Distribution Of Coefficients Research Articles

Analyzing the effectiveness of quantum annealing with meta-learning

The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, but there is not yet a clear understanding of which are the characteristics of a problem that make it difficult to solve with QA. In this work, we propose a new methodology to study the effectiveness of QA based on meta-learning models. To do so, we first build a dataset composed of more than five thousand instances of ten different optimization problems. We define a set of more than a hundred features to describe their characteristics and solve them with both QA and three classical solvers. We publish this dataset online for future research. Then, we train multiple meta-models to predict whether QA would solve that instance effectively and use them to probe which features with the strongest impact on the effectiveness of QA. Our results indicate that it is possible to accurately predict the effectiveness of QA, validating our methodology. Furthermore, we observe that the distribution of the problem coefficients representing the bias and coupling terms is very informative in identifying the probability of finding good solutions, while the density of these coefficients alone is not enough. The methodology we propose allows to open new research directions to further our understanding of the effectiveness of QA, by probing specific dimensions or by developing new QUBO formulations that are better suited for the particular nature of QA. Furthermore, the proposed methodology is flexible and can be extended or used to study other quantum or classical solvers.

Applying Machine Learning Techniques: Uncertainty Quantification in Nonlinear Dynamics Characters Predictions via Gated Recurrent Unit-Based Reduced-Order Models

The development of reduced-order models has been a pivotal advancement in the computational analysis of fluid dynamics, substantially simplifying the complexity and boosting the efficiency of simulations. The accuracy and practicality of these models largely depend on the reduction techniques applied and the inherent characteristics of the fluid dynamics systems they represent. In this paper, we introduce an innovative machine-learning framework for assessing model uncertainty in computationally intensive reduced-order models. By combining subspace construction methods with advanced Bayesian inference techniques, our approach effectively captures the posterior distribution of model parameters, thereby providing an accurate representation of uncertainty in aerodynamic performance predictions. We employ the NACA0012 airfoil as a case study to validate our method’s ability to enhance the efficiency of reduced-order models and precisely measure the uncertainty inherent in predictions made by recurrent neural networks. It is important to note that our approach is influenced by specific constraints and variables that significantly impact the mean and variability of the predicted final lift coefficient distribution. Our findings indicate that setting the goodness of fit (R2) threshold above 0.985 markedly improves the correlation between Computational Fluid Dynamics (CFD) outcomes and model predictions, increasing from 72.2% to 97.9% as the interval amplification factor adjusts from 1.5 to 3. However, this adjustment causes a considerable expansion of the confidence interval, from 0.0737 to 0.1282, an increase of over 70%. Despite these challenges, our machine learning-based methodology provides essential insights into the further development of reduced-order modeling and uncertainty quantification in fluid dynamics. This highlights the need for ongoing research into the model parameters, especially in applications concerning aircraft control systems, to meet design specifications and ensure system reliability.

Numerical simulation of hydraulic characteristics and pollutant transport in the channels with different ice coverages

ABSTRACT In winter, rivers at high latitudes often freeze and an ice cover emerges at the free surface of the river. According to the coverage degree of the ice cover, rivers can be divided into free-flowing, partially frozen, and completely frozen. The presence of ice cover can greatly alter the hydraulic conditions of rivers, complicating the transport of pollutants in river channels. The distribution of pollutants in rivers with different ice coverages was simulated using the Detached Eddy Simulation (DES) model in this paper. The velocity distribution, the pollutant diffusion coefficient distribution, the turbulent kinetic energy distribution, and the vortex distribution with different ice coverages were analyzed. The velocity in the ice cover zone is at a lower rate than in the open flow zone. With the increase of the ice coverage, the turbulent kinetic energy of the ice cover area in partially frozen conditions gradually increases, which makes the water flow turbulence increase. The diffusion coefficient of pollutants in the ice cover area is greater than that in the open flow area, so pollutants disperse more quickly in the ice cover area. The turbulence energy and the diffusion coefficient have similar distribution characteristics beneath the ice cover. The presence of the ice cover promotes the movement of the vortex from the open flow area to the ice cover area, which may be the reason for the larger turbulence energy and the diffusion coefficient. The simulation results can provide a reference for ice disasters and water quality remediation.

Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels

Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression Cd = (a + b × U10) × 10−3. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution.

A wideband polarization diversified MIMO antenna with improved isolation using machine learning optimized metasurface absorber

This communication presents a model and optimization of a dual-sense circularly polarized (RHCP/LHCP) 2-port MIMO antenna with increased isolation. The antenna operates in a broad frequency range of 1.76–4.0 GHz and is loaded with a metasurface. It has dimensions of 98.5 × 39 mm2 and an edge-to-edge distance of 55.5 mm. The MIMO antenna is created by replicating a single unit and placing it in a mirrored configuration around the y-axis. The dimensions of the unit antenna are 40 × 39 × 0.8 mm3 and it is printed on a substrate made of FR4 with a thickness of 0.8 mm. The substrate has a relative permittivity (ε r) of 4.4 and a loss tangent (tanδ) of 0.02. The isolation between the ports may be enhanced by including a metasurface absorber, increasing from 12 dB to 23 dB. To verify the wideband and CP properties, we examine the MIMO antenna unit element for its reflection coefficient, peak gain (4.2 dB), radiation efficiency (about 90%), axial ratio, and surface current distribution. Machine learning is used to improve the design of metasurface unit cells to achieve optimum absorption. The Envelope correlation coefficient (ECC), Diversity gain (DG), and Channel capacity loss (CCL) of the proposed metasurface absorber-enabled antenna are simulated and calculated to prove its MIMO identity. The HFSS simulations are consistent with the experimental results obtained from the developed model.

Methods and algorithms of optimization in computer engineering: review and comparative analysis

Introduction: The main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Methods: One of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered. Results: Through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal.Conclusions: A comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented

Sums of coefficients of general L-functions over arithmetic progressions and applications

In this paper, we study the asymptotic distribution of coefficients of general L-functions over arithmetic progressions without the Ramanujan conjecture. As an application, we consider the high mean of Fourier coefficients of holomorphic forms or Maass forms for Γ=SL(2,Z) over arithmetic progressions, and improve the results of Jiang and Lü [10]. Our new results remove the restriction to prime module and improve the interval length of module q.

Local condensation heat transfer analysis of three-component mixture refrigerant R455A in plate heat exchanger

Plate heat exchangers are gaining popularity as condensers for their compact design and high efficiency. However, the uneven fluid distribution in channels poses a challenge to effective heat transfer. Existing literature lacks experimental analysis of local heat transfer coefficients for pure and zeotropic mixture refrigerants in plate heat exchangers. This study experimentally analyzed the local heat transfer of the three-component zeotropic mixture R455A at various conditions (mass flux 20, 30, and 50 kg m−2 s−1 and saturation temperatures 30 and 35 °C). Results show that the heat transfer coefficient increases with flow rates and decreases with saturation temperature. However, results indicated lower heat transfer coefficients compared to pure refrigerants due to mixture effects. In addition, the local distribution of the heat transfer coefficient revealed that the heat transfer coefficient is highest at the center compared to the edges of the channel due uneven distribution of refrigerants within the channel. This HTC pattern confirms an asymmetrical flow within the plate. A new correlation is being developed to predict the heat transfer coefficient of the ternary mixture, showing improved accuracy than previously proposed correlations.

