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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Probabilistic Data Association for Orbital-Element Estimation Using Multistage Expectation–Maximization

Tracking space objects is important for managing space traffic and predicting collisions, but is difficult in part due to data association and orbit model uncertainty. Expectation–maximization (EM) is a commonly used tracking method that has not been widely considered for tracking space objects. The technique consists of iteratively computing data association probabilities with a set of current element estimates, and updating estimates of the elements by solving a nonlinear weighted least-squares regression problem where the weights are the data association probabilities. In this paper, we demonstrate the use of EM for probabilistic data association and orbital-element estimation by applying the technique to simulated data from two angles-only tracking scenarios. In both scenarios, EM provides correct data associations and accurate maximum likelihood estimates of orbital elements. One scenario considers tracking a single object in clutter and quantifies the improvement of the orbital-element estimates and data associations as the detection probability increases. However, standard application of EM requires knowing the number of objects or may fail when a large number of objects are present. To address these issues, this paper employs a multistage version of EM that is applicable when there are a large and possibly unknown number of objects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,

Borowiec, Katarzyna↗
DOE OSTI2023

The DESI One-Percent Survey: exploring a generalized SHAM for multiple tracers with the UNIT simulation

We perform SubHalo Abundance Matching (SHAM) studies on UNIT simulations with {σ, V ceil , v smear }-SHAM and {σ, V ceil , $f$ sat }-SHAM. They are designed to reproduce the clustering on 5–30 h -1 Mpc of luminous red galaxies (LRGs), emission-line galaxies (ELGs), and quasi-stellar objects (QSOs) at 0.4 < z < 3.5 from DESI (Dark Energy Spectroscopic Instrument) One Percent Survey. V ceil is the incompleteness of the massive host (sub)haloes and is the key to the generalized SHAM. v smear models the clustering effect of redshift uncertainties, providing measurements consistent with those from repeat observations. A free satellite fraction $f$ sat is necessary to reproduce the clustering of ELGs. We find ELGs present a more complex galaxy–halo mass relation than LRGs reflected in their weak constraints on σ. LRGs, QSOs, and ELGs show increasing V ceil values, corresponding to the massive galaxy incompleteness of LRGs, the quenched star formation of ELGs and the quenched black hole accretion of QSOs. For LRGs, a Gaussian v smear presents a better profile for subsamples at redshift bins than a Lorentzian profile used for other tracers. The impact of the statistical redshift uncertainty on ELG clustering is negligible. The best-fitting satellite fraction for DESI ELGs is around 4 per cent, lower than previous estimations for ELGs. The mean halo mass log 10 ($\langle$M vir $\rangle$) in h -1 M ⊙ for LRGs, ELGs, and QSOs are 13.16 ± 0.01, 11.90 ± 0.06, and 12.66 ± 0.45, respectively. Our generalized SHAM algorithms facilitate the production of multitracer galaxy mocks for cosmological tests.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dynamical Complexity of Non-Gaussian Many-Body Systems with Dissipation

We characterize the dynamical state of many-body bosonic and fermionic many-body models with intersite Gaussian couplings, on-site non-Gaussian interactions, and local dissipation comprising incoherent particle loss, particle gain, and dephasing. We first establish that, for fermionic systems, if the dephasing noise is larger than the non-Gaussian interactions, irrespective of the Gaussian coupling strength, the system state is a convex combination of Gaussian states at all times. Furthermore, for bosonic systems, we show that if the particle loss and particle gain rates are larger than the Gaussian intersite couplings, the system remains in a separable state at all times. Building on this characterization, we establish that at noise rates above a threshold, there exists a classical algorithm that can efficiently sample from the system state of both the fermionic and bosonic models. Finally, we show that, unlike fermionic systems, bosonic systems can evolve into states that are not convex Gaussian even when the dissipation is much higher than the on-site non-Gaussianity. Similarly, unlike bosonic systems, fermionic systems can generate entanglement even with noise rates much larger than the intersite couplings.

