lattice gauge theory calculations. The importance of an improved determination for phenomenology is illustrated by considering the impact on critical neutrino-nucleus cross sections at neutrino oscillation experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Intrafraction Variation of Mean Tumor Position During Image-Guided Hypofractionated Stereotactic Body Radiotherapy for Lung Cancer

Purpose: Prolonged delivery times during daily cone-beam computed tomography (CBCT)-guided lung stereotactic body radiotherapy (SBRT) introduce concerns regarding intrafraction variation (IFV) of the mean target position (MTP). The purpose of this study was to evaluate the magnitude of the IFV-MTP and to assess target margins required to compensate for IFV and postonline CBCT correction residuals. Patient, treatment, and tumor characteristics were analyzed with respect to their impact on IFV-MTP. Methods and Materials: A total of 126 patients with 140 tumors underwent 659 fractions of lung SBRT. Dose prescribed was 48 or 60 Gy in 12 Gy fractions. Translational target position correction of the MTP was performed via onboard CBCT. IFV-MTP was measured as the difference in MTP between the postcorrection CBCT and the posttreatment CBCT excluding residual error. Results: IFV-MTP was 0.2 {+-} 1.8 mm, 0.1 {+-} 1.9 mm, and 0.01 {+-} 1.5 mm in the craniocaudal, anteroposterior, and mediolateral dimensions and the IFV-MTP vector was 2.3 {+-} 2.1 mm. Treatment time and excursion were found to be significant predictors of IFV-MTP. An IFV-MTP vector greater than 2 and 5 mm was seen in 40.8% and 7.2% of fractions, respectively. IFV-MTP greater than 2 mm was seen in heavier patients with larger excursions and longer treatment times. Significant differences in IFV-MTP were seen between immobilization devices. The stereotactic frame immobilization device was found to be significantly less likely to have an IFV-MTP vector greater than 2 mm compared with the alpha cradle, BodyFIX, and hybrid immobilization devices. Conclusions: Treatment time and respiratory excursion are significantly associated with IFV-MTP. Significant differences in IFV-MTP were found between immobilization devices. Target margins for IFV-MTP plus post-correction residuals are dependent on immobilization device with 5-mm uniform margins being acceptable for the frame immobilization device.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Fast QSTS for Distributed PV Impact Studies Using Vector Quantization and Variable Time-Steps

As the penetration level of PV systems increases, utilities are in great need of methods to quickly and effectively analyze PV's impact on electric distribution systems. Compared to other conventional methods, quasi-static time-series (QSTS) based analysis has demonstrated its advantages of accurately capturing the impact of interest, e.g. control device operations, and min and max voltages. However, yearlong QSTS simulation with 1-second granularity is currently too computationally expensive for regular use. Therefore, vector quantization (VQ) and variable time-step (VTS) solution methods have been developed to accelerate QSTS simulation. This paper proposes the combination of VQ with VTS. We investigate the effectiveness of implementing VQ in combination with 2 VTS algorithms, predetermined time-step (PT) and VTS with backtrack downsampling (VTS-BD) to demonstrate a runtime reduction of 99.55% with error metrics below acceptable thresholds.

14 SOLAR ENERGY↗
DOE OSTI2020

An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

Mass absorption cross-section of black carbon (MAC BC ) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MAC BC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MAC BC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MAC BC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MAC BC using different statistical metrics (such as coefficient of determination (R 2 ), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MAC BC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MAC BC , we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Lossy compression of statistical data using quantum annealer

Abstract We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3–3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

WE-AB-BRA-02: Development of Biomechanical Models to Describe Dose-Volume Response to Liver Stereotactic Body Radiation Therapy (SBRT) Patients

