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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-IeP1-01: Bias and Computational Efficiency of Variance Reduction Methods for the Monte Carlo Simulation of Imaging Detectors

Purpose: Variance reduction techniques (VRTs) are employed in Monte Carlo simulations to obtain estimates with reduced statistical uncertainty for a given simulation time. In this work, we study the bias and efficiency of a VRT for estimating the response of imaging detectors. Methods: We implemented Directed Sampling (DS), preferentially directing a fraction of emitted optical photons directly towards the detector by altering the isotropic model. The weight of each optical photon is appropriately modified to maintain simulation estimates unbiased. We use a Monte Carlo tool called fastDETECT2 (part of the hybridMANTIS open-source package) for optical transport, modified for VRT. The weight of each photon is calculated as the ratio of original probability (no VRT) and the new probability for a particular direction. For our analysis of bias and efficiency, we use pulse height spectra, point response functions, and Swank factors. We obtain results for a variety of cases including analog (no VRT, isotropic distribution), and DS with 0.2 and 0.8 optical photons directed towards the sensor plane. We used 10,000, 25-keV primaries. Results: The Swank factor for all cases in our simplified model converged fast (within the first 100 primaries) to a stable value of 0.9. The root mean square error per pixel for DS VRT for the point response function between analog and VRT cases was approximately 5e-4. Conclusion: Our preliminary results suggest that DS VRT does not affect the estimate of the mean for the Swank factor. Our findings indicate that it may be possible to design VRTs for imaging detector simulations to increase computational efficiency without introducing bias.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Atomic Scale Studies of La/Sr Ordering in Colossal Magnetoresistant La 2-2x Sr 1+2x Mn 2 O 7 Single Crystals

To date, it is unclear whether chemical order (or disorder) is in any way connected to double exchange, electronic phase separation, or charge ordering (CO) in manganites. In this work, we carry out an atomic resolution study of the colossal magnetoresistant manganite La 2-2x Sr 1+2x Mn 2 O 7 (LSMO). We combine aberration-corrected electron microscopy and spectroscopy with spectroscopic image simulations, to analyze cation ordering at the atomic scale in real space in a number of LSMO single crystals. We compare three different compositions within the phase diagram: a ferromagnetic metallic material (x=0.36), an insulating, antiferromagnetic charge ordered (AF-CO) compound (x=0.5), which also exhibits orbital ordering, and an additional AF sample (x=0.56). Detailed image simulations are essential to accurately quantify the degree of chemical ordering of these samples. We find a significant degree of long-range chemical ordering in all cases, which increases in the AF-CO range. However, the degree of ordering is never complete nor can it explain the strongly correlated underlying ordering phenomena. Our results show that chemical ordering over distinct crystallographic sites is not needed for electronic ordering phenomena to appear in manganites, and cannot by itself explain the complex electronic behavior of LSMO.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detector Tilt Considerations in Bragg Coherent Diffraction Imaging: A Simulation Study

This paper addresses the three-dimensional signal distortion and image reconstruction issues in X-ray Bragg coherent diffraction imaging (BCDI) in the event of a general non-orthogonal orientation of the area detector with respect to the diffracted beam. Growing interest in novel BCDI adaptations at fourth-generation synchrotron light sources has necessitated improvisations in the experimental configuration and the subsequent data analysis. One such possibly unavoidable improvisation that is envisioned in this paper is a photon-counting area detector whose face is tilted away from the perpendicular to the Bragg-diffracted beam during the acquisition of the coherent diffraction signal. We describe a likely circumstance in which one would require such a detector configuration, along with the experimental precedent at third-generation synchrotrons. Using physically accurate diffraction simulations from synthetic scatterers in the presence of such tilted detectors, we analyze the general nature of the observed signal distortion qualitatively and quantitatively and provide a prescription to correct for it during image reconstruction. Our simulations and reconstructions are based on an adaptation of the known theory of BCDI sampling geometry, as well as the recently developed projection-based methods of wavefield propagation. Such configurational modifications and their numerical remedies are potentially valuable in realizing unconventional coherent diffraction measurement geometries, eventually paving the way for the integration of BCDI into new material characterization experiments at next-generation light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2025

OpenUniverse2024: a shared, simulated view of the sky for the next generation of cosmological surveys

