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At least 937 records · Page 52

An a priori Analysis of Mixture Fraction-Based Modeling of Coal Combustion

Due to the high computational cost associated with using detailed kinetic mechanisms, it is common practice to implement reaction models which rely on relatively small number of variables when simulating turbulent combustion systems. In this study, we perform an a priori assessment of the accuracy of three chemistry models: a Burke-Schumann chemistry model, an equilibrium chemistry model, and a steady laminar flamelet model. All chemistry modeling approaches considered include a mixture fraction for devolatilization products, and a normalized enthalpy deficit as parameters, with scalar dissipation rate as an additional input parameter for the flamelet model. Data used as a basis for the study is generated through One-Dimensional Turbulence (ODT) simulation of a coal combustion system in which high-fidelity models are used for gas phase chemistry and devolatilization. The flamelet model was found to be more accurate than both the equilibrium and Burke-Schumann models over a majority of parameter space. The performance of all models considered declined substantially for sufficiently fuel-rich conditions. Furthermore, the analysis in this work indicate that both differential diffusion and speciation of volatiles have a perceptible negative impact on flamelet reconstructions, though the effect of differential diffusion was more substantial.

97 MATHEMATICS AND COMPUTING↗

Data-driven global ocean modeling for seasonal to decadal prediction

Accurate modeling of ocean dynamics is crucial for enhancing our understanding of complex ocean circulation processes, predicting climate variability, and tackling challenges posed by climate change. Although great efforts have been made to improve traditional numerical models, predicting global ocean variability over multiyear scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), a data-driven three-dimensional ocean model for seasonal to decadal prediction of global ocean dynamics. ORCA-DL accurately simulates the three-dimensional structure of global ocean dynamics with high physical consistency and outperforms state-of-the-art numerical models in capturing extreme events, including El Niño–Southern Oscillation and upper ocean heat waves. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections. Our results demonstrate the high potential of data-driven models for providing efficient and accurate global ocean modeling and prediction.

Science & Technology - Other Topics↗

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗

Validation of 3-D Ice Accretion Measurement Methodology for Experimental Aerodynamic Simulation

Determining the adverse aerodynamic effects due to ice accretion often relies on dry-air wind-tunnel testing of artificial, or simulated, ice shapes. Recent developments in ice-accretion documentation methods have yielded a laser-scanning capability that can measure highly three-dimensional (3-D) features of ice accreted in icing wind tunnels. The objective of this paper was to evaluate the aerodynamic accuracy of ice-accretion simulations generated from laser-scan data. Ice-accretion tests were conducted in the NASA Icing Research Tunnel using an 18-in. chord, two-dimensional (2-D) straight wing with NACA 23012 airfoil section. For six ice-accretion cases, a 3-D laser scan was performed to document the ice geometry prior to the molding process. Aerodynamic performance testing was conducted at the University of Illinois low-speed wind tunnel at a Reynolds number of 1.8 × 10(exp 6) and a Mach number of 0.18 with an 18-in. chord NACA 23012 airfoil model that was designed to accommodate the artificial ice shapes. The ice-accretion molds were used to fabricate one set of artificial ice shapes from polyurethane castings. The laser-scan data were used to fabricate another set of artificial ice shapes using rapid prototype manufacturing such as stereolithography. The iced-airfoil results with both sets of artificial ice shapes were compared to evaluate the aerodynamic simulation accuracy of the laser-scan data. For five of the six ice-accretion cases, there was excellent agreement in the iced-airfoil aerodynamic performance between the casting and laser-scan based simulations. For example, typical differences in iced-airfoil maximum lift coefficient were less than 3 percent with corresponding differences in stall angle of approximately 1 deg or less. The aerodynamic simulation accuracy reported in this paper has demonstrated the combined accuracy of the laser-scan and rapid-prototype manufacturing approach to simulating ice accretion for a NACA 23012 airfoil. For several of the ice-accretion cases tested, the aerodynamics is known to depend upon the small, three-dimensional features of the ice. These data show that the laser-scan and rapid-prototype manufacturing approach is capable of replicating these ice features within the reported accuracies of the laser-scan measurement and rapid-prototyping method; thus providing a new capability for high-fidelity ice-accretion documentation and artificial ice-shape fabrication for icing research.

Aircraft Icing↗

Exploiting Colorimetry for Fidelity in Data Visualization

Advances in multimodal characterization methods fuel the generation of increasing immense hyper-dimensional data sets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. In this work, we review core concepts underlying the design of perceptually uniform color maps and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. Additionally, we develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.