Purpose: Previous studies have shown that radiotherapy treatment for liver metastases causes marked liver hypertrophy in areas receiving low dose and atrophy/fibrosis in areas receiving high dose. The purpose of this work is to develop and evaluate a biomechanical model-based dose-response model to describe these liver responses to SBRT. Methods: In this retrospective study, a biomechanical model-based deformable registration algorithm, Morfeus, was expanded to include dose-based boundary conditions. Liver and tumor volumes were contoured on the planning images and CT/MR images three months post-RT and converted to finite element models. A thermal expansion-based relationship correlating the delivered dose and volume response was generated from 22 patients previously treated. This coefficient, combined with the planned dose, was applied as an additional boundary condition to describe the volumetric response of the liver of an additional cohort of metastatic liver patients treated with SBRT. The accuracy of the model was evaluated based on overall volumetric liver comparisons and the target registration error (TRE) using the average deviations in positions of identified vascular bifurcations on each set of registered images, with a target accuracy of the 2.5mm isotropic dose grid (vector dimension 4.3mm). Results: The thermal expansion coefficient models the volumetric change of the liver to within 3%. The accuracy of Morfeus with dose-expansion boundary conditions a TRE of 5.7±2.8mm compared to 11.2±3.7mm using rigid registration and 8.9±0.28mm using Morfeus with only spatial boundary conditions. Conclusion: A biomechanical model has been developed to describe the volumetric and spatial response of the liver to SBRT. This work will enable the improvement of correlating functional imaging with delivered dose, the mapping of the delivered dose from one treatment onto the planning images for a subsequent treatment, and will further provide information to assist with the biological characterization of patients’ response to radiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Vector spin modeling for magnetic tunnel junctions with voltage dependent effects

Integration and co-design of CMOS and spin transfer devices requires accurate vector spin conduction modeling of magnetic tunnel junction (MTJ) devices. A physically realistic model of the MTJ should comprehend the spin torque dynamics of nanomagnet interacting with an injected vector spin current and the voltage dependent spin torque. Vector spin modeling allows for calculation of 3 component spin currents and potentials along with the charge currents/potentials in non-collinear magnetic systems. Here, we show 4-component vector spin conduction modeling of magnetic tunnel junction devices coupled with spin transfer torque in the nanomagnet. Nanomagnet dynamics, voltage dependent spin transport, and thermal noise are comprehended in a self-consistent fashion. We show comparison of the model with experimental magnetoresistance (MR) of MTJs and voltage degradation of MR with voltage. Proposed model enables MTJ circuit design that comprehends voltage dependent spin torque effects, switching error rates, spin degradation, and back hopping effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

A Space-Filling Visualization Technique for Multivariate Small World Graphs

We introduce an information visualization technique, known as GreenCurve, for large sparse graphs that exhibit small world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a sophisticated algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. We show in the paper that the GreenCurve technology is (1) theoretically sustainable by introducing an error estimation metric to measure the fidelity of the new graph representation, (2) empirically rigorous by conducting a usability study to investigate its strengths and weaknesses against the traditional graph layout, and (3) pragmatically feasible by applying it to analyze stressed conditions of the large scale electric power grid on the west coast.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Image Guidance During Head-and-Neck Cancer Radiation Therapy: Analysis of Alignment Trends With In-Room Cone-Beam Computed Tomography Scans

Purpose: On-board cone-beam computed tomography (CBCT) is currently available for alignment of patients with head-and-neck cancer before radiotherapy. However, daily CBCT is time intensive and increases the overall radiation dose. We assessed the feasibility of using the average couch shifts from the first several CBCTs to estimate and correct for the presumed systematic setup error. Methods and Materials: 56 patients with head-and-neck cancer who received daily CBCT before intensity-modulated radiation therapy had recorded shift values in the medial-lateral, superior-inferior, and anterior-posterior dimensions. The average displacements in each direction were calculated for each patient based on the first five or 10 CBCT shifts and were presumed to represent the systematic setup error. The residual error after this correction was determined by subtracting the calculated shifts from the shifts obtained using daily CBCT. Results: The magnitude of the average daily residual three-dimensional (3D) error was 4.8 {+-} 1.4 mm, 3.9 {+-} 1.3 mm, and 3.7 {+-} 1.1 mm for uncorrected, five CBCT corrected, and 10 CBCT corrected protocols, respectively. With no image guidance, 40.8% of fractions would have been >5 mm off target. Using the first five CBCT shifts to correct subsequent fractions, this percentage decreased to 19.0% of all fractions delivered and decreased the percentage of patients with average daily 3D errors >5 mm from 35.7% to 14.3% vs. no image guidance. Using an average of the first 10 CBCT shifts did not significantly improve this outcome. Conclusions: Using the first five CBCT shift measurements as an estimation of the systematic setup error improves daily setup accuracy for a subset of patients with head-and-neck cancer receiving intensity-modulated radiation therapy and primarily benefited those with large 3D correction vectors (>5 mm). Daily CBCT is still necessary until methods are developed that more accurately determine which patients may benefit from alternative imaging strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Secure Wireless Communication Using Distributed Coherent Transmission and Spatial Signal Decomposition