The OpenUniverse2024 simulation suite is a cross-collaboration effort to produce matched simulated imaging for multiple surveys as they would observe a common simulated sky. Both the simulated data and associated tools used to produce it are intended to uniquely enable a wide range of studies to maximize the science potential of the next generation of cosmological surveys. We have produced simulated imaging for approximately 70 deg 2 of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Wide-Fast-Deep survey and the Nancy Grace Roman Space Telescope High-Latitude Wide-Area Survey, as well as overlapping versions of the ELAIS-S1 Deep-Drilling Field for LSST and the High-Latitude Time-Domain Survey for Roman. OpenUniverse2024 includes (i) an early version of the updated extragalactic model called Diffsky, which substantially improves the realism of optical and infrared photometry of objects, compared to previous versions of these models; (ii) updated transient models that extend through the wavelength range probed by Roman and Rubin; and (iii) improved survey, telescope, and instrument realism based on up-to-date survey plans and known properties of the instruments. It is built on a new and updated suite of simulation tools that improves the ease of consistently simulating multiple observatories viewing the same sky. The approximately 400 TB of synthetic survey imaging and simulated universe catalogs are publicly available, and we preview some scientific uses of the simulations.

large-scale structure of Universe↗
DOE OSTI2017

The redshift distribution of cosmological samples: a forward modeling approach

Determining the redshift distribution n ( z ) of galaxy samples is essential for several cosmological probes including weak lensing. For imaging surveys, this is usually done using photometric redshifts estimated on an object-by-object basis. We present a new approach for directly measuring the global n ( z ) of cosmological galaxy samples, including uncertainties, using forward modeling. Our method relies on image simulations produced using \textsc(UFig) (Ultra Fast Image Generator) and on ABC (Approximate Bayesian Computation) within the MCCL (Monte-Carlo Control Loops) framework. The galaxy population is modeled using parametric forms for the luminosity functions, spectral energy distributions, sizes and radial profiles of both blue and red galaxies. We apply exactly the same analysis to the real data and to the simulated images, which also include instrumental and observational effects. By adjusting the parameters of the simulations, we derive a set of acceptable models that are statistically consistent with the data. We then apply the same cuts to the simulations that were used to construct the target galaxy sample in the real data. The redshifts of the galaxies in the resulting simulated samples yield a set of n ( z ) distributions for the acceptable models. We demonstrate the method by determining n ( z ) for a cosmic shear like galaxy sample from the 4-band Subaru Suprime-Cam data in the COSMOS field. We also complement this imaging data with a spectroscopic calibration sample from the VVDS survey. We compare our resulting posterior n ( z ) distributions to the one derived from photometric redshifts estimated using 36 photometric bands in COSMOS and find good agreement. This offers good prospects for applying our approach to current and future large imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Fine precipitation scenarios of AlZnMg(Cu) alloys revealed by advanced atomic-resolution electron microscopy study Part I: Structure determination of the precipitates in AlZnMg(Cu) alloys

Although they are among the most important precipitation-hardened materials for industry applications, the high-strength AlZnMg(Cu) alloys have thus far not yet been understood adequately about their underlying precipitation scenarios in relation with the properties. This is partly due to the fact that the structures of a number of different precipitates involved in electron microscopy in association with quantitative image simulations have to be employed; a systematic study of these hardening precipitates in different alloys is also necessary. In Part I of the present study, it is shown that there are five types of structurally different precipitates including the equilibrium η-phase precipitate. Using two state-of-the-art atomic-resolution imaging techniques in electron microscopy in association with quantitative image simulations, we have determined and clarified all the unknown precipitate structures. It is demonstrated that atomic-resolution imaging can directly suggest approximate structure models, whereas quantitative image analysis can refine the structure details that are much smaller than the resolution of the microscope. This combination is crucially important for solving the difficult structure problems of the strengthening precipitates in AlZnMg(Cu) alloys. - Highlights: Part I: • We determine and verify all the key precipitate structures in AlMgZn(Cu) alloys. • We employ aberration-corrected scanning transmission electron microscopy (STEM). • We use aberration-corrected high-resolution TEM (HRTEM) for the investigations. • We obtain atomic-resolution images of the precipitates and model their structures. • We refine all precipitate structures with quantitative image simulation analysis. Part II: • The hardening precipitates in AlZnMg alloys shall be classified into two groups. • Two precipitation scenarios coexist in the alloys. • The precipitation behavior of such an alloy depends on the alloy's composition. • Very detailed phase/structure transformations among the precipitates are revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Potential of dual-energy subtraction for converting CT numbers to electron density based on a single linear relationship

