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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 991 records · Page 55
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DOE OSTI2016

Consensus Statement on Proton Therapy in Early-Stage and Locally Advanced Non–Small Cell Lung Cancer

Radiation dose escalation has been shown to improve local control and survival in patients with non–small cell lung cancer in some studies, but randomized data have not supported this premise, possibly owing to adverse effects. Because of the physical characteristics of the Bragg peak, proton therapy (PT) delivers minimal exit dose distal to the target volume, resulting in better sparing of normal tissues in comparison to photon-based radiation therapy. This is particularly important for lung cancer given the proximity of the lung, heart, esophagus, major airways, large blood vessels, and spinal cord. However, PT is associated with more uncertainty because of the finite range of the proton beam and motion for thoracic cancers. PT is more costly than traditional photon therapy but may reduce side effects and toxicity-related hospitalization, which has its own associated cost. The cost of PT is decreasing over time because of reduced prices for the building, machine, maintenance, and overhead, as well as newer, shorter treatment programs. PT is improving rapidly as more research is performed particularly with the implementation of 4-dimensional computed tomography–based motion management and intensity modulated PT. Given these controversies, there is much debate in the oncology community about which patients with lung cancer benefit significantly from PT. The Particle Therapy Co-operative Group (PTCOG) Thoracic Subcommittee task group intends to address the issues of PT indications, advantages and limitations, cost-effectiveness, technology improvement, clinical trials, and future research directions. This consensus report can be used to guide clinical practice and indications for PT, insurance approval, and clinical or translational research directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Quantum Information Science in High Energy Physics at the Large Hadron Collider (Final Report-QuantISED)

We pursue scientific research at the interface of High Energy Physics and Quantum Information Science. This includes studies of thermal radiation and quantum entanglement in high-energy collisions at the Large Hadron Collider (LHC), with special emphasis on entanglement entropy and the Higgs boson. This project has also been extended to include quantum entanglement and charged current weak interactions using Fermilab results. And most recently, we have begun tests of the temporal entanglement using LHC data. Collider experiments such as proton-proton collisions at the LHC yield hadrons that exhibit an exponential behavior at low transverse momenta. This surprising behavior is seen in data from both the ATLAS and CMS collaborations. We attribute this phenomenon to quantum entanglement between the regions in the nucleon wave function. The exponential component to the transverse momentum distribution is a result of thermal radiation that is akin to Hawking or Unruh radiation that should exist at the event horizon of astrophysical black holes and neutron stars. The Principal Investigator, in collaboration with a theoretical physicist at Stony Brook University and Brookhaven National Laboratory, and with Yale University students, has shown evidence for this thermal radiation in several production and decay processes in the ATLAS and CMS data, and its connection to entanglement entropy (O.K. Baker and D.E Kharzeev, Phys. Rev. D 98, 054007 (2018)), including even the Higgs boson sector. Interestingly, this thermal behavior is also seen in momentum distributions of charged current weak interactions according to our studies. These findings suggest a deep connection between quantum entanglement (entanglement entropy) and thermalization in both hadron collisions at the energy frontier and neutrino scattering at the intensity frontier. We have confirmed the proposed relation between the effective temperature and the hard-scattering scale at lower energies using the most recent LHC data for the following systems: Higgs bosons, top quarks, and charged hadrons. Additionally, we have results for hadron production in neutrino scattering from nuclei using Fermilab weak interaction data. This study is carried out using data from the MINERvA collaboration. In those cases where entanglement is expected, there is an exponential component to the momentum distribution, while this component is absent in those processes where no entanglement is expected. This research thus tests the hypothesis about a link between quantum entanglement and thermalization in strong and weak interactions. See Phys Lett B 811, 135948 (2020). We also initiated research applying a quantum search algorithm (Grover's Algorithm) to LHC data. This quantum algorithm was used to show how rare events in LHC data can be searched for in large, unsorted databases, with quadratic speedup compared to classical search algorithms on classical computers. See "Application of a Quantum Search Algorithm to High- Energy Physics Data at the Large Hadron Collider", arXiv:2010.00649 [quant-ph].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Dynamics of atoms in strong laser fields I: A quasi analytical model in momentum space based on a Sturmian expansion of the interacting nonlocal Coulomb potential