We present a new approach to secure wireless communications using coherent distributed transmission of signals that are spatially decomposed between a two-element distributed antenna array. High-accuracy distributed coordination of microwave wireless systems supports the ability to transmit different parts of a signal from separate transmitters such that they combine coherently at a designated destination. In this article, we explore this concept using a two-element coherent distributed phased array where each of the two transmitters sends a separate component of a communication signal, where each symbol is decomposed into a sum of two pseudorandom signal vectors, the coherent summation of which yields the intended symbol. By directing the transmission to an intended receiver using distributed beamforming, the summation of the two vector components is largely confined to a spatial region at the destination receiver. We implement the technique in a $50−λ$ array operating at 3 GHz. We evaluate the symbol error rate (SER) in 2-D space through simulation and measurement, showing that the approach yields a spatially confined secure region where the information is recoverable (i.e., the received signal has low SER), and outside of which the information is unrecoverable (high SER). The proposed system is also compared against a traditional beamforming system where each node sends the same data. We validate experimentally that our approach achieves a low SER of 0.0082 at broadside and an SER above 0.25 at all other locations compared to a traditional beamforming approach that achieves a SER of 0 at all locations measured.

Engineering - Electronic and electrical engineerin↗
DOE OSTI2016

SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

3D reconstruction of magnetization from dichroic soft X-ray transmission tomography

The development of magnetic nanostructures for applications in spintronics requires methods capable of visualizing their magnetization. Soft X-ray magnetic imaging combined with circular magnetic dichroism allows nanostructures up to 100–300 nm in thickness to be probed with resolutions of 20–40 nm. Here a new iterative tomographic reconstruction method to extract the three-dimensional magnetization configuration from tomographic projections is presented. The vector field is reconstructed by using a modified algebraic reconstruction approach based on solving a set of linear equations in an iterative manner. In conclusion, the application of this method is illustrated with two examples (magnetic nano-disc and micro-square heterostructure) along with comparison of error in reconstructions, and convergence of the algorithm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Quantum Complexity of the Kronecker Coefficients

Whether or not the Kronecker coefficients of the symmetric group count some set of combinatorial objects is a longstanding open question. In this work we show that a given Kronecker coefficient is proportional to the rank of a projector that can be measured efficiently using a quantum computer. In other words a Kronecker coefficient counts the dimension of the vector space spanned by the accepting witnesses of a QMA verifier, where QMA is the quantum analogue of NP . This implies that approximating the Kronecker coefficients to within a given relative error is not harder than a certain natural class of problems that captures the complexity of estimating thermal properties of quantum many-body systems. A second consequence is that deciding positivity of Kronecker coefficients is contained in QMA , complementing a recent NP -hardness result of Ikenmeyer, Mulmuley, and Walter. We obtain similar results for the related problem of approximating row sums of the character table of the symmetric group. Finally, we discuss an efficient quantum algorithm that approximates normalized Kronecker coefficients to inverse-polynomial additive error. Published by the American Physical Society 2024