Purpose: The conversion of the computed tomography (CT) number to electron density is one of the main processes that determine the accuracy of patient dose calculations in radiotherapy treatment planning. However, the CT number and electron density of tissues cannot be generally interrelated via a simple one-to-one correspondence because the CT number depends on the effective atomic number as well as the electron density. The purpose of this study is to present a simple conversion from the energy-subtracted CT number ({Delta}HU) by means of dual-energy CT (DECT) to the relative electron density ({rho}{sub e}) via a single linear relationship. Methods: The {Delta}HU-{rho}{sub e} conversion method was demonstrated by performing analytical DECT image simulations that were intended to imitate a second-generation dual-source CT (DSCT) scanner with an additional tin filtration for the high-kV tube. The {Delta}HU-{rho}{sub e} calibration line was obtained from the image simulation with a 33 cm-diameter electron density calibration phantom equipped with 16 inserts including polytetrafluoroethylene, polyvinyl chloride, and aluminum; the elemental compositions of these three inserts were quite different to those of body tissues. The {Delta}HU-{rho}{sub e} conversion method was also applied to previously published experimental CT data, which were measured using two different CT scanners, to validate the clinical feasibility of the present approach. In addition, the effect of object size on {rho}{sub e}-calibrated images was investigated by image simulations using a 25 cm-diameter virtual phantom for two different filtrations: with and without the tin filter for the high-kV tube. Results: The simulated {Delta}HU-{rho}{sub e} plot exhibited a predictable linear relationship over a wide range of {rho}{sub e} from 0.00 (air) to 2.35 (aluminum). Resultant values of the coefficient of determination, slope, and intercept of the linear function fitted to the data were close to those of the ideal case. The maximum difference between the ideal and simulated {rho}{sub e} values was -0.7%. The satisfactory linearity of {Delta}HU-{rho}{sub e} was also confirmed from analyses of the experimental CT data. In the experimental cases, the maximum difference between the nominal and simulated {rho}{sub e} values was found to be 2.5% after two outliers were excluded. When compared with the case without the tin filter, the {Delta}HU-{rho}{sub e} conversion performed with the tin filter yielded a lower dose and more reliable {rho}{sub e} values that were less affected by the object-size variation. Conclusions: The {Delta}HU-{rho}{sub e} calibration line with a simple one-to-one correspondence would facilitate the construction of a well-calibrated {rho}{sub e} image from acquired dual-kV images, and currently, second generation DSCT may be a feasible modality for the clinical use of the {Delta}HU-{rho}{sub e} conversion method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

A dose point kernel database using GATE Monte Carlo simulation toolkit for nuclear medicine applications: Comparison with other Monte Carlo codes

Purpose: GATE is a Monte Carlo simulation toolkit based on the Geant4 package, widely used for many medical physics applications, including SPECT and PET image simulation and more recently CT image simulation and patient dosimetry. The purpose of the current study was to calculate dose point kernels (DPKs) using GATE, compare them against reference data, and finally produce a complete dataset of the total DPKs for the most commonly used radionuclides in nuclear medicine. Methods: Patient-specific absorbed dose calculations can be carried out using Monte Carlo simulations. The latest version of GATE extends its applications to Radiotherapy and Dosimetry. Comparison of the proposed method for the generation of DPKs was performed for (a) monoenergetic electron sources, with energies ranging from 10 keV to 10 MeV, (b) beta emitting isotopes, e.g., {sup 177}Lu, {sup 90}Y, and {sup 32}P, and (c) gamma emitting isotopes, e.g., {sup 111}In, {sup 131}I, {sup 125}I, and {sup 99m}Tc. Point isotropic sources were simulated at the center of a sphere phantom, and the absorbed dose was stored in concentric spherical shells around the source. Evaluation was performed with already published studies for different Monte Carlo codes namely MCNP, EGS, FLUKA, ETRAN, GEPTS, and PENELOPE. A complete dataset of total DPKs was generated for water (equivalent to soft tissue), bone, and lung. This dataset takes into account all the major components of radiation interactions for the selected isotopes, including the absorbed dose from emitted electrons, photons, and all secondary particles generated from the electromagnetic interactions. Results: GATE comparison provided reliable results in all cases (monoenergetic electrons, beta emitting isotopes, and photon emitting isotopes). The observed differences between GATE and other codes are less than 10% and comparable to the discrepancies observed among other packages. The produced DPKs are in very good agreement with the already published data, which allowed us to produce a unique DPKs dataset using GATE. The dataset contains the total DPKs for {sup 67}Ga, {sup 68}Ga, {sup 90}Y, {sup 99m}Tc, {sup 111}In, {sup 123}I, {sup 124}I, {sup 125}I, {sup 131}I, {sup 153}Sm, {sup 177}Lu {sup 186}Re, and {sup 188}Re generated in water, bone, and lung. Conclusions: In this study, the authors have checked GATE's reliability for absorbed dose calculation when transporting different kind of particles, which indicates its robustness for dosimetry applications. A novel dataset of DPKs is provided, which can be applied in patient-specific dosimetry using analytical point kernel convolution algorithms.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

