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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probing parity violation with the four-point correlation function of BOSS galaxies

Parity-violating physics in the early universe can leave detectable traces in late-time observables. While vector- and tensor-type parity violation can be observed in the B -modes of the cosmic microwave background, scalar-type signatures are visible only in the four-point correlation function (4PCF) and beyond. This work presents a blind test for parity violation in the 4PCF of the baryon oscillation spectroscopic survey (BOSS) CMASS sample, considering galaxy separations in the range [20; 160]h -1 Mpc The parity-odd 4PCF contains no contributions from standard Λ CDM physics and can be efficiently measured using recently developed estimators. Data are analyzed using both a nonparametric rank test (comparing the BOSS 4PCFs to those of realistic simulations) and a compressed χ 2 analysis, with the former avoiding the assumption of a Gaussian likelihood. These find similar results, with the rank test giving a detection probability of 99.6% (2.9 σ). This provides significant evidence for parity violation, from either cosmological sources or systematics. We perform a number of systematic tests: although these do not reveal any observational artifacts, we cannot exclude the possibility that our detection is caused by the simulations not faithfully representing the statistical properties of the BOSS data. Our measurements can be used to constrain physical models of parity violation. As an example, we consider a coupling between the inflaton and a U (1) gauge field and place bounds on the latter’s energy density, which are several orders of magnitude stronger than those previously reported. Upcoming probes such as DESI and Euclid will reveal whether our detection of parity violation is due to new physics, and strengthen the bounds on a variety of models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-G-TeP1-06: Fast GPU Framework for Four-Dimensional Monte Carlo in Adaptive Intensity Modulated Proton Therapy (IMPT) for Mobile Tumors

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) treatment planning and verification using four-dimensional CT (4DCT) for adaptive IMPT for lung cancer patients. Methods: A validated GPU MC code, gPMC, has been linked to the patient database at our institution and employed to compute the dose-influence matrices (Dij) on the planning CT (pCT). The pCT is an average of the respiratory motion of the patient. The Dijs and patient structures were fed to the optimizer to calculate a treatment plan. To validate the plan against motion, a 4D dose distribution averaged over the possible starting phases is calculated using the 4DCT and a model of the time structure of the delivered spot map. The dose is accumulated using vector maps created by a GPU-accelerated deformable image registration program (DIR) from each phase of the 4DCT to the reference phase using the B-spline method. Calculation of the Dij matrices and the DIR are performed on a cluster, with each field and vector map calculated in parallel. Results: The Dij production takes ∼3.5s per beamlet for 10e6 protons, depending on the energy and the CT size. Generating a plan with 4D simulation of 1000 spots in 4 fields takes approximately 1h. To test the framework, IMPT plans for 10 lung cancer patients were generated for validation. Differences between the planned and the delivered dose of 19% in dose to some organs at risk and 1.4/21.1% in target mean dose/homogeneity with respect to the plan were observed, suggesting potential for improvement if adaptation is considered. Conclusion: A fast MC treatment planning framework has been developed that allows reliable plan design and verification for mobile targets and adaptation of treatment plans. This will significantly impact treatments for lung tumors, as 4D-MC dose calculations can now become part of planning strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems: Preprint

An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods - linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost - are applied to two different sets of preprocess data for the prediction of the frequency nadir in the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. The training and testing data sets are collected by extensive generation scheduling simulations on the Multi-timescale Integrated Dynamic and Scheduling (MIDAS) toolbox. Numerical results show that all five machine learning methods can achieve high performance accuracy for power system nadir frequency estimation. Among them, the gradient boosting and the XGBoost are clear winners by providing the best prediction accuracy.

data driven↗
DOE OSTI2015

SU-E-J-48: Imaging Origin-Radiation Isocenter Coincidence for Linac-Based SRS with Novalis Tx

Purpose To implement and evaluate an image-based Winston-Lutz (WL) test to measure the displacement between ExacTrac imaging origin and radiation isocenter on a Novalis Tx system using RIT V6.2 software analysis tools. Displacement between imaging and radiation isocenters was tracked over time. The method was applied for cone-based and MLC-based WL tests. Methods The Brainlab Winston-Lutz phantom was aligned to room lasers. The ExacTrac imaging system was then used to detect the Winston- Lutz phantom and obtain the displacement between the center of the phantom and the imaging origin. EPID images of the phantom were obtained at various gantry and couch angles and analyzed with RIT calculating the phantom center to radiation isocenter displacement. The RIT and Exactrac displacements were combined to calculate the displacement between imaging origin and radiation isocenter. Results were tracked over time. Results Mean displacements between ExacTrac origin and radiation isocenter were: VRT: −0.1mm ± 0.3mm, LNG: 0.5mm ± 0.2mm, LAT: 0.2mm ± 0.2mm (vector magnitude of 0.7 ± 0.2mm). Radiation isocenter was characterized by the mean of the standard deviations of the WL phantom displacements: σVRT: 0.2mm, σLNG: 0.4mm, σLAT: 0.6mm. The linac couch base was serviced to reduce couch walkout. This reduced σLAT to 0.2mm. These measurements established a new baseline of radiation isocenter-imaging origin coincidence. Conclusion The image-based WL test has ensured submillimeter localization accuracy using the ExacTrac imaging system. Standard deviations of ExacTrac-radiation isocenter displacements indicate that average agreement within 0.3mm is possible in each axis. This WL test is a departure from the tradiational WL in that imaging origin/radiation isocenter agreement is the end goal not lasers/radiation isocenter.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Vector dark matter at the LHC

