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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

A novel method to reconstruct the energy of hadronic showersin the CMS High Granularity Calorimeter (HGCAL) is presented. TheHGCAL is a sampling calorimeter with very fine transverse andlongitudinal granularity. The active media are silicon sensors andscintillator tiles readout by SiPMs and the absorbers are acombination of lead and Cu/CuW in the electromagnetic section, andsteel in the hadronic section. The shower reconstruction method isbased on graph neural networks and it makes use of a dynamicreduction network architecture. It is shown that the algorithm isable to capture and mitigate the main effects that normally hinderthe reconstruction of hadronic showers using classicalreconstruction methods, by compensating for fluctuations in themultiplicity, energy, and spatial distributions of the shower'sconstituents. The performance of the algorithm is evaluated usingtest beam data collected in 2018 prototype of the CMS HGCALaccompanied by a section of the CALICE AHCAL prototype. Thecapability of the method to mitigate the impact of energy leakagefrom the calorimeter is also demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of $\sqrt{s}=13\,\text {TeV} $ at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $\,\text {fb}^{-1}$. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to $\hbox {b}\bar{\hbox {b}}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline

Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗
NASA NTRS2016

Inverse Heat Conduction Methods in the CHAR Code for Aerothermal Flight Data Reconstruction

Reconstruction of flight aerothermal environments often requires the solution of an inverse heat transfer problem, which is an ill-posed problem of specifying boundary conditions from discrete measurements in the interior of the domain. This paper will present the algorithms implemented in the CHAR code for use in reconstruction of EFT-1 flight data and future testing activities. Implementation nuances will be discussed, and alternative hybrid-methods that are permitted by the implementation will be described. Results will be presented for a number of one-dimensional and multi-dimensional problems

Oliver, A Brandon↗
NASA NTRS2016

Inverse Heat Conduction Methods in the CHAR Code for Aerothermal Flight Data Reconstruction

Reconstruction of flight aerothermal environments often requires the solution of an inverse heat transfer problem, which is an ill-posed problem of determining boundary conditions from discrete measurements in the interior of the domain. This paper will present the algorithms implemented in the CHAR code for use in reconstruction of EFT-1 flight data and future testing activities. Implementation details will be discussed, and alternative hybrid-methods that are permitted by the implementation will be described. Results will be presented for a number of problems.

Oliver, A. Brandon↗
NASA NTRS1999

Experimental Investigation of Pool Boiling Heat Transfer Enhancement in Microgravity in the Presence of Electric Fields