PICS: SIMULATIONS OF STRONG GRAVITATIONAL LENSING IN GALAXY CLUSTERS

Gravitational lensing has become one of the most powerful tools available for investigating the “dark side” of the universe. Cosmological strong gravitational lensing, in particular, probes the properties of the dense cores of dark matter halos over decades in mass and offers the opportunity to study the distant universe at flux levels and spatial resolutions otherwise unavailable. Studies of strongly lensed variable sources offer even further scientific opportunities. One of the challenges in realizing the potential of strong lensing is to understand the statistical context of both the individual systems that receive extensive follow-up study, as well as that of the larger samples of strong lenses that are now emerging from survey efforts. Motivated by these challenges, we have developed an image simulation pipeline, Pipeline for Images of Cosmological Strong lensing (PICS), to generate realistic strong gravitational lensing signals from group- and cluster-scale lenses. PICS uses a low-noise and unbiased density estimator based on (resampled) Delaunay Tessellations to calculate the density field; lensed images are produced by ray-tracing images of actual galaxies from deep Hubble Space Telescope observations. Other galaxies, similarly sampled, are added to fill in the light cone. The pipeline further adds cluster member galaxies and foreground stars into the lensed images. The entire image ensemble is then observed using a realistic point-spread function that includes appropriate detector artifacts for bright stars. Noise is further added, including such non-Gaussian elements as noise window-paning from mosaiced observations, residual bad pixels, and cosmic rays. The aim is to produce simulated images that appear identical—to the eye (expert or otherwise)—to real observations in various imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Microscopic Understanding of Fischer-Tropsch Synthesis on Ruthenium

Total energy calculations and scanning tunneling microscope (STM) image simulations were conducted in an effort to interpret new experimental images of CO and H adsorbed on the closepacked surface of ruthenium metal. The images are remarkable in suggesting that the adsorbed species are intermixed, plausibly accounting for the superior catalytic activity of this metal in forming hydrocarbons. Insight was gained over the short duration of the project, but a more accurate method of simulating images will be required before contact between theory and experiment points to a final result.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Lateral Diffusion Estimation in Fully Depleted Thick CCD Using Flat Field Image Analysis

In thick fully depleted CCDs charge carrier transport from the back window to the gates is accompanied by charge diffusion. Lateral diffusion smooths out density variations of the incoming photon flux by redistributing charges spatially. This creates short range positive correlations in recorded amplitudes. Pixel-to-pixel amplitude variations can also be caused by pixel size and quantum efficiency variations. Pixel size variations result in short range negative correlations. Our study shows that the characteristic diffusion width can be extracted from flat field data. The study was performed on fully depleted, thick CCDs produced in a technology study for the Large Synoptic Survey Telescope (LSST). Data were taken in the laboratory at bias voltages between -5 and -40 V. To increase statistical accuracy, images taken in identical conditions were co-added after base line subtraction and master files were produced. A flat field image simulator was developed for statistical comparison of simulated and measured images. Results on diffusion, pixel response variations, data features, analysis and modeling techniques are presented and discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Using Verified Lifting to Optimize Legacy Stencil Codes (Final Project Report)

This project investigated new techniques for compiling stencil and stencil-like computations. Stencils computations are commonly found in applications such as image processing, physical simulations, image processing, and machine learning. In recent years, many high-performance domain-specific languages (DSLs) have been proposed to optimize stencil computations. To leverage such DSLs, however, existing codes often need to be rewritten. Such rewriting is manual, labor intensive, and error-prone. To alleviate such issues, this project investigated the application of program synthesis and artificial learning techniques to enable stencil computations to automatically leverage new high-performance DSLs. Rather than constructing syntax driven rules, verified lifting uses program synthesis to search for a target code fragment to compile the given input code into. In addition, it also searches for a proof that validates how the found target code fragment preserves the semantics of the original input. Thus, the target code fragment is guaranteed to be semantically equivalent to the input.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Imaging Enhanced Simulation Validation for Directed Energy Deposition Additive Manufacturing (Final CRADA Report – NFE-22-09107)

Directed Energy Deposition (DED) is a welding-based metal Additive Manufacturing (AM) process that relies on the programmed rastering of an electric arc or laser induced weld pool to construct a component in a layerwise fashion. The induced complex thermal field and uneven thermal contraction depends on the printed geometry and scan pattern, and as such, accumulated residual stresses and distortions are complex and difficult to predict. Several prior works have resulted in tools to combat this issue; ANSYS has developed a thermoplastic simulation package targeting DED AM, and ORNL has developed an in-situ imaging sensor package ‘Stereo Correlated Optical and Pyrometric System’ (SCOPS) that can spatially monitor temperature and full field strain. In this CRADA, these tools are compared in order to validate the results and complimentarily address the weaknesses in each other.

36 MATERIALS SCIENCE↗