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This presentation will discuss Ir for online diagnostics with acoustics; Ptot, Ttot, Pstat rake surveys for mean flow measurements; focused Schleiren for density and some turbulence structure; Laser sheet visualization for near-nozzle diagnostics; and Two-point hotwire measurements for turbulence models.
A numerical method to simulate high Reynolds number jet flows was formulated and applied to gain a better understanding of the flow physics. Large-eddy simulation was chosen as the most promising approach to model the turbulent structures due to its compromise between accuracy and computational expense. The filtered Navier-Stokes equations were developed including a total energy form of the energy equation. Subgrid scale models for the momentum and energy equations were adapted from compressible forms of Smagorinsky's original model. The effect of using disparate temporal and spatial accuracy in a numerical scheme was discovered through one-dimensional model problems and a new uniformly fourth-order accurate numerical method was developed. Results from two- and three-dimensional validation exercises show that the code accurately reproduces both viscous and inviscid flows. Numerous axisymmetric jet simulations were performed to investigate the effect of grid resolution, numerical scheme, exit boundary conditions and subgrid scale modeling on the solution and the results were used to guide the three-dimensional calculations. Three-dimensional calculations of a Mach 1.4 jet showed that this LES simulation accurately captures the physics of the turbulent flow. The agreement with experimental data was relatively good and is much better than results in the current literature. Turbulent intensities indicate that the turbulent structures at this level of modeling are not isotropic and this information could lend itself to the development of improved subgrid scale models for LES and turbulence models for RANS simulations. A two point correlation technique was used to quantify the turbulent structures. Two point space correlations were used to obtain a measure of the integral length scale, which proved to be approximately 1/2 D(sub j). Two point space-time correlations were used to obtain the convection velocity for the turbulent structures. This velocity ranged from 0.57 to 0.71 U(sub j).
An assessment of an acoustic analogy for the mixing noise component of jet noise in the presence of an infinite surface is presented. The reflection of jet noise by the ground changes the distribution of acoustic energy and is characterized by constructive and destructive interference patterns. The equivalent sources are modeled based on the two-point cross- correlation of the turbulent velocity fluctuations and a steady Reynolds-Averaged Navier-Stokes (RANS) solution. Propagation effects, due to reflection by the surface and refaction by the jet shear layer, are taken into account by calculating the vector Green's function of the linearized Euler equations (LEE). The vector Green's function of the LEE is written in relation to Lilley's equation; that is, approximated with matched asymptotic solutions and the Green's function of the convective Helmholtz equation. The Green's function of the convective Helmholtz equation for an infinite flat plane with impedance is the Weyl-van der Pol equation. Predictions are compared with an unheated Mach 0.95 jet produced by a nozzle with an exit diameter of 0.3302 meters. Microphones are placed at various heights and distances from the nozzle exit in the peak jet noise direction above an acoustically hard and an asphalt surface. The predictions are shown to accurately capture jet noise ground effects that are characterized by constructive and destructive interference patterns in the mid- and far-field and capture overall trends in the near-field.
Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-ΛCDM framework, incorporating the effects of redshift-space, Alcock–Paczynski distortions, and models of the halo–galaxy connection. Our models reach sub-percent level accuracy down to $1 \, h^{-1}\text{Mpc}$ and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ω cdm , σ 8 , and n s by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters.
The optical modeling of laser beam propagation between two points on the earth's surface is discussed with reference to a space relay experiment using relay mirrors on one or more satellites. A two-dimensional laser beam propagation code is applied, and several novel modules are developed to realistically model the environment of the experiment. These are the Kolmogorov atmosphere, a deformable mirror, a segmented aperture, and wind shear. The model allows the calculation of the beam-Strehl ratio and efficiency of the energy transfer for an arbitrary input beam distribution.
Abstract Standard halo occupation distribution (HOD) models predict how galaxies occupy halos based on a single property, the virial mass, M vir . To incorporate galaxy assembly bias, we consider alternative halo mass proxies to use in the HOD. A promising replacement is maximum circular velocity, V max , which measures the halo’s potential well, and successfully predicts galaxy clustering in subhalo abundance matching models. We fit galaxy clustering measurements from SDSS DR7 with two modified HODs besides the standard M vir -based model: one where V max is used as the halo property that determines galaxy occupation, and the other adopting an adjustable combination of M vir and V max . Except for the reduced volume M r < − 20 threshold sample fit with V max alone, neither change improves the model performance in predicting the projected two-point correlation function, w p . We conclude that switching the primary variable to V max does not significantly improve the HOD model’s ability to fit galaxy clustering.