36 MATERIALS SCIENCE↗

A range-resolved bistatic lidar using a high-sensitive CCD-camera

Until now monostatic type lidar systems have been mainly utilized in the field of lidar measurements of the atmosphere. We propose here a range-resolved bistatic lidar system using a high-sensitive cooled charge coupled device (CCD) camera. This system has the ability to measure the three dimensional distributions of aerosol, atmospheric density, and cloud by processing the image data of the laser beam trajectory obtained by a CCD camera. Also, this lidar system has a feature that allows dual utilization of continuous wave (CW) lasers and pulse lasers. The scheme of measurement with this bistatic lidar is shown. A laser beam is emitted vertically and the image of its trajectory is taken with a remote high-sensitive CCD detector using an interference filter and a camera lens. The specifications of the bistatic lidar system used in the experiments are shown. The preliminary experimental results of our range-resolved bistatic lidar system suggest potential applications in the field of lidar measurements of the atmosphere.

Yamaguchi, K.↗

Stochastic analysis of surface metrology

The design and evaluation of the expected performance of optical systems require sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. Modern x-ray source facilities are reliant upon the availability of optics with unprecedented quality (surface slope accuracy <0.1 μrad). The problem is especially complex in the case of x-ray optics, particularly for the X-ray Surveyor under development and other missions. The high angular resolution and throughput of future x-ray space observatories requires hundreds of square meters of high-quality optics. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. Here, we discuss improvements in metrology efficacy via comprehensive statistical analysis of a compact volume of metrology data. The data are considered stochastic, and a statistical model called invertible time-invariant linear filter (InTILF) is developed now for two-dimensional (2-D) surface profiles to provide compact description of the 2-D data in addition to one-dimensional data treated so far. The InTILF model captures stochastic patterns in the data and can be used as a quality metric and feedback to polishing processes, avoiding high-resolution metrology measurements over the entire optical surface. The modeling, implemented in our BeatMark™ software, allows simulating metrology data for optics made by the same vendor and technology. The data are vital for reliable specification for optical fabrication, to be exactly adequate for the required system performance.

42 ENGINEERING↗

Accelerated version of NUBEAM capabilities in DIII-D using neural networks

A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. Furthermore, this makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Density Functional Theory Study of Epitaxially Strained Monolayer Transition Metal Chalcogenides for Piezoelectricity Generation

Two-dimensional transition metal chalcogenides (2D TMCs) are known for their wide range of bandgaps, flexibility, and high strength. Recent synthesis and data mining efforts indicate that 56 2D TMCs have low exfoliation energies and are relatively stable in monolayer form. Under epitaxial strain, we predict using density functional theory (DFT) calculations that the majority of these 2D TMCs can accommodate ±10% strain without breaking their crystal symmetry. The elastic and piezoelectric tensors indicate that 22 of 56 candidates are piezoelectric, and we derive their in-plane piezoelectric coefficient d 11 . The epitaxial strain is further predicted to enhance the d 11 by over 100% at 10% tensile epitaxial strain for most of these piezoelectric 2D TMCs. ReSe 2 at pristine state and Au 2 Se 2 at +5% epitaxial strain are predicted to obtain the extreme d 11 coefficients at -120 and 326 pm/V, respectively. Lastly, these findings have implications for the use of high-performance 2D piezoelectric materials in devices.

2D materials↗

Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies

Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this data set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. Lastly, we use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.

36 MATERIALS SCIENCE↗

Observation of spontaneous ferromagnetism in a two-dimensional electron system

Significance Ground states of low-disorder, interacting, dilute electron systems are of fundamental interest in understanding electron–electron interaction phenomena. Our experimental data on a clean two-dimensional electron system reveal a sequence of transitions as the density is lowered. Starting from high densities, we observe a transition from a metallic state to an exotic insulating phase that is stabilized by disorder and interaction. As we further lower the density, we observe signs of a spontaneous transition to a ferromagnetic ground state and then to a strongly insulating state whose characteristics suggest that it is a pinned solid phase, possibly a (ferromagnetic) Wigner solid. We also find that the onset of ferromagnetism can be controlled by tuning the electrons’ valley degree of freedom.