The distribution of Fourier coefficients of symmetric square L-functions over arithmetic progressions

The distribution of Fourier coefficients of symmetric square L-functions over arithmetic progressions

A Preconditioner-Based Data-Driven Polynomial Expansion Method: Application to Compressor Blade With Leading Edge Uncertainty

Abstract In engineering practice, the amount of measured data is often scarce and limited, posing a challenge in uncertainty quantification (UQ) and propagation. Data-driven polynomial chaos (DDPC) is an effective way to tackle this challenge. However, the DDPC method faces problems from the lack of robustness and convergence difficulty. In this paper, a preconditioner-based data-driven polynomial chaos (PDDPC) method is developed to deal with UQ problems with scarce measured data. Two numerical experiments are used to validate the computational robustness, convergence property, and application potential in case of scarce data. Then, the PDDPC is first applied to evaluate the uncertain impacts of real leading edge (LE) errors on the aerodynamic performance of a two-dimensional compressor blade. Results show that the overall performance of compressor blade is degraded and there is a large performance dispersion at off-design incidence conditions. The actual blade performance has a high probability of deviating from the nominal performance. Under the influence of uncertain LE geometry, the probability distributions of the total pressure loss coefficient and static pressure ratio have obvious skewness characteristics. Compared with the PDDPC method, the UQ results obtained by the fitted Gaussian and Beta probability distributions seriously underestimate the performance dispersion of compressor blade. The mechanism analysis illustrates that the large flow variation around the leading edge is the main reason for the overall performance degradation and the fluctuations of the entire flow field.

A two‐stage combined filtration‐consolidation model for slurry ground treated by vacuum preloading

AbstractThe vacuum preloading technique is extensively employed for ground improvement, particularly for slurry ground characterized by high‐water content and low strength. Such ground frequently exhibits a delay in pore water pressure dissipation when treated with prefabricated vertical drains. To clarify the drainage and consolidation behaviour of high‐water content slurry ground under vacuum preloading, this study proposed a two‐stage combined model that integrates both filtration and consolidation processes. Initially, an axisymmetric filtration model was used to describe the formation of the soil column through the radial migration and compaction of the particles. The end‐of‐filtration radial distributions of void ratio, permeability coefficient, and effective pressure served as initial conditions for the consolidation stage analysis. This stage was depicted using a large strain consolidation model based on the free strain condition. The results showed the necessity of incorporating the filtration stage to capture the overall drainage mechanism and characteristics of slurry ground with vacuum preloading treatment.

Angle-of-attack characteristics of opposing jet for improving drag and heat reduction

While the opposing jet technique has the potential to achieve efficient drag and heat reduction, it can be severely affected by the incoming angle of attack. To analyze the angle-of-attack characteristics of opposing jet for improving drag and heat reduction, a three-dimensional blunt model was studied under various jet stagnation pressure ratios and angles of attack using the verified numerical method. The results showed that the enhanced reattachment shock on the windward side resulted in a higher pressure and temperature rise, which led to the deterioration of drag and heat reduction. Under the influence of the incoming angle of attack, the recirculation vortex transformed into a longitudinal vortex, resulting in a slanted U-shaped distribution of the surface pressure coefficient and Stanton number. Increasing the jet stagnation pressure ratio widened the coverage of the recirculation vortex on both the windward and leeward sides, which brought an improvement in drag and heat reduction. The interaction between the incoming angle of attack and the opposing jet caused a double-peak distribution of Stanton number due to the recirculation vortex reattachment and the compression of the incoming flow. The inclined opposing jet could reduce the peak values of pressure coefficient and Stanton number when subjected to the incoming flow with an angle of attack by spreading the recirculation vortex along the windward side. There should exist an optimal inclination angle that can effectively reduce the peak caused by the compression of the incoming flow without generating an excessive peak due to the recirculation vortex reattachment.

A Generative Model for Realistic Galaxy Cluster X-Ray Morphologies

The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.

Unsteady solute dispersion of electro-osmotic flow of micropolar fluid in a rectangular microchannel

This study scrutinizes the two-dimensional concentration distribution for a solute cloud containing a micropolar fluid in a rectangular microchannel under the influence of an applied electric field. The concentration distribution is obtained up to second order approximation using Mei's homogenization method. Analytical formulas are derived for dispersion coefficient, mean and two-dimensional concentration distributions. This study also includes the analytical expressions for electric potential, velocity, and microrotation profiles. This study discusses the impact of coupling number, couple stress parameter, electric double layer thickness, and Péclet number on solute concentration distribution. The results of fluid velocity and dispersion coefficient are validated with available works in the literature. The non-Newtonian parameter and electric double layer thickness are shown to have a significant impact on dispersion. Our study reveals that concentration distribution rises but spreading of solute reduces when the coupling number increases. This is also true when the Debye length decreases. It is also obtained that the solute spreads more in the Newtonian fluid case compared to the micropolar fluid case. Finally, coupling number and electric double layer thickness show a symmetric pattern to the indicator function for the transverse concentration variation rate. The findings of this work have broad implications in deoxyribonucleic acid analysis, chemical mixing, and separation.