A better estimation of surface reaction efficiency of semiconductor-grade graphite with atomic nitrogen, as well as the calibration error are calculated using Bayesian updating based on experimental data. Compared with a conventional deterministic model, the stochastic model approach is a powerful tool in the sense that the model is capable of taking into account underlying error correlations among the data quantities. In this paper, we investigate four different stochastic models (called “stochastic system model classes” herein) corresponding to different descriptions of modeling and measurement error structures, given one deterministic physical model. These stochastic system model classes differ in the covariance matrix structure that is used in the uncertainty model to represent uncertainties associated with the physical model and experimental measurements. For each model class, Bayesian inference is used to estimate the posterior probabilities of the physical model parameters as well as of the stochastic model parameters. Model comparison and selection are then applied based on two measures including Bayesian evidence and Bayesian information criterion, as well as the deviance information criterion. Both measures suggest the stochastic model class, which considers that a correlation between errors in two data quantities among different data points is the most plausible. With the stochastic model class, the range of uncertainty in surface reaction efficiency is estimated to be about two orders of magnitude at [Formula: see text].
The capability to utilize neural networks to predict macroscopic and microscopic cross section parametric spaces has been developed for the Griffin reactor physics application. The LibTorch interface enables Griffin's MOOSE-based materials to interact with LibTorch-trained models, allowing for the evaluation of complex macroscopic or microscopic cross section spaces, which are then used to evaluate the neutronic properties of the Griffin finite element model. This study benchmarks traditional ISOXML-formatted tabulation libraries against neural network-based models for 279 nuclides on 20,160 grid points for zero-dimensional and two-dimensional reactor models. Benchmark metrics include the fundamental mode eigenvalue, fission and absorption rates, and various temperature coefficients of reactivity (isothermal, fuel, and moderator). From the perspective of storage space, the complete set of LibTorch models uses 11 MB on disk, compared to the 10 GB for the ISOXML multigroup library that covers the same grid space. For the two-dimensional performance case considered in Griffin, the Torch model uses 97% less RAM than the reference ISOXML dataset while runtime increases by a factor of 3 when using the LibTorch model compared to the ISOXML dataset with multi-linear interpolation. The LibTorch model consistently yields errors within 0.01% for most analyzed quantities except for the temperature coefficients of reactivity where the maximum discrepancies are up to 0.3 $\frac{pcm}{K}$. Due to the neural network attempting to best predict quantities with no regard for a positive or negative bias for any given quantity, predictions may experience random fluctuations, resulting in both positive and negative errors. Future work will entail both depletion and coupled transient analysis to determine the predictive capabilities of Griffin with neural network-based cross sections.
The stress-strain behavior at room temperature and at 1100 C (2000 F) was measured for two carbon-fiber-reinforced silicon carbide (C/SiC) composite materials: a two-dimensional plain-weave quasi-isotropic laminate and a three-dimensional angle-interlock woven composite. Micromechanics-based material models were developed for predicting the response properties of these two materials. The micromechanics based material models were calibrated by correlating the predicted material property values with the measured values. Four-point beam bending sub-element specimens were fabricated with these two fiber architectures and four-point bending tests were performed at room temperature and at 1100 C. Displacements and strains were measured at various locations along the beam and recorded as a function of load magnitude. The calibrated material models were used in concert with a nonlinear finite element solution to simulate the structural response of these two materials in the four-point beam bending tests. The structural response predicted by the nonlinear analysis method compares favorably with the measured response for both materials and for both test temperatures. Results show that the material models scale up fairly well from coupon to subcomponent level.
Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.
Fabric roller shades are common shading materials used in commercial and residential buildings. Accurately characterizing and modeling shades helps practitioners select the appropriate product and its control strategy based on climate and occupants' priorities, such as visual comfort and view to outdoors. Previous studies established a generalized method for modeling complex fenestration systems using data-driven tabulated bidirectional scattering distribution functions. However, deploying such a method at scale to all fabric shading products on the market is too costly and time-consuming. Analytical models that are based on a limited set of measurements (e.g., normal-normal and normal-hemispherical visible transmittance and reflectance, and directional cut-off angles) can be used to model the wide variety of shading products on the market. This study evaluates the performance of two isotropic analytical models, Roos-Wienold and Modified-Kotey, for modeling fabric roller shades, with a focus on the model's ability to predict occupant visual comfort. The performance evaluation was conducted through laboratory and field measurements and simulations. The results showed that both models are sufficient for predicting vertical illuminance at seated eye-level. Roos-Wienold model was able to predict binary visual comfort classification (glare/no-glare) under a wide range of luminance conditions, while Modified-Kotey model did not perform as well under high-contrast low-adaptation conditions. Both models are insufficient in predicting visual comfort at a four-point scale (e.g., imperceptible, perceptible, disturbing, intolerable). The two isotropic models become less accurate when the fabric exhibits high anisotropy.
