Multiple Variable Parameters Research Articles

Deep-learning-assisted design of multi-degree-of-freedom metamaterial absorber

In recent years, the development of metamaterial absorbers (MAs) based on deep learning methods has become a popular research topic. Nevertheless, conventional analytical approaches have limitations, leading to MAs characterized by low-degree-of-freedom cell structures, thereby restricting their absorption characteristics. In this study, we propose a multi-degree-of-freedom MA with multiple variable parameters in terms of structures, materials, and number of layers, which are distinguished by coding. Additionally, deep learning models, including a fully connected neural network, recurrent neural network, AlexNet, and residual neural network, are designed to predict structures and absorption spectra. We compare the prediction accuracy of these four neural network models and identify the optimal network model for spectral prediction and on-demand design. Using trained neural network models, we successfully designed broadband, dual-band, and single-band MAs in on-demand design. Compared with other design methods, this method provides a greater degree of freedom in device design, which allows the design of MAs to have more absorption characteristics

Stability Region of Grid-Forming Wind Turbine with Variable Parameters Using Bialternate Sum Matrix Approach

Although the stability regions of wind turbines in the islanding mode have been widely researched, small-signal modeling of grid-forming wind turbines (GFWTs) in the islanding mode has yet to be explored. In addition, the state-space matrix of the wind turbine system has yet to be fully represented. Therefore, this paper proposes a small-signal modeling of GFWT and a method for identifying the stabilization region of a system with variable parameters. First, small-signal modeling of a GFWT based on virtual synchronous generator control is developed. Second, the bialternate sum matrix approach is used to determine the system stabilization region. The system matrix with multiple variable parameters is first decomposed into the sum of several matrices in this paper. Furthermore, the rotor-side generator control is simplified. It can reduce the dimensionality of the system matrix model. Finally, the simulation shows that the proposed method for determining the stabilization region of the variable system is accurate.

4D spectral-spatial computational photoacoustic dermoscopy

Photoacoustic dermoscopy (PAD) is an emerging non-invasive imaging technology aids in the diagnosis of dermatological conditions by obtaining optical absorption information of skin tissues. Despite advances in PAD, it remains unclear how to obtain quantitative accuracy of the reconstructed PAD images according to the optical and acoustic properties of multilayered skin, the wavelength and distribution of excitation light, and the detection performance of ultrasound transducers. In this work, a computing method of four-dimensional (4D) spectral-spatial imaging for PAD is developed to enable quantitative analysis and optimization of structural and functional imaging of skin. This method takes the optical and acoustic properties of heterogeneous skin tissues into account, which can be used to correct the optical field of excitation light, detectable ultrasonic field, and provide accurate single-spectrum analysis or multi-spectral imaging solutions of PAD for multilayered skin tissues. A series of experiments were performed, and simulation datasets obtained from the computational model were used to train neural networks to further improve the imaging quality of the PAD system. All the results demonstrated the method could contribute to the development and optimization of clinical PADs by datasets with multiple variable parameters, and provide clinical predictability of photoacoustic (PA) data for human skin.

SYSTEMATIC ANALYSIS OF MULTIVARIATE SCENARIOS USING ADVANCED CLUSTERING METHODS

The continuous improvement of fuel cycle simulators in conjunction with the increase of computing capacities have led to a new scale of scenario studies. Taking into consideration multiple variable parameters and observing their effect on multiple evaluation criteria, these scenario studies regroup several thousands of trajectories paving the different possible values for multiple operational parameters. If global methods like sensitivity analysis allow extracting useful information from these groups of trajectories, they only provide average and global values. In this work we present a new method to analyze these groups of trajectories while keeping some localization in the information. Based on principal component analysis, clustering method have been implemented in order to mathematically extract, from the ensemble of trajectories simulated for a scenario study, subgroups of trajectories that have similar behaviors. Typical trajectories, representative of these subgroups, are then determined. The application of this new method on a sample scenario for two different output, the total amount of transuranic elements within the fuel cycle and the number of time the MOX fuel could not be built during the simulated time, is presented. The comparison of the results between the two analyses shows that the method allows good clustering for continuous and regular outputs but struggle with discrete highly non-linear ones.