Physics↗
DOE OSTI2017

Dark Matter Search in a Proton Beam Dump with MiniBooNE

The MiniBooNE-DM Collaboration searched for vector-boson mediated production of dark matter using the Fermilab 8-GeV Booster proton beam in a dedicated run with 1.86 × 10 20 protons delivered to a steel beam dump. The MiniBooNE detector, 490 m downstream, is sensitive to dark matter via elastic scattering with nucleons in the detector mineral oil. Analysis methods developed for previous MiniBooNE scattering results were employed, and several constraining data sets were simultaneously analyzed to minimize systematic errors from neutrino flux and interaction rates. No excess of events over background was observed, leading to a 90% confidence limit on the dark matter cross section parameter, Y = ε 2 α D (m χ /m V ) 4 ≲10 –8 , for α D = 0.5 and for dark matter masses of 0.01 < m χ < 0.3 GeV in a vector portal model of dark matter. This is the best limit from a dedicated proton beam dump search in this mass and coupling range and extends below the mass range of direct dark matter searches. Here, these results demonstrate a novel and powerful approach to dark matter searches with beam dump experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Interstellar flow longitude from the symmetry of the pickup ion cutoff at 1 AU

We provide a proof of concept that the pickup ion (PUI) cutoff speed variation with ecliptic longitude can provide the interstellar neutral (ISN) flow longitude λ{sub ISN∞} outside the heliosphere with a precision of the order of 0.°1. We compare 2007–2014 STEREO A PLASTIC observations in the solar wind frame with a simple analytical cutoff model and perform a Pearson correlation of the cutoff as a function of longitude with those values mirrored about a location λ{sub M} that slides in longitude. The resulting maximum correlation at λ{sub M} = 255.°21 ± 0.°04 reflects the ISN upwind direction with a purely statistical uncertainty. This result is consistent with recent ISN flow longitude values, but a precision determination requires the evaluation of underlying systematic effects. Obtaining a precision ISN flow longitude is highly complementary to the IBEX ISN observations, which return a narrow tube in the ISN parameter space that couples flow speed and longitude tightly, but leaves a substantially larger error bar along the tube. The ISN flow provides one cardinal axis of the heliosphere, with a strong leverage on the plane subtended by the ISN velocity and the interstellar magnetic field vector, which controls the shape of the heliosphere and its interaction with the interstellar medium. In addition, a discussion about potential temporal variations of the ISN flow has started. Both issues require a precision determination of the ISN flow velocity over decade-long time intervals. ACE SWICS, STEREO PLASTIC, and SOHO CTOF PUI data allow such long-term studies over almost two decades.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Challenge Problem 1: Preliminary Model Development and Assessment of Flexible Heat Transfer Modeling Approaches

This report presents the modeling progress of a university consortium studying flexible modeling for turbulent heat transfer. In the context of advanced nuclear reactor design, where challenges include non-unity Prandtl fluids, free and mixed convection, and transitional flow, computational fluid dynamics methods are underutilized due to low confidence in modeling approaches and high computational cost. This report evaluates the performance of engineering CFD models in diabatic, buoyant turbulent flow. It finds that all turbulence models including several variants of the k-ε and k-ω models struggle to predict accurate turbulent momentum and heat transfer in such flows. The Nusselt numbers have been compared between the models and the DNS data, where calculations have been performed for each case and trends have shown a good agreement between DNS estimated Nusselt numbers and available correlations as well as experimental data. A novel DNS correlation for high Pr cases as they are transitioning from mixed convection to natural convection has been proposed. While Nusselt number errors relative to DNS range from 20% to 50%, the models capture similar trends to DNS with respect to Nusselt suppression and amplification under varying levels of buoyancy effect. This report also highlights model form error as a significant contributor to CFD predictions and proposes a framework for quantifying model error and improving confidence in CFD calculations. To improve the predictive capability of engineering CFD models, data-driven approaches for turbulence models are investigated. Theoretical frameworks based on the invariant tensor / vector basis neural networks for prediction of Reynolds stresses and turbulent heat fluxes are employed. The models are developed using direct numerical simulations data for forced convection flows of different fluids in vertical planar channel domain. The framework is implemented in spectral element solvers Nek5000 / nekRS and has shown a potential for future development and consideration of mixed convection flows.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