In boiling high heat fluxes are possible driven by relatively small temperature differences, which make its use increasingly attractive in aerospace applications. The objective of the research is to develop ways to overcome specific problems associated with boiling in the low gravity environment by substituting the buoyancy force with the electric force to enhance bubble removal from the heated surface. Previous studies indicate that in terrestrial applications nucleate boiling heat transfer can be increased by a factor of 50, as compared to values obtained for the same system without electric fields. The goal of our research is to experimentally explore the mechanisms responsible for EHD heat transfer enhancement in boiling in low gravity conditions, by visualizing the temperature distributions in the vicinity of the heated surface and around the bubble during boiling using real-time holographic interferometry (HI) combined with high-speed cinematography. In the first phase of the project the influence of the electric field on a single bubble is investigated. Pool boiling is simulated by injecting a single bubble through a nozzle into the subcooled liquid or into the thermal boundary layer developed along the flat heater surface. Since the exact location of bubble formation is known, the optical equipment can be aligned and focused accurately, which is an essential requirement for precision measurements of bubble shape, size and deformation, as well as the visualization of temperature fields by HI. The size of the bubble and the frequency of bubble departure can be controlled by suitable selection of nozzle diameter and mass flow rate of vapor. In this approach effects due to the presence of the electric field can be separated from effects caused by the temperature gradients in the thermal boundary layer. The influence of the thermal boundary layer can be investigated after activating the heater at a later stage of the research. For the visualization experiments a test cell was developed. All four vertical walls of the test cell are transparent, and they allow transillumination with laser light for visualization experiments by HI. The bottom electrode is a copper cylinder, which is electrically grounded. The copper block is heated with a resistive heater and it is equipped with 6 thermocouples that provide reference temperatures for the measurements with HI. The top electrode is a mesh electrode. Bubbles are injected with a syringe into the test cell through the bottom electrode. The working fluids presently used in the interferometric visualization experiments, water and PF 5052, satisfy requirements regarding thermophysical, optical and electrical properties. A 30kV power supply equipped with a voltmeter allows to apply the electric field to the electrodes during the experiments. The magnitude of the applied voltage can be adjusted either manually or through the LabVIEW data acquisition and control system connected to a PC. Temperatures of the heated block are recorded using type-T thermocouples, whose output is read by a data acquisition system. Images of the bubbles are recorded with 35mm photographic and 16mm high-speed cameras, scanned and analyzed using various software packages. Visualized temperature fields HI allows the visualization of temperature fields in the vicinity of bubbles during boiling in the form of fringes. Typical visualized temperature distributions around the air bubbles injected into the thermal boundary layer in PF5052 are shown. The temperature of the heated surface is 35 C. The temperature difference for a pair of fringes is approximately 0.05 C. The heat flux applied to the bottom surface is moderate, and the fringe patterns are regular. In the image a bubble penetrating the thermal boundary layer is visible. Because of the axial symmetry of the problem, simplified reconstruction techniques can be applied to recover the temperature field. The thermal plume developing above the heated surface for more intensive heating is shown. The temperature distribution in the liquid is clearly 3D, and tomographic techniques have to be applied to recover the temperature distribution in such a physical situation. A sequence of interferometric images showing the temperature distribution around the rising bubble, recorded with a high-speed camera is shown. Again, the temperature distribution is 3D, and a more complex approach to the evaluation, the tomographic reconstruction has to be taken. Measurement of the temperature distribution from the fringe pattern temperature distributions that yield important information regarding heat transfer are determined. Two algorithms that allow the quantitative evaluation of interferometric fringe patterns and the reconstruction of temperature fields during boiling have been developed at the Heat Transfer Laboratory of the Johns Hopkins University. In the first algorithm the bubble is assumed to be axially symmetrical, which significantly reduces the computational effort for quantifying the temperature distribution around the bubble. For this purpose the thermal boundary layer around the bubble is divided into equidistant concentric shells, and the refractive index is assumed to be constant in each of the shells. Since large temperature gradients are expected in the vicinity of the bubble during boiling, the deflection of the light beam cannot be neglected in boiling experiments. Since the exit angle of the light beam is known, this allows to account for the deflections and phase shifts outside the boundary layer (in the bulk fluid and in the windows of the test cell). Three dimensional temperature distributions in the vicinity of the bubble are reconstructed using tomographic techniques. In tomography, the measurement volume is sliced into 2D planes. In the present study these planes are parallel to the heated surface. The objective is to determine the values of the field parameter of interest in form of the field function in these 2D planes. The field parameter is the change of the refractive index of the liquid in the measurement volume caused by temperature changes. By superimposing data for many 2D planes recorded at the same time instant, the 3D temperature distribution in the measurement volume is recovered.

Herman, Cila↗
NASA NTRS1998

Virtual Reality Simulation of the Effects of Microgravity in Gastrointestinal Physiology

The ultimate goal of this research is to create an anatomically accurate three-dimensional (3D) simulation model of the effects of microgravity in gastrointestinal physiology and to explore the role that such changes may have in the pharmacokinetics of drugs given to the space crews for prevention or therapy. To accomplish this goal the specific aims of this research are: 1) To generate a complete 3-D reconstructions of the human GastroIntestinal (GI) tract of the male and female Visible Humans. 2) To develop and implement time-dependent computer algorithms to simulate the GI motility using the above 3-D reconstruction.

Compadre, Cesar M.↗
DOE OSTI2023

Novel method for the identification of the production flavor of neutral charmed mesons

We propose a new algorithm for the identification of the production flavor of neutral D mesons in the Belle II experiment. The algorithm exploits the correlation between the flavor of a reconstructed neutral D meson (signal D meson) and the electric charges of particles reconstructed in the rest of the e + e – →$c\bar{c}$ event. These include those originating from the decay of the other charm hadron produced in the event, as well as those possibly produced in association with the signal D meson. We develop the algorithm using simulation and calibrate it in data using decay modes that identify the flavor of the decaying neutral D meson. We use a data sample of e + e – collisions, corresponding to 362 fb –1 of integrated luminosity, collected by Belle II at center-of-mass energies near the Υ(4S) mass. The effective tagging efficiency in data is (47.91±0.07 (stat) ±0.51 (syst)) %, independent of the neutral-D-meson decay mode. This charm flavor tagger will approximately double the effective sample size of many CP-violation and charm-mixing measurements that so far have exclusively relied on neutral D mesons originating from D* ± decays. While developed for Belle II, the basic principles underlying the charm flavor tagger can be used in other experiments, including those at hadron colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