Computational complexity↗
DOE OSTI2025

Validation of the DESI 2024 Lyα forest BAO analysis using synthetic datasets

The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-α (Lyα) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift z = 2.33. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Lyα forest BAO measurement presented in [1]. The synthetic data sets are based on Gaussian random fields using the log-normal approximation. We produce realistic synthetic DESI spectra that include all major contaminants affecting the Lyα forest. The synthetic data sets span a redshift range 1.8 < z < 3.8, and are analyzed using the same framework and pipeline used for the DESI 2024 Lyα forest BAO measurement. To measure BAO, we use both the Lyα auto-correlation and its cross-correlation with quasar positions. We use the mean of correlation functions from the set of DESI DR1 realizations to show that our model is able to recover unbiased measurements of the BAO position. We also fit each mock individually and study the population of BAO fits in order to validate BAO uncertainties and test our method for estimating the covariance matrix of the Lyα forest correlation functions. Finally, we discuss the implications of our results and identify the needs for the next generation of Lyα forest synthetic data sets, with the top priority being to simulate the effect of BAO broadening due to non-linear evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

Non-Linear Cosmological Power Spectra in Real and Redshift Space

We present an expression for the non-linear evolution of the cosmological power spectrum based on Lagrangian trajectories. This is simplified using the Zel'dovich approximation to trace particle displacements, assuming Gaussian initial conditions. The model is found to exhibit the transfer of power from large to small scales expected in self-gravitating fields. Some exact solutions are found for power-law initial spectra. We have extended this analysis into red-shift space and found a solution for the non-linear, anisotropic redshift-space power spectrum in the limit of plane-parallel redshift distortions. The quadrupole-to-monopole ratio is calculated for the case of power-law initial spectra. We find that the shape of this ratio depends on the shape of the initial spectrum, but when scaled to linear theory depends only weakly on the redshift-space distortion parameter, beta. The point of zero-crossing of the quadrupole, kappa(sub o), is found to obey a simple scaling relation and we calculate this scale in the Zel'dovich approximation. This model is found to be in good agreement with a series of N-body simulations on scales down to the zero-crossing of the quadrupole, although the wavenumber at zero-crossing is underestimated. These results are applied to the quadrupole-to-monopole ratio found in the merged QDOT plus 1.2-Jy-IRAS redshift survey. Using a likelihood technique we have estimated that the distortion parameter is constrained to be beta greater than 0.5 at the 95 percent level. Our results are fairly insensitive to the local primordial spectral slope, but the likelihood analysis suggests n = -2 un the translinear regime. The zero-crossing scale of the quadrupole is k(sub 0) = 0.5 +/- 0.1 h Mpc(exp -1) and from this we infer that the amplitude of clustering is sigma(sub 8) = 0.7 +/- 0.05. We suggest that the success of this model is due to non-linear redshift-space effects arising from infall on to caustic and is not dominated by virialized cluster cores. The latter should start to dominate on scales below the zero-crossing of the quadrupole, where our model breaks down.

Taylor, A. N.↗
DOE OSTI2025

Constraints on Local Primordial Non-Gaussianity with 3D Velocity Reconstruction from the Kinetic Sunyaev-Zeldovich Effect

The cosmic velocity field is an unbiased probe of the total matter distribution but is challenging to measure directly at intermediate and high redshifts. The large-scale velocity field imprints a signal in the cosmic microwave background (CMB) through the kinetic Sunyaev-Zeldovich (kSZ) effect. We perform the first 3D reconstruction of the large-scale velocity field from the kSZ effect by applying a quadratic estimator to CMB temperature maps and the 3D positions of galaxies. We do so by combining CMB data from the fifth data release of the Atacama Cosmology Telescope (in combination with Planck) and a spectroscopic galaxy sample from the Sloan Digital Sky Survey. We then measure the galaxy-velocity cross-power spectrum and detect the presence of the kSZ signal at a signal-to-noise ratio of 7.2⁢𝜎. Using this galaxy-velocity cross-correlation alone, we constrain the amplitude of local primordial non-Gaussianity finding 𝑓 NL =−9⁢0$^{+210}_{−350}$. In conclusion, this pathfinder measurement sets the stage for joint galaxy-CMB kSZ constraints to significantly enhance the 𝑓 NL information obtained from galaxy surveys through sample variance cancellation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Assessment of the Impact of Realistic Sensor Physics and the Integration of Ex-Core Sensors on Reactor Power Synthesis