2D electron system↗

Discriminative versus generative approaches to simulation-based inference

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.

high energy physics↗

Three-dimensional nanostructure determination from a large diffraction data set recorded using scanning electron nanodiffraction

A diffraction-based technique is developed for the determination of three-dimensional nanostructures. The technique employs high-resolution and low-dose scanning electron nanodiffraction (SEND) to acquire three-dimensional diffraction patterns, with the help of a special sample holder for large-angle rotation. Grains are identified in three-dimensional space based on crystal orientation and on reconstructed dark-field images from the recorded diffraction patterns. Application to a nanocrystalline TiN thin film shows that the three-dimensional morphology of columnar TiN grains of tens of nanometres in diameter can be reconstructed using an algebraic iterative algorithm under specified prior conditions, together with their crystallographic orientations. The principles can be extended to multiphase nanocrystalline materials as well. Thus, the tomographic SEND technique provides an effective and adaptive way of determining three-dimensional nanostructures.

36 MATERIALS SCIENCE↗

SU-E-J-121: Measuring Prompt Gamma Emission Profiles with a Multi-Stage Compton Camera During Proton Beam Irradiation: Initial Studies

Purpose: Recent studies have suggested that the characteristics of prompt gammas (PG) emitted during proton beam irradiation are advantageous for determining beam range during treatment delivery. The purpose of this work was to determine the feasibility of determining the proton beam range from PG data measured with a prototype Compton camera (CC) during proton beam irradiation. Methods: Using a prototype multi-stage CC the PG emission from a water phantom was measured during irradiation with clinical proton therapy beams. The measured PG emission data was used to reconstruct an image of the PG emission using a backprojection reconstruction algorithm. One dimensional (1D) profiles extracted from the PG images were compared to: 1) PG emission data measured at fixed depths using collimated high purity Germanium and Lanthanum Bromide detectors, and 2) the measured depth dose profiles of the proton beams. Results: Comparisons showed that the PG emission profiles reconstructed from CC measurements agreed very well with the measurements of PG emission as a function of depth made with the collimated detectors. The distal falloff of the measured PG profile was between 1 mm to 4 mm proximal to the distal edge of the Bragg peak for proton beam ranges from 4 cm to 16 cm in water. Doses of at least 5 Gy were needed for the CC to measure sufficient data to image the PG profile and localize the distal PG falloff. Conclusion: Initial tests of a prototype CC for imaging PG emission during proton beam irradiation indicated that measurement and reconstruction of the PG profile was possible. However, due to limitations of the operational parameters (energy range and count rate) of the current CC prototype, doses of greater than a typical treatment dose (∼2 Gy) were needed to measure adequate PG signal to reconstruct viable images. Funding support for this project provided by a grant from DoD.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Effective Padding of Multi-Dimensional Arrays to Avoid Cache Conflict Misses

Caches are used to significantly improve performance. Even with high degrees of set-associativity, the number of accessed data elements mapping to the same set in a cache can easily exceed the degree of associativity, causing conflict misses and lowered performance, even if the working set is much smaller than cache capacity. Array padding (increasing the size of array dimensions) is a well known optimization technique that can reduce conflict misses. In this paper, we develop the first algorithms for optimal padding of arrays for a set associative cache for arbitrary tile sizes, In addition, we develop the first solution to padding for nested tiles and multi-level caches. The techniques are in implemented in PAdvisor tool. Experimental results with multiple benchmarks demonstrate significant performance improvement from use of PAdvisor for padding.

array padding↗

Materials Data on GaAs by Materials Project

GaAs is High Pressure Cadmuum Telluride structured and crystallizes in the orthorhombic Pmm2 space group. The structure is three-dimensional. Ga3+ is bonded in a linear geometry to two equivalent As3- atoms. Both Ga–As bond lengths are 2.73 Å. As3- is bonded to two equivalent Ga3+ and four equivalent As3- atoms to form a mixture of edge and corner-sharing AsGa2As4 octahedra. The corner-sharing octahedra tilt angles range from 0–1°. There are two shorter (2.71 Å) and two longer (2.72 Å) As–As bond lengths.

36 MATERIALS SCIENCE↗

Materials Data on CdTe by Materials Project

CdTe is High Pressure Cadmuum Telluride structured and crystallizes in the orthorhombic Pmm2 space group. The structure is three-dimensional. Cd2+ is bonded in a linear geometry to two equivalent Te2- atoms. Both Cd–Te bond lengths are 3.19 Å. Te2- is bonded to two equivalent Cd2+ and four equivalent Te2- atoms to form a mixture of edge and corner-sharing TeCd2Te4 octahedra. The corner-sharing octahedra tilt angles range from 0–1°. There are two shorter (3.09 Å) and two longer (3.10 Å) Te–Te bond lengths.

36 MATERIALS SCIENCE↗