Measuring the growth rate of large-scale structures ( f ) as a function of redshift has the potential to break degeneracies between modified gravity and dark energy models, when combined with expansion-rate probes. Direct estimates of peculiar velocities of galaxies have attracted interest as a means of estimating fσ 8 . In particular, field-level methods can be used to fit the field nuisance parameter along with cosmological parameters simultaneously. This article aims to provide the community with a unified framework for the theoretical modeling of the likelihood-based field-level inference by performing fast field covariance calculations for velocity and density fields. Our purpose is to lay the foundations for a nonlinear extension of the likelihood-based method at the field level. We have developed a generalized framework, implemented in the dedicated software flip to perform a likelihood-based inference of fσ 8 . We derived a new field covariance model, which includes wide-angle corrections. We also included the models previously described in the literature inside our framework. We compared their performance against ours, and we validated our model by comparing it with the two-point statistics of a recent N-body simulation. The tests we performed have allowed us to validate our software and determine the appropriate wavenumber range to integrate our covariance model and its validity in terms of separation. Our framework allows for a wider wavenumber coverage to be used in our calculations than in previous works, which is particularly interesting for nonlinear model extensions. Finally, our generalized framework allows us to efficiently perform a survey geometry-dependent Fisher forecast of the fσ 8 parameter. We show that the Fisher forecast method we developed gives an error bar that is 30% closer to a full likelihood-based estimation than a standard volume Fisher forecast.
Analytical methods are developed to predict the pressure distribution and overall loads on the hulls of airships which have close coupled, relatively large and/or high disk loading propulsors for attitude control, station keeping, and partial support of total weight as well as provision of thrust in cruise. The methods comprise a surface-singularity, potential-flow model for the hull and lifting surfaces (such as tails) and a rotor model which calculates the velocity induced by the rotor and its wake at points adjacent to the wake. Use of these two models provides an inviscid pressure distribution on the hull with rotor interference. A boundary layer separation prediction method is used to locate separation on the hull, and a wake pressure is imposed on the separated region for purposes of calculating hull loads. Results of calculations are shown to illustrate various cases of rotor-hull interference and comparisons with small scale data are made to evaluate the method.
The thermal deformation of the critical point action of the 2D tricritical Ising model gives rise to an exact scattering theory with seven massive excitations based on the exceptional E_7 E 7 Lie algebra. The high and low temperature phases of this model are related by duality. This duality guarantees that the leading and sub-leading magnetisation operators, \sigma(x) σ ( x ) and \sigma'(x) σ ′ ( x ) , in either phase are accompanied by associated disorder operators, \mu(x) μ ( x ) and \mu'(x) μ ′ ( x ) . Working specifically in the high temperature phase, we write down the sets of bootstrap equations for these four operators. For \sigma(x) σ ( x ) and \sigma'(x) σ ′ ( x ) , the equations are identical in form and are parameterised by the values of the one-particle form factors of the two lightest \mathbb{Z}_2 ℤ 2 odd particles. Similarly, the equations for \mu(x) μ ( x ) and \mu'(x) μ ′ ( x ) have identical form and are parameterised by two elementary form factors. Using the clustering property, we show that these four sets of solutions are eventually not independent; instead, the parameters of the solutions for \sigma(x)/\sigma'(x) σ ( x ) / σ ′ ( x ) are fixed in terms of those for \mu(x)/\mu'(x) μ ( x ) / μ ′ ( x ) . We use the truncated conformal space approach to confirm numerically the derived expressions of the matrix elements as well as the validity of the \Delta Δ -sum rule as applied to the off-critical correlators. We employ the derived form factors of the order and disorder operators to compute the exact dynamical structure factors of the theory, a set of quantities with a rich spectroscopy which may be directly tested in future inelastic neutron or Raman scattering experiments.