We consider monojet searches at the Large Hadron Collider (LHC) for spin-1 dark matter that interacts with quarks through a contact operator. If the dark matter particles are produced with longitudinal polarizations, then the production matrix element is enhanced by factors of the energy. We show that this particularly effective search strategy can test models for which the energy suppression scale of the operator is as large as 10 5 TeV. As such, these searches can probe a large class of models for which the contact-operator approximation is valid. We find that for contact operators that permit velocity-independent dark matter-nucleon scattering, LHC monojet searches for spin-1 dark matter are competitive with or far surpass direct-detection searches depending on whether the scattering is spin independent or spin dependent, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Precise Experimental Test of the Luttinger Theorem and Particle-Hole Symmetry for a Strongly Correlated Fermionic System

A fundamental concept in physics is the Fermi surface, the constant-energy surface in momentum space encompassing all the occupied quantum states at absolute zero temperature. In 1960, Luttinger postulated that the area enclosed by the Fermi surface should remain unaffected even when electron-electron interaction is turned on, so long as the interaction does not cause a phase transition. Understanding what determines the Fermi surface size is a crucial and yet unsolved problem in strongly interacting systems such as high-$T_c$ superconductors. Here we present a precise test of the Luttinger theorem for a two-dimensional Fermi liquid system where the exotic quasiparticles themselves emerge from the strong interaction, namely, for the Fermi sea of composite fermions (CFs). Via direct, geometric resonance measurements of the CFs’ Fermi wave vector down to very low electron densities, we show that the Luttinger theorem is obeyed over a significant range of interaction strengths, in the sense that the Fermi sea area is determined by the density of the minority carriers in the lowest Landau level. Our data in this work also address the ongoing debates on whether or not CFs obey particle-hole symmetry, and if they are Dirac particles. We find that particle-hole symmetry is obeyed, but the measured Fermi sea area differs quantitatively from that predicted by the Dirac model for CFs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Development of a MEMS dual-axis differential capacitance floating element shear stress sensor

A single-axis MEMS wall shear stress sensor with differential capacitive transduction method is produced. Using a synchronous modulation and demodulation interface circuit, the system is capable of making real time measurements of both mean and fluctuating wall shear stress. A sensitivity of 3.44 mV/Pa is achieved, with linearity in response demonstrated up to testing limit of 2 Pa. Minimum detectable signals of 340 μPa at 100 Hz and 120 μPa at 1 kHz are indicated, with a resonance of 3.5 kHz. Multiple full scale wind tunnel tests are performed, producing spectral measurements of turbulent boundary layers in wind speeds ranging up to 0.5 Ma (18 Pa of mean wall shear stress). The compact packaging allows for minimally invasive installation, and has proven relatively robust over multiple testing events. Temperature sensitivity, likely due to poor CTE matching of packaged materials, is an ongoing concern being addressed. These successes are being directly leveraged into a development plan for a dual-axis wall shear stress sensor, capable of producing true vector estimates at the wall.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD 50 ). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R 2 = 0.88), validation set (R 2 = 0.95), and true external test set (R 2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Theory of subcycle time-resolved photoemission: Application to terahertz photodressing in graphene

Here, motivated by recent experimental progress we revisit the theory of pump–probe time- and angle-resolved photoemission spectroscopy (trARPES), which is one of the most powerful techniques to trace transient pump-driven modifications of the electronic properties. The pump-induced dynamics can be described in different gauges for the light–matter interaction. Standard minimal coupling leads to the velocity gauge, defined by linear coupling to the vector potential. In the context of tight-binding (TB) models, the Peierls substitution is the commonly employed scheme for single-band models. Multi-orbital extensions – including the coupling of the dipole moments to the electric field – have been introduced and tested recently. In this work, we derive the theory of time-resolved photoemission within both gauges from the perspective of nonequilibrium Green’s functions. This approach naturally incorporates the photoelectron continuum, which allows for a direct calculation of the observable photocurrent. Following this route we introduce gauge-invariant expressions for the time-resolved photoemission signal. The theory is applied to graphene pumped with short terahertz pulses, which we treat within a first-principles TB model. We investigate the gauge invariance and discuss typical effects observed in subcycle time-resolved photoemission. Our formalism is an ideal starting point for realistic trARPES simulations including scattering effects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