Assessment of Look-up tables for the prediction of heat transfer coefficient distribution in rod bundles cooled by supercritical water

The evaluation of available Look-Up Tables for prediction of heat transfer coefficient distribution in rod bundles cooled by supercritical water with the aim of their further use in computational analyses of various Fuel Assembles of Supercritical Water-Cooled Reactors is made. The comparison between the calculations based on Look-Up Tables with the values from empirical correlations and experimental data for smooth and wire-wrapped rod bundles was presented. The obtained results showed that Look-Up Table of the University of Ottawa, which was created to describe improved and deteriorated heat transfer regimes in round tubes, allows describing available data points with 30 % of the mean square deviation. It is noted that the presence of wire intensifies heat transfer exchange near pseudocritical temperature region but existing versions of Look-Up Tables cannot take into account this effect. Nevertheless, there is potential for further improvement in predicting the heat transfer coefficient using Look-Up Table by introducing additional correction factors.

Infrared spectrum resolution enhancement model via Gabor transform regularization for object detection

In this paper, we proposed a novel resolution enhancement model for the unknown instrument function and infrared spectral signal estimation. The Gabor transform can be utilized on the degraded spectrum to save the proposed resolution enhancement model from converging the mistake spectral signal. In the proposed model, the spatial random noises are regularized by the total variation regularization, as well as the spectral local smoothness is reduced by the Gabor coefficient distribution regularization. Then, the split Bregman iteration scheme is introduced to alternately optimize the instrument function and latent spectrum, which shown excellent convergence and stability. After those steps, the high-resolution spectral signals can be produced by our proposed model. Comparing with the existing resolution enhancement algorithms, the proposed model can produce the comparable quality even if the observed spectrum exists the low contrast.

Investigation of Different Windshield Designs for Aerodynamic Drag Force in Motorcycle

This research investigates the aerodynamic performance of different windshielddesigns for the Honda CB500X motorcycleat 140 km/h, focusing on drag force, drag coefficient, and pressure distribution. Three aftermarket windshields and oneoriginal equipment manufacturer(OEM)windshieldwere developed in SolidWorks and simulated using computational fluid dynamics (CFD) in ANSYS Fluent. The OEM windshieldoutperformed the aftermarket designs in terms of aerodynamic efficiency, with a lower drag force (735.65991 N) and drag coefficient (0.3951). However, apressure distribution study found that aftermarket windshields provided better deflection of airflow, resulting in less direct wind effect on the rider, potentially improving rider comfort and safety.These findings highlight the trade-off between aerodynamic efficiency and rider comfort in windshield design.

Variation in Flow Characteristics and Power Performance Due to Axial Distance Optimization in the Design of Counter-Rotating Tidal Turbines

Counter-rotating turbines, designed to enhance the performance efficiency of tidal turbines, exhibit variable operational characteristics depending on the axial distance between the front and rear blades. This study encompassed both numerical analyses and performance experiments to establish the optimal design by examining the relationship between flow field alterations and the performance of a counter-rotating tidal turbine with varied axial distances. The blades of the counter-rotating tidal turbine, based on a 10-kW single turbine, were designed utilizing the Blade Element Momentum technique. The turbine blades were assessed for changes in output performance attributed to flow separation by analyzing the velocity distribution and separation points within the flow, demonstrating a maximum power coefficient of 40.3% at a design Tip Speed Ratio of 3. At y/D = 0.3. The counter-rotating tidal turbine achieved a maximum power coefficient of 47%, with performance enhancements of the rear blades driven by the accelerated wake of the front blades. Furthermore, the pressure coefficients of the blades, influenced by their shape, inflow velocity, and angle, were detailed separately for the suction and pressure sides. The study also explored the correlation between the flow characteristics and the output performance of each blade by analyzing the distribution of pressure coefficients.