In the work documented in this report, a weighting function–based core power synthesis method was applied to multiple Monte Carlo N-Particle (MCNP) reactor models, which are informed based on simulated self-powered neutron detector (SPND) responses. The weighting function method used has been coined the point-based iterative (PBI) method. The goal of this application is to assess the impact of considering realistic sensor physics in the generation of the simulated SPND outputs as well as to consider how the synthesis is impacted based on the inclusion of ex-core detectors in the model. The NuScale small modular reactor (SMR) and Westinghouse AP1000 pressurized water reactor (PWR) are the models that served as the testbeds for the assessment of realistic sensor physics; this was achieved by using Geant4 SPND models in comparison with analytical models, such that the effect of electron transport in realistic SPND geometries in the Geant4 model can be understood in terms of synthesis error and convergence time. The comparison was considered for fuel burnup–induced perturbations, for a range of sensor string densities and synthesized power distribution axial fidelities. The Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor MCNP model was used to assess the impact of ex-core sensors; this was done by performing synthesis with and without the ex-core detectors and by quantifying the synthesis error and number of iterations associated with Gaussian-type perturbations in many locations in the core. The TAMU TRIGA model was particularly pertinent for this study because of the interest in future experimental tests with SPNDs in this reactor, as well as the ease of modifying the MCNP model to include ex-core detectors with heterogeneously described response functions. Results from the comparison between the Geant4 and analytical SPND models indicate that similar average and maximum synthesis errors were obtained for burnup-induced perturbations in both the NuScale SMR and the AP1000. This was true for a range of sensor string densities and axial fidelities. However, there were marked differences between both the Geant4 and analytically informed models in terms of the iterations required to converge on the synthesized power distribution. Namely, the Geant4-informed models tended to lead to fewer iterations, except for a few sensor–core configurations that had particularly numerous iterations. Results from the ex-core sensor assessment with the TAMU TRIGA model indicate that the inclusion of ex-core sensors drastically reduces the synthesis error of Gaussian-type perturbations close to the edge of the core, and it slightly reduces synthesis errors for perturbations closer to the center of the core. This was achieved with a minimal increase in computational cost—that is, the number of iterations required for convergence. The errors were identified to be in the same location as the perturbation in the core, indicating that the methodology remains robust for unperturbed regions of the core. A secondary result from this study with the TAMU TRIGA was yielded by analysis of the neutron flux levels in the in-core and ex-core sensor locations of the core; these flux levels indicate that SPNDs could be used as both in-core and ex-core sensors, so long as the emitter material is sensitive to thermal neutrons. The results from these studies provide a quantitative understanding of the importance of considering realistic sensor physics and including ex-core sensors to perform accurate and timely power distribution synthesis of a reactor core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1992

Detection of x ray sources in PROS

The problem of detecting discrete sources in x-ray images has much in common with the problem of automatic source detection at other wavelengths. In all cases, one searches for positive brightness enhancements exceeding a certain threshold, which appear consistent with what one expects for a point source, in the presence of a (possibly) spatially variable background. Multidimensional point spread functions (e.g., dependent on detector position and photon energy) are also common. At the same time, the problem in x-ray astronomy has some unique aspects. For example, for typical x-ray exposures in current or recent observatories, the number of available pixels far exceeds the number of actual x-ray events, so Poisson, rather than Gaussian statistics apply. Further, extended cosmic x-ray sources are common, and one often desires to detect point sources in the vicinity or even within bright, diffuse x-ray emission. Finally, support structures in x-ray detectors often cast sharp shadows in x-ray images making it necessary to detect sources in a region of rapidly varying exposure. We have developed a source detection package within the IRAF/PROS environment which attempts to deal with some of the problems of x-ray source detection. We have patterned our package after the successful Einstein Observatory x-ray source detection programs. However, we have attempted to improve the flexibility and accessibility of the functions and to provide a graphical front-end for the user. Our philosophy has been to use standard IRAF tasks whenever possible for image manipulation and to separate general functions from mission-specific ones. We will report on the current status of the package and discuss future developments, including simulation tasks, to allow the user to assess detection efficiency and source significance, tasks to determine source intensity, and alternative detection algorithms.