In this study we present a model that we have formulated in the momentum space to describe atoms interacting with intense laser fields. As a further step, it follows our recent theoretical approach in which the kernel of the reciprocal-space time-dependent Schrödinger equation (TDSE) is replaced by a finite sum of separable potentials, each of them supporting one bound state of atomic hydrogen (Tetchou Nganso et al. 2013). The key point of the model is that the nonlocal interacting Coulomb potential is expanded in a Coulomb Sturmian basis set derived itself from a Sturmian representation of Bessel functions of the first kind in the position space. As a result, this decomposition allows a simple spectral treatment of the TDSE in the momentum space. In order to illustrate the credibility of the model, we have considered the test case of atomic hydrogen driven by a linearly polarized laser pulse, and have evaluated analytically matrix elements of the atomic Hamiltonian and dipole coupling interaction. For various regimes of the laser parameters used in computations our results are in very good agreement with data obtained from other time-dependent calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler’s exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the exact posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this article: when the likelihood has some local regularity, the number of model evaluations per Markov chain Monte Carlo (MCMC) step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ordinary differential equation (ODE) and partial differential equation (PDE) inference problems, with both synthetic and real data. Supplementary materials for this article are available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sequence2Self: Self-supervised image sequence denoising of pixel-level spray breakup morphology

Optical imaging of fast and transient phenomena such as the turbulent breakup of liquid sprays exhibit low signal-to-noise ratios due to the limited illumination intensity relative to the short exposure time. Image denoising is required to facilitate physical studies over these data but is challenging due to the absence of clean ground-truths and the stringency of the denoising task (e.g., strong and complex noise, limited resolution, preserving physical fidelity), preventing supervised and existing un-/self-supervised deep learning methods. To this end, Sequence2Self (Seq2S) is proposed, an extension of Self2Self (S2S) to image sequences that leverages both the signal’s spatial and temporal correlation. Seq2S is demonstrated on time-resolved x-ray phase contrast imaging of liquid jet fuel sprays in a gas turbine combustor, which possesses all of challenges detailed above. Experiments are conducted across four fuels with different breakup morphology using various state-of-the-art methods. Overall, many of the methods failed and Seq2S was most successful: (1) Accurate spray structures were reconstructed with consistent evolution across frames void of artifacts. (2) The performance was robust, invariant to the hyperparameter choice. (3) Computational time is short and can be made eligible for real-time denoising. In particular, the images denoised by Seq2S showed spray droplet diameter distributions with near-zero Kullback–Leibler divergence (0.01 ± 0.01) to a cleaner reference, whereas the second best method yielded 0.06 ± 0.03. In conclusion, this suggests that Seq2S can be reliably used prior to subsequent quantitative spray analyses as it retains (if not, improves) the statistical physical properties of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

New Particle Formation and Growth in the Houston Atmosphere During TRACER (Final Report)

From 2020-2025, researchers from UC Irvine, UC Riverside, and Colorado State University collaborated on a Department of Energy-funded project to understand how airborne particles form and grow in urban atmospheres, conducting an intensive field campaign in Houston, Texas during summer 2022. Using advanced instruments to measure gas-phase chemicals, particle composition, and a specialized chamber to study particle growth, the team discovered that sulfur-containing compounds from industrial and power plant emissions are the dominant driver of new particle formation in Houston, with particles typically forming locally in the city and growing as air moves away in the urban plume. The research revealed an important methodological insight: measurements from fixed ground stations can be misleading when interpreting how particles actually evolve as air masses move, which has significant implications for how scientists worldwide interpret atmospheric observations. These findings improve understanding of urban air quality and help reduce uncertainties in climate models, since these particles play critical roles in cloud formation and Earth's radiation balance, while also providing detailed information about ultrafine particle composition relevant to public health. The project trained three doctoral students, developed enhanced computer models for urban particle formation, and made all data publicly available through the DOE Atmospheric Radiation Measurement data archive for use by the broader scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis

Purpose: In radiotherapy based only on magnetic resonance imaging (MRI), knowledge about tissue electron densities must be derived from the MRI. This can be achieved by converting the MRI scan to the so-called pseudo-computed tomography (pCT). An obstacle is that the voxel intensities in conventional MRI scans are not uniquely related to electron density. The authors previously demonstrated that a patch-based method could produce accurate pCTs of the brain using conventional T{sub 1}-weighted MRI scans. The method was driven mainly by local patch similarities and relied on simple affine registrations between an atlas database of the co-registered MRI/CT scan pairs and the MRI scan to be converted. In this study, the authors investigate the applicability of the patch-based approach in the pelvis. This region is challenging for a method based on local similarities due to the greater inter-patient variation. The authors benchmark the method against a baseline pCT strategy where all voxels inside the body contour are assigned a water-equivalent bulk density. Furthermore, the authors implement a parallelized approximate patch search strategy to speed up the pCT generation time to a more clinically relevant level. Methods: The data consisted of CT and T{sub 1}-weighted MRI scans of 10 prostate patients. pCTs were generated using an approximate patch search algorithm in a leave-one-out fashion and compared with the CT using frequently described metrics such as the voxel-wise mean absolute error (MAE{sub vox}) and the deviation in water-equivalent path lengths. Furthermore, the dosimetric accuracy was tested for a volumetric modulated arc therapy plan using dose–volume histogram (DVH) point deviations and γ-index analysis. Results: The patch-based approach had an average MAE{sub vox} of 54 HU; median deviations of less than 0.4% in relevant DVH points and a γ-index pass rate of 0.97 using a 1%/1 mm criterion. The patch-based approach showed a significantly better performance than the baseline water pCT in almost all metrics. The approximate patch search strategy was 70x faster than a brute-force search, with an average prediction time of 20.8 min. Conclusions: The authors showed that a patch-based method based on affine registrations and T{sub 1}-weighted MRI could generate accurate pCTs of the pelvis. The main source of differences between pCT and CT was positional changes of air pockets and body outline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A method for volumetric imaging in radiotherapy using single x-ray projection

Purpose: It is an intriguing problem to generate an instantaneous volumetric image based on the corresponding x-ray projection. The purpose of this study is to develop a new method to achieve this goal via a sparse learning approach. Methods: To extract motion information hidden in projection images, the authors partitioned a projection image into small rectangular patches. The authors utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients was built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are then further used to generate a motion vector field and hence a volumetric image. The authors have also proposed an intensity baseline correction method based on the partitioned projection, in which the first and the second moments of pixel intensities at a patch in a simulated projection image are matched with those in a measured one via a linear transformation. The proposed method has been validated in both simulated data and real phantom data. Results: The algorithm is able to identify patches that contain relevant motion information such as the diaphragm region. It is found that an intensity baseline correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced the prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning was not used, and the 95th percentile error for the predicted motion vector was reduced from 2.40 to 0.92 mm. In the phantom case with a regular tumor motion, the predicted tumor trajectory was successfully reconstructed with a 0.82 mm error for tumor center localization compared to a 1.66 mm error without using the sparse learning method. When the tumor motion was driven by a real patient breathing signal with irregular periods and amplitudes, the average tumor center error was 0.6 mm. The algorithm robustness with respect to sparsity level, patch size, and presence or absence of diaphragm, as well as computation time, has also been studied. Conclusions: The authors have developed a new method that automatically identifies motion information from an x-ray projection, based on which a volumetric image is generated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Angle-dependent strong-field molecular ionization rates with tuned range-separated time-dependent density functional theory

Strong-field ionization and the resulting electronic dynamics are important for a range of processes such as high harmonic generation, photodamage, charge resonance enhanced ionization, and ionization-triggered charge migration. Modeling ionization dynamics in molecular systems from first-principles can be challenging due to the large spatial extent of the wavefunction which stresses the accuracy of basis sets, and the intense fields which require non-perturbative time-dependent electronic structure methods. In this paper, we develop a time-dependent density functional theory approach which uses a Gaussian-type orbital (GTO) basis set to capture strong-field ionization rates and dynamics in atoms and small molecules. This involves propagating the electronic density matrix in time with a time-dependent laser potential and a spatial non-Hermitian complex absorbing potential which is projected onto an atom-centered basis set to remove ionized charge from the simulation. For the density functional theory (DFT) functional we use a tuned range-separated functional LC-PBE*, which has the correct asymptotic 1/r form of the potential and a reduced delocalization error compared to traditional DFT functionals. Ionization rates are computed for hydrogen, molecular nitrogen, and iodoacetylene under various field frequencies, intensities, and polarizations (angle-dependent ionization), and the results are shown to quantitatively agree with time-dependent Schrödinger equation and strong-field approximation calculations. This tuned DFT with GTO method opens the door to predictive all-electron time-dependent density functional theory simulations of ionization and ionization-triggered dynamics in molecular systems using tuned range-separated hybrid functionals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