Transport of reactive contaminant in a wetland flow with the effects of reversible and irreversible reactions on the bed surface

Wetlands are crucial for maintaining biodiversity in ecosystems. The reversible chemical reactions can significantly affect the pollutant reduction capacity in wetland flow. The spatial transport of contaminants in wetlands is further complicated by phase exchange kinetics due to reversible reactions in the wetland bed. This paper provides an analytical solution for studying the multi-dimensional concentration distribution of solutes in wetland flow, considering both reversible and irreversible boundary reactions. Unlike previous one-dimensional studies (Jiang et al. (2022), Zhan et al. (2024)), our approach offers a comprehensive view of wetland contaminant transport, accounting for complex interactions between different phases and environmental factors. The solute may undergo reversible sorptive phase exchange between the immobile (wetland bed phase) and the mobile phase (fluid phase). Multi-scale homogenization methods are used to determine the dispersion coefficient, mean concentration, longitudinal and vertical real concentration distributions, rate of variation of vertical concentration distribution under the influence of vegetation force, and reversible and irreversible reactions at a stationary wetland bed. The effects of retention parameter (θ), Damkohler number (Da), vegetation effect (α), dispersion time (τ), and first-order irreversible boundary reaction (β′) on solute mixing are thoroughly analyzed. The study's analytical results are validated by numerical results obtained using the finite difference method with a Shishkin mesh, and other comparisons are made with the limiting case of Taylor dispersivity. Results show that an increase in Damkohler number and vegetation force weakens contaminant transport in wetland flow, but an increase in Da enhances contaminant concentration in the mobile phase. The presence of reversible phase exchange kinetics causes non-uniform vertical concentration variation across the wetland cross-section. Critical values where the multi-scale homogenization technique may fail are suggested as θ≤0.5 and Da≥1. The study's findings have potential applications to pollution control in wetlands.

The impact of cationic/anionic ratio on the physicochemical aspects of catanionic niosomes as a promising carrier for anticancer drugs

BackgroundNiosomes are adaptable nanocarriers for the delivery and controlled release of anticancer drugs. The addition of ionic surfactants could easily functionalize these carriers. Catanionic niosomes that are comprised of cationic and anionic surfactants are effective drug-delivery vehicles. Here, we aim to evaluate the impact of the cationic/anionic ratio on the noisome characteristics. MethodsA novel approach utilizing molecular modeling was utilized to analyze some characteristics of catanionic niosomes, showing great potential as a vehicle for anticancer drugs. The nonionic surfactant SPAN60, along with cholesterol as the stabilizer, was utilized in the study. Moreover, CTAB and SDS were used as positively and negatively charged surfactants, respectively. An FDA-approved c-Met inhibitor, cabozantinib, was selected as the drug. Several significant parameters associated with the cabozantinib molecule and its properties within the bilayer like hydrogen bonding, total energy, radius of gyration (Rgyr), diffusion coefficient, and radial distribution function (RDF). ResultsThe findings suggest that the cabozantinib’s hydrogen bonding plays a vital role in the drug’s movement through the bilayer. The CTAB-rich niosomes showed a higher number of hydrogen bonds compared to the SDS-rich formulations. The diffusion coefficient was in agreement with the number of H-bonding, and this value increased from 3.12 × 10-12 (m2/s) in CTAB-rich niosome to 7.66 × 10-12 (m2/s) in SDS-rich niosime. ConclusionThe total binding energy between the drug and bilayer components can control drug release. The computational approach could sufficiently describe the influence of cationic/anionic molar ratios on the bilayer features, and the characteristics of catanionic niosomes could be easily adjusted for the controlled release of anticancer drugs.