Methods of evaluating the effects of coding on SAR data

It is recognized that mean square error (MSE) is not a sufficient criterion for determining the acceptability of an image reconstructed from data that has been compressed and decompressed using an encoding algorithm. In the case of Synthetic Aperture Radar (SAR) data, it is also deemed to be insufficient to display the reconstructed image (and perhaps error image) alongside the original and make a (subjective) judgment as to the quality of the reconstructed data. In this paper we suggest a number of additional evaluation criteria which we feel should be included as evaluation metrics in SAR data encoding experiments. These criteria have been specifically chosen to provide a means of ensuring that the important information in the SAR data is preserved. The paper also presents the results of an investigation into the effects of coding on SAR data fidelity when the coding is applied in (1) the signal data domain, and (2) the image domain. An analysis of the results highlights the shortcomings of the MSE criterion, and shows which of the suggested additional criterion have been found to be most important.

Dutkiewicz, Melanie↗
DOE OSTI2021

Jet energy scale and resolution measured in proton–proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36–81 fb -1 of proton–proton collision data with a centre-of-mass energy of √s=13 TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-k t jet algorithm with radius parameter R=0.4 is the primary jet definition used for both jet types. This result presents new jet energy scale and resolution measurements in the high pile-up conditions of late LHC Run 2 as well as a full calibration of particle-flow jets in ATLAS. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several in situ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets (|η|<1.2) vary from 1% for a wide range of high-p T jets (250 T <2000 GeV), to 5% at very low p T (20 GeV) and 3.5% at very high p T (>2.5 TeV). The relative jet energy resolution is measured and ranges from (24±1.5)% at 20 GeV to (6±0.5)% at 300 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

A new procedure for dynamic adaption of three-dimensional unstructured grids

A new procedure is presented for the simultaneous coarsening and refinement of three-dimensional unstructured tetrahedral meshes. This algorithm allows for localized grid adaption that is used to capture aerodynamic flow features such as vortices and shock waves in helicopter flowfield simulations. The mesh-adaption algorithm is implemented in the C programming language and uses a data structure consisting of a series of dynamically-allocated linked lists. These lists allow the mesh connectivity to be rapidly reconstructed when individual mesh points are added and/or deleted. The algorithm allows the mesh to change in an anisotropic manner in order to efficiently resolve directional flow features. The procedure has been successfully implemented on a single processor of a Cray Y-MP computer. Two sample cases are presented involving three-dimensional transonic flow. Computed results show good agreement with conventional structured-grid solutions for the Euler equations.

Biswas, Rupak↗
NASA NTRS1999

Modeling of polychromatic attenuation using computed tomography reconstructed images

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

NASA Discipline Musculoskeletal↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗
NASA NTRS1980

Data recording and processing for speckle image reconstruction

Image reconstruction from astronomical speckle data using the Knox-Thompson algorithm has now been extensively demonstrated as feasible through analysis and computer simulations. Experimental verification of the technique and its implementation for astronomy places stringent constraints on the recording system and requires complex processing algorithms to handle the many experimental details. In this paper we describe a video recording system specifically designed for the requirements of speckle imaging. This system has been built and tested, yielding high quality image reconstructions from a laboratory-atmospheric simulator for a wide range of input light levels and test objects.

Nisenson, P.↗
DOE OSTI2021

Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable‑density and shear flows

Abstract This work presents a novel numerical procedure for reconstructing volumetric density and velocity fields from planar laser-induced fluorescence (PLIF) and stereoscopic particle image velocimetry (SPIV) data. This new method is theoretically and practically demonstrated to provide more accurate 3D vortical structures and density fields in high shear flows than reconstruction methods based on the mean convective velocity. While Taylor’s hypothesis of frozen turbulence is commonly applied by using the local mean streamwise velocity, the proposed algorithm uses the measured local instantaneous velocity for data convection. It consists of a step-by-step reconstruction based on a mixed Lagrangian–Eulerian solver that includes the 3D interpolation of scattered flow data and that relaxes the Taylor’s hypothesis by iterative enforcement of the incompressibility constraint on the velocity field. This methodology provides 3D fields with temporal resolution, spatial resolution, and accuracy comparable to that of real 3D snapshots, thus providing a practical alternative to tomographic measurements. The procedure is validated using numerical data of the constant-density channel flow available on the Johns Hopkins University Turbulence Database (JHTDB), showing the accurate reconstruction of the 3D velocity field. The algorithm is applied to an experimental dataset of PLIF and SPIV measurements of a variable-density jet flow, demonstrating its capability to provide 3D velocity and density fields that are more consistent with the Navier–Stokes equations compared to the mean flow convective method. Graphic abstract

42 ENGINEERING↗