Deponte, J.↗
NASA NTRSDate not supplied

Derivation of Effective Properties Based on Porous Scale Simulations Using Filtering Techniques

This study presents a method for derivation of effective properties at the interface and in-depth of porous materials. The method defines a Representative Elementary Volume (REV) and applies filtering techniques to computer effective properties such as porosity and flow quantities, such as velocity and pressure. The script, developed to process the data was tested on the VTK type files that contain the mesh information and the flow solution. The method allows to choose between two types of filters, such as cellular and top-hat and define the size of the REV and number of samples along the domain. Extraction of the REV from the domain is performed to exact boundaries requested for the user. This is done using a triangulation technique and cutting through the cells to comply to the requested boundaries of the volume. The method can be applied to both structured and unstructured meshes. Filtering the material porosity and flow quantities involves integration of the numerical data. The algorithm provides three integration methods, such as Riemann sum, Monte Carlo and Quadrature rule to perform the integration. The Monte-Carlo technique permits the use of either uniform or linearly spaced distribution of points. The Quadrature rule is currently applicable to tetrahedral element types. The Monte Carlo and Quadrature rule methods require interpolation of the flow quantities at the sample points. For interpolation, two methods were tested and are readily available, Gaussian interpolation and re-sampling. It has been shown that re-sampling method has better consistency and acceptable accuracy in interpolation of the data. The algorithm was written in Python language and uses a number of modules. The major module besides numpy is PyVista. It is used to process the computational domain, clip the REV and interpolate the data. Quadrature rule integration was performed using a quadpy module. ParaView software was used externally to convert the flow solution to the VTK (or more specifically VTU) format. Integration of ParaView in the same environment with PyVista encountered problems and could not be implemented in this work. The developed algorithm is expected to be applicable to unstructured meshes and more complex porous structures as soon as the data can be passed in VTK type format. With the report is provided Python script for filtering the solution and a Matlab script for simple generation and processing of 2-D and 3-D porous channel geometries. The two scripts don't communicate.

Alexsander Zibitsker↗
DOE OSTI2022

Nuclear data covariances are critical input to determine upper sub-critical limits and to design experiments to increase it [Slides]

This presentation discusses how Upper Subcritical Limits (USL) are key parameters to determine operational limits in nuclear criticality safety evaluations. It also discusses an example of plutonium casting operation using tantalum at LANL PF-4. The Whisper tool at Los Alamos relies on many inputs, including covariance data, leading the presentation to ask if an existing benchmark data be used in Whisper to adjust nuclear data and covariances to justify a higher USL. If not, Whisper can be used to help design an optimal new benchmark experiment. The presentation also seeks to determine what the possible impacts are on USL and operational limits for plutonium casting. In conclusion, nuclear data covariances are used for by Whisper for: GSSL adjustment of nuclear data and covariances, identification of most similar existing benchmark experiments to application, simulation of Upper Subcritical Limit of application, and input to optimization techniques for designing most appropriate new benchmark experiment(s) to meet requirements. This requires a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications). The presentation concludes by asking if end users should trust results that depend on current covariance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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 OSTI2022

A machine learning pipeline for membrane segmentation of cryo-electron tomograms

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify cell membrane structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging technique that tries to piece together disconnected components belonging to the same membrane structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here works well for extracting membrane surfaces in two real tomogram datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Entanglement structures in quantum field theories: Negativity cores and bound entanglement in the vacuum