External beam planning module of Eclipse for external beam radiation therapy

Eclipse is a 3-dimensional (3D) treatment planning system for radiation therapy offered by Varian Medical Systems, Inc. The system has the network connectivity for the electronic transfer of image datasets and digital data communication among different equipment. The scope of this project for this special issue of Medical Dosimetry on 3D treatment planning systems is the assessment of planning tools in the external beam planning module of Eclipse to generate optimized treatment plans for patients undergoing external beam radiation therapy. This treatment planning system is relatively mature to be able to generate (1) simple treatment plans, (2) conformal radiation therapy plans, (3) static intensity-modulated radiation therapy (IMRT) plans, (4) volumetric-modulated arc therapy (VMAT) plans, and (5) treatment plans for electron beam therapy. The treatment planning tools are relatively plentiful to assist in the radiation therapy treatment planning. Some new features have been incorporated in the latest version and are helpful for making high-quality treatment plans. However, the location of the tools is not intuitive, and hence, familiarity with the user interface is essential to the efficient use of the treatment planning system. In addition, there are a number of dose algorithms available for the computation of dose distributions. The understanding of each dose computation algorithm is essential for the optimal use of this treatment planning system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

A robust approach to Gaussian process implementation

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Mukangango, Juliette↗
DOE OSTI2020

Climate-Related Trends of Within-Storm Intensities Using Dimensionless Temporal-Storm Distributions

Huff curves are probabilistic time distributions of rainfall expressed as dimensionless cumulative percentages of storm depth and duration. Previous studies have documented development factors, spatial robustness, and the utility of Huff curves in practical applications. However, the effects of trending rainfall on Huff curve intensity patterns have not yet been studied. As such, the goal of this paper is to fill this gap by studying Huff curve patterns in a watershed with demonstrated increasing trends of temperature and precipitation, with the intention that it can be generalized to other areas in the US and the world. To achieve this goal, the high temporal resolution precipitation data collected from a high spatial density, 72-year precipitation-gauge network on the 4.25-km 2 North Appalachian Experimental Watershed in east-central Ohio were used. Seasonal storm pattern trends from 1939 to 2010 were investigated using dimensionless depth (with the frequency of 50%, $d_{50}$) and the curve variability $(V = d_{80} - d_{20})$ at three dimensionless within-storms time periods (three verticals). The Spearman rank correlation procedure (correlation coefficient, ρ and significance probability, p) was used to statistically determine trends over time using 8 periods of 4-season sets of Huff curves over the 72 years. Two averages of ρ and p were computed: (1) by averaging the individual ρ and p obtained from 10 gauges (AvgI); and (2) by grouped averaging of all individual gauge values of $d_{50}$ and V and then computing ρ and p (AvgG). The test results of individual gauges showed that 4 cases for $d_{50}$ and 23 cases for V were significant for all seasons and verticals (total of 120 cases for each variable). The test results of AvgI for $d_{50}$ and V and AvgG for $d_{50}$ showed no significant trends in all seasons and verticals. Only the AvgG for V led to a significant trend for V in spring and fall at different times within storm patterns. The data do not provide sufficient evidence at the p=0.05 significance level to reject the null hypothesis of unchanging position of the dimensionless depth of the 50% Huff curves for individual or averages for all seasons and verticals. Also, there is insufficient evidence to reject the null hypothesis of unchanging variability, V, using the average ρ of individual gauges (AvgI for V) for all seasons and verticals. AvgG results showed a significant trend in V; however, this analysis may be affected by the nonindependence of storm data. The results suggest that it is likely that there is little if any effect of trending climate over approximately 70 years on Huff curve patterns. The results of this study add to the robustness characteristics of Huff curves and to their potential use in hydrological practice as design storms, as the foundation of stochastic storm generation, and for storm disaggregation, and they deserve further investigation. These different forms of inputs to watershed models have the potential to improve runoff estimation. The results suggest that, if verified in other studies, they have applicability to provide useful stationary precipitation patterns across the US and other areas of the world in areas of nonstationary climate. Also, individual rain gauge data may not be representative of trends even over small areas, and seasonal differences were noticeable as found in other studies. Recommendations are provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