Here, we consider the Bose–Hubbard model in two and three spatial dimensions and numerically compute the quantum circuit complexity of the ground state in the Mott insulator and superfluid phases using a mean field approximation with additional quadratic fluctuations. After mapping to a qubit system, the result is given by the complexity associated with a Bogoliubov transformation applied to the reference state taken to be the mean field ground state. In particular, the complexity has peaks at the O(2) critical points where the system can be described by a relativistic quantum field theory. Given that we use a Gaussian approximation, near criticality the numerical results agree with a free field theory calculation. To go beyond the Gaussian approximation we use general scaling arguments that imply that, as we approach the critical point t → t c , there is a non-analytic behavior in the complexity c 2 (t) of the form |c 2 (t) – c 2 (t c )| ~ |t – t c | νd , up to possible logarithmic corrections. Here d is the number of spatial dimensions and ν is the usual critical exponent for the correlation length ξ ~ |t – t c | –ν . As a check, for d = 2 this agrees with the numerical computation if we use the Gaussian critical exponent $v=\frac{1}{2}$. Finally, using AdS/CFT methods, we study higher dimensional examples and confirm this scaling argument with non-Gaussian exponent ν for strongly interacting theories that have a gravity dual.
In this work, we derive an exact renormalization group recursion relation for the Loschmidt amplitude of the quantum Q-state clock model and the quantum Q-state Potts model in one dimension. The renormalization group flow is discussed in detail. The fixed points of the renormalization group flow are found to be complex in general. These fixed points control the dynamical phases of the two models, giving rise to nonanalyticities in its Loschmidt rate function, for both the pure and the disordered system. For the quench protocols studied, dynamical quantum phase transitions are found to occur in the clock model for all Qs considered, while in the Potts model, they only occur when Q < 4 .
Cu-rich Pt–Cu bimetallic catalysts are among the most promising candidates for actively catalyzing the hydrodechlorination of 1,2-dichloroethane (1,2-DCA) toward ethylene production. Combining reaction kinetics experiments with density functional theory (DFT) calculations and mean-field microkinetic modeling, we present a systematic mechanistic study for 1,2-DCA hydrodechlorination on Cu-rich Pt–Cu alloy catalysts. Our DFT (PBE+(TS+SCS)) results suggest that increasing Cu content in the Pt–Cu alloy destabilizes C 2 -species adsorption while stabilizing the binding of atomic chlorine. The reaction energetics of all the elementary steps in the 1,2-DCA reaction network were calculated on a Pt 1 Cu 3 (111) model surface. The DFT results were then used to construct a microkinetic model, and the model-predicted reaction rates were compared with our reaction kinetics experimental results on a Cu-rich SiO 2 -supported Pt–Cu alloy catalyst through a parameter estimation procedure. Both the reaction kinetics experiments and the microkinetic model after parameter adjustments yielded 100% selectivity to ethylene. The microkinetic model pointed to a reaction pathway involving two sequential chlorine-removal steps on the Pt–Cu alloy catalyst, a mechanism distinct from the one previously identified on pure Pt/SiO 2 catalysts, which involved an initial hydrogen-removal step. Adjustments to the DFT-derived parameters indicate the possible formation of chlorine-induced Cu-enriched surface sites during 1,2-DCA hydrodechlorination conditions, sites that are more active than those encountered in the bulk Pt 1 Cu 3 (111) alloy surface. Furthermore, our study offers valuable initial insights on the 1,2-DCA hydrodechlorination reaction mechanism and the nature of the active sites on PtCu bimetallic catalysts.
To understand such an aerodynamic characteristic of the CMC blades, we numerically simulated three different CMC blades with varying trailing edge thicknesses. We utilized both Large Eddy simulation (LES) with the dynamic subgrid closure as well a recently implemented Reynolds-averaged Navier-Stokes (RANS) coupled with an intermittency function-based transition model. We considered two sets of experimental test points with different Reynolds numbers at a given high-free stream turbulence range (Tu=~10-13%). The LES was previously validated by comparing with experimental data. We validated the RANS transition model against the ERCOFTAC experimental zero-pressure gradient smooth flat plate boundary layer at low- and high freestream turbulence offered by the experimental data of the Stripf’s for a turbine blade. We confirmed that the predicted skin friction and the heat flux agree well with the data. Finally, we computed the predicted pressure loading profiles, total loss distributions, and integrated loss of the three different CMC blades and made comparisons against the experimental data acquired at NASA Glenn Transonic Turbine Blade Cascade Rig. Our paper will present the aforementioned results and discuss the ability of LES and RANS model of our choice to predict salient aerodynamic characteristics of the blades with the various trailing edge thickness effects.