Here, the many-body entanglement between two finite (size-d) disjoint vacuum regions of noninteracting lattice scalar field theory in one spatial dimension, i.e., a (d A × d B ) mixed Gaussian continuous variable system, is locally transformed into a tensor-product core of (1 A × 1 B ) mixed entangled pairs. Accessible entanglement within these core pairs exhibits an exponential hierarchy and as such identifies the structure of dominant region modes from which vacuum entanglement could be extracted into a spatially separated pair of quantum detectors. Beyond the core, the remaining modes of the halo are determined to be AB separable in isolation, as well as separable from the core. However, state preparation protocols that distribute entanglement in the form of (1 A × 1 B ) mixed core pairs are found to require additional entanglement in the halo that is obscured by classical correlations. This inaccessible (bound) halo entanglement is found to mirror the accessible entanglement, but with a step behavior as the continuum is approached. It remains possible that alternate initialization protocols that do not utilize the exponential hierarchy of core-pair entanglement may require less inaccessible entanglement. Entanglement consolidation is expected to persist in higher dimensions and may aid classical and quantum simulations of asymptotically free gauge field theories, such as quantum chromodynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Information content of higher order galaxy correlation functions

The shapes of galaxy N-point correlation functions can be used as standard rulers to constrain the distance–redshift relationship. The cosmological density fields traced by late-time galaxy formation are initially nearly Gaussian, and hence, all the cosmological information can be extracted from their two-point correlation function. Subsequent non-linear evolution under gravity, as well as halo and then galaxy formation, generates higher order correlation functions. Since the mapping of the initial to the final density field is, on large scales, invertible, it is often claimed that the information content of the initial field’s power spectrum is equal to that of all the higher order functions of the final, non-linear field. This claim implies that reconstruction of the initial density field from the non-linear field renders analysis of higher order correlation functions of the latter superfluous. We show that this claim is false when the N-point functions are used as standard rulers. Constraints available from joint analysis of the two and three-point correlation functions can, in some cases, exceed those offered by the initial power spectrum. We provide a mathematical justification for this claim and demonstrate it using a large suite of N-body simulations. In particular, we show that for the z = 0 real-space matter field in the limit of vanishing shot-noise, taking modes up to k max = 0.2 h Mpc −1 , using the bispectrum alone offers a factor of 2 reduction in the variance on the cosmic distance scale relative to that available from the linear power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2023

A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: Capturing jet-to-ambient density effects

A direct numerical simulation (DNS) campaign is deployed for a series of confined downward oriented, non-isothermal turbulent impinging jet configurations. A baseline Reynolds number of 9960 is obtained through a precursor DNS pipe flow simulation (Re τ = 505). Three jet temperature configurations (confinement height to nozzle diameter of three) enter a cylindrical domain that share ambient and impingement plate temperatures (298.15 K). The range of jet temperatures are crafted such that the ratio of inlet to ambient density varies from unity to 0.52, showcasing the effect of density disparity on flow characteristics such as core collapse, radial mixing of momentum and energy, near-wall stagnation behavior, wall-jet profiles, and large-scale vortical structures. Surface quantities provided include mean radial heat flux and wall-shear stress profiles, and heat flux histograms at select radial stations. Results showcase increased radial normal stresses for higher temperature jets that support increased mixing, resulting in large-scale recirculation structures that are smaller, while retaining similar normalized radial wall profiles for shear stress, heat flux and pressure. Furthermore, radial plots for wall shear stress and Nusselt number showcase strong radial decay as compared to previous configurations that share similar jet and ambient temperatures. For the 373.15 K case, a Gaussian-like histogram for heat fluxes at the impingement plate transitions to a log-normal profile as radial distances increase. In contrast, the 573.15 K configuration displays a bi-modal heat flux characteristic at the impingement plate, and in similar manner to the moderate temperature counterpart, transitions to a log-normal profile at larger radial distances.

33 ADVANCED PROPULSION SYSTEMS↗