A Mobile Automated Tomographic Gamma Scanning System - 13231

Canberra Industries have recently designed and built a new automated Tomographic Gamma Scanning (TGS) system for mobile deployment. The TGS technique combines high-resolution gamma spectroscopy with low spatial resolution 3-dimensional image reconstruction to provide increased accuracy over traditional approaches for the assay of non-uniform source distributions in low-to medium-density, non-heterogeneous matrices. Originally pioneered by R. Estep at Los Alamos National Laboratory (LANL), the TGS method has been further developed and commercialized by Canberra Industries in recent years. The present system advances the state of the art on several fronts: it is designed to be housed in a standard cargo transport container for ease of transport, allowing waste characterization at multiple facilities under the purview of a single operator. Conveyor feed, drum rotator, and detector and collimator positioning mechanisms operated by programmable logic control (PLC) allow automated batch mode operation. The variable geometry settings can accommodate a wide range of waste packaging, including but not limited to standard 220 liter drums, 380 liter overpack drums, and smaller 20 liter cans. A 20 mCi Eu-152 transmission source provides attenuation corrections for drum matrices up to 1 g/cm{sup 3} in TGS mode; the system can be operated in Segmented Gamma Scanning (SGS) mode to measure higher density drums. To support TGS assays at higher densities, the source shield is sufficient to house an alternate Co-60 transmission source of higher activity, up to 250 mCi. An automated shutter and attenuator assembly is provided for operating the system with a dual intensity transmission source. The system's 1500 kg capacity rotator turntable can handle heavy containers such as concrete lined 380 liter overpack drums. Finally, data acquisition utilizes Canberra's Broad Energy Germanium (BEGE) detector and Lynx MCA, with 32 k channels, providing better than 0.1 keV/channel resolution to support both isotopic analysis with the MGA/MGAU software and a wide 3 MeV dynamic range. The calibration and verification of the system is discussed, and quantitative results are presented for a variety of drum types and matrices. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Achievements and Ongoing Challenges for advanced CFD Boiling Closure Model Development using Physical Insights from Validation-Oriented High-Fidelity Boiling Experiment

The fundamental understanding of the boiling-associated heat transfer, including sliding bubble effect, is still challenging despite the intensive research for decades in both experimental and computational boiling research communities. One of the main difficulties of boiling experiment come from the fact that direct observation of the underlying principle through experiment is very difficult due to the high complexity of the boiling phenomenon. Also, since the heat transfer characteristics associated with boiling is the result of non-linear interaction of various physical parameters coupled to each other, it is challenging to find the relationship between the parameters for modeling. This paper discusses the authors’ past and ongoing boiling research under CASL (Consortium for Advanced Simulation of Light Water Reactors) program, including the efforts to overcome the difficulties of boiling measurement and model development. In particular, we focus on describing the major achievements, lessons learnt, and ongoing challenges for the CFD boiling closure model development based on the high-fidelity subcooled flow boiling experiment at Texas A&M University (TAMU). The major research achievements discussed through this paper include (i) novel boiling measurement strategy and high-fidelity validation data production, (ii) dedicated effort to identify the critical measurement issues in boiling experiment, (iii) new physical insight into sliding bubble heat transfer mechanisms, and (iv) new boiling closure model development. Discussion is also made on the ongoing challenges for the boiling closure model development and the future research plan based on the lessons learnt.

42 ENGINEERING↗
DOE OSTI2020

C HIMERA : A Massively Parallel Code for Core-collapse Supernova Simulations

Herein we provide a detailed description of the C HIMERA code, a code developed to model core collapse supernovae (CCSNe) in multiple spatial dimensions. The CCSN explosion mechanism remains the subject of intense research. Progress to date demonstrates that it involves a complex interplay of neutrino production, transport, and interaction in the stellar core, three-dimensional stellar core fluid dynamics and its associated instabilities, nuclear burning, and the fundamental physics of the neutrino–stellar core weak interactions and the equations of state of all stellar core constituents—particularly, the nuclear equation of state associated with core nucleons, both free and bound in nuclei. C HIMERA , by incorporating detailed neutrino transport, realistic neutrino–matter interactions, three-dimensional hydrodynamics, realistic nuclear, leptonic, and photonic equations of state, and a nuclear reaction network, along with other refinements, can be used to study the role of neutrino radiation, hydrodynamic instabilities, and a variety of input physics in the explosion mechanism itself. It can also be used to compute observables such as neutrino signatures, gravitational radiation, and the products of nucleosynthesis associated with CCSNe. The code contains modules for neutrino transport, multidimensional compressible hydrodynamics, nuclear reactions, a variety of neutrino interactions, equations of state, and modules to provide data for post-processing observables such as the products of nucleosynthesis, and gravitational radiation. C HIMERA is an evolving code, being updated periodically with improved input physics and numerical refinements. We detail here the current version of the code, from which future improvements will stem, which can in turn be described as needed in future publications.

79 ASTRONOMY AND ASTROPHYSICS↗