TESTING CPT SYMMETRY WITH CURRENT AND FUTURE CMB MEASUREMENTS

In this paper, we use the current and future cosmic microwave background (CMB) experiments to test the Charge-Parity-Time Reversal (CPT) symmetry. We consider a CPT-violating interaction in the photon sector L{sub cs}∼p{sub μ}A{sub ν} F-tilde {sup μν}, which gives rise to a rotation of the polarization vectors of the propagating CMB photons. By combining the 9 yr WMAP, BOOMERanG 2003, and BICEP1 observations, we obtain the current constraint on the isotropic rotation angle α-bar =−2.12±1.14 (1σ), indicating that the significance of the CPT violation is about 2σ. Here, we particularly take the systematic errors of CMB measurements into account. Then, we study the effects of the anisotropies of the rotation angle [Δα( n-hat )] on the CMB polarization power spectra in detail. Due to the small effects, the current CMB polarization data cannot constrain the related parameters very well. We obtain the 95% C.L. upper limit of the variance of the anisotropies of the rotation angle C {sup α}(0) < 0.035 from all of the CMB data sets. More interestingly, including the anisotropies of rotation angle could lower the best-fit value of r and relax the tension on the constraints of r between BICEP2 and Planck. Finally, we investigate the capabilities of future Planck polarization measurements on α-bar and Δα( n-hat ). Benefited from the high precision of Planck data, the constraints of the rotation angle can be significantly improved.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem

Solving problems related to planning and operations of large-scale power systems is challenging on classical computers due to their inherent nature as mixed-integer and nonlinear problems. Quantum computing provides new avenues to approach these problems. We develop a hybrid quantum-classical algorithm for the Unit Commitment (UC) problem in power systems which aims at minimizing the total cost while optimally allocating generating units to meet the hourly demand of the power loads. The hybrid algorithm combines a variational quantum algorithm (VQA) with a classical Benders-type heuristic. The resulting algorithm computes approximate solutions to UC in three stages: i) a collection of UC vectors capable meeting the power demand with lowest possible operating costs is generated based on VQA; ii) a classical sequential least squares programming (SLSQP) routine is leveraged to find the optimal power level corresponding to a predetermined number of candidate vectors; iii) in the last stage, the approximate solution of UC along with generating units power level combination is given. To demonstrate the effectiveness of the presented method, three different systems with 3 generating units, 10 generating units, and 26 generating units were tested for different time periods. In addition, convergence of the hybrid quantum-classical algorithm for select time periods is proven out on IonQ's Forte system.

Aboumrad, Willie [IonQ, Inc]↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
DOE OSTI2023

Dataset for SIAM MPI23 Project "Model inversion for complex physical systems using low-dimensional surrogates"

Training and testing data for the SIAM MPI23 workshop "Model inversion for complex physical systems using low-dimensional surrogates". The data consists of ensembles of input and output pairs corresponding to queries of a 2D saturated groundwater flow model. The inputs consist of random vectors which map to discretized model parameter fields via a Kosambi-Karhunen-Loève expansion. Output corresponds to discretized pressure fields.

basis adaptation, conditional Karhunen-Loève expan↗
DOE OSTI2017

Sparse Bayesian Regression with Integrated Feature Selection for Nuclear Reactor Analysis

High-dimensional, nonlinear function estimation using large datasets is a current area of interest in the machine learning community, and applications may be found throughout the analytical sciences, where ever-growing datasets are making more information available to the analyst. In this paper, we leverage the existing relevance vector machine, a sparse Bayesian version of the well-studied support vector machine, and expand the method to include integrated feature selection and automatic function shaping. These innovations produce an algorithm that is able to distinguish variables that are useful for making predictions of a response from variables that are unrelated or confusing. We test the technology using synthetic data, conduct initial performance studies, and develop a model capable of making position-independent predictions of the coreaveraged burnup using a single specimen drawn randomly from a nuclear reactor core.

Dayman, Ken J↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Snorri Version 1.0

The software decomposes n-component time-series data into persistence vectors and provide visualizations of various summaries of this information. Persistence vectors are a type of vector, based on multidimensional persistent homology theory, which have magnitudes and directions that reflect dominant trends in the multi-component data. The software is a collection of MATLAB scripts, and thus MATLAB runs the scripts. The master script that calls other scripts is called Snorri. The purpose is thus analysis of time-series data to extract dominant trends or events and to classify the type of event. The software produces characteristic vectors (an average of the persistence vectors), which can have directions that reflect the overall dominant trend in a time-series data set. The MATLAB scripts have been partially converted to the PYTHON language and no do not have the full functionality of the MATLAB and are not fully tested; the python scripts are to be covered under this copyright assertion. Two versions of the PYTHON scripts are included, called: mph25.py and mph38.py, each with their own main.py file.

Callor, Nickolas↗
DOE OSTI2023

Generalizing mkFit and its Application to HL-LHC

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