A Non-stationary Combined Ternary 5-point Subdivision Scheme with $C^{4}$ Continuity

In this paper, a family of non-stationary combined ternary $5$-point subdivision schemes with multiple variable parameters is proposed. The construction of the scheme is based on the generalized ternary subdivision scheme of order $4$, which is built upon refinement of a family of generalized B-splines, using the variable displacements. For such a non-stationary scheme, we study its smoothness and get that it can generate $C^{2}$ interpolating limit curves and $C^{4}$ approximating limit curves. Besides, we investigate the exponential polynomial generation/reproduction property and approximation order. It can generate/reproduce certain exponential polynomials with suitable choices of the variable parameters, and reach approximation order $5$.

Linear difference operator with multiple variable parameters and applications to second-order differential equations

In this article, we first investigate the linear difference operator (Ax)(t):=x(t)-sum_{i=1}^{n}c_{i}(t)x(t- delta _{i}(t)) in a continuous periodic function space. The existence condition and some properties of the inverse of the operator A are explicitly pointed out. Afterwards, as applications of properties of the operator A, we study the existence of periodic solutions for two kinds of second-order functional differential equations with this operator. One is a kind of second-order functional differential equation, by applications of Krasnoselskii’s fixed point theorem, some sufficient conditions for the existence of positive periodic solutions are established. Another one is a kind of second-order quasi-linear differential equation, we establish the existence of periodic solutions of this equation by an extension of Mawhin’s continuous theorem.

An Efficient Unified Approach for Performance Analysis of Functionally Graded Annular Fin with Multiple Variable Parameters

The present work investigates the performance of an annular fin of constant thickness, made up of a functionally graded material (FGM). The fin surfaces are exposed in an environment where the heat loss occurs through convection and radiation. The various parameters, describing the fin performances, are considered to be varying with the local temperature and fin radius. An efficient and unified approach, homotopy perturbation method (HPM), is applied for obtaining the closed form solution from the non-linear heat transfer equation of equilibrium. The effects of key thermal parameters, i.e. thermo-geometric, conduction— radiation, thermal conductivity variation, heat generation, exponent of heat transfer co-efficient and the parameter of surface emissivity are explored on the temperature field leading to an overall investigation of thermal performance. And, the effects of such parameters on heat transfer rate, efficiency, effectiveness and temperature distributions are found to be significant. The accuracy of the present closed form solution is measured when its results are verified with the results of FEM and FDM solutions. The present results agree very well with those of FDM results and FEM results which is obtained using COMSOL software.

A mechanism for electromagnetic trapping of extended objects

In this work a mechanism is proposed for levitation and stable confinement of a heavy rotating body using electromagnetic interactions alone, with no position-dependent control. Support of the weight and vertical confinement are achieved through an axisymmetric magnetic field while lateral confinement is accomplished by a rotating octupolar field via the Brouwer saddle mechanism. The orientational degrees of freedom are stabilized through gyroscopic action. The design features multiple variable parameters, thus allowing considerable flexibility for scaling the system size and maximizing the basin of attraction of the stable state.

Differences in bead-milling-induced hemolysis of red blood cells due to shape andsize of oscillating bead.

Red blood cell (RBC) susceptibility to hemolysis - or fragility - can be profiled by subjecting a sample to progressive durations of mechanical stress and measuring hemolysis upon each. The ability to control stress application with multiple variable parameters can be useful in various areas of research. Bead milling, by oscillating an object in a blood sample, can offer control of parameters including oscillation force and frequency. This work addresses the role of bead shape and size, for a given container, in potentially creating qualitatively as well as quantitatively different fluidic stresses in the sample. Identical, diluted RBC samples were stressed via bead milling using different beads, with other parameters the same. Resulting hemolysis was plotted for several time increments in each case. For a cylindrical bead oscillating at a given frequency and force, bead length was a determinant of albumin's protective effect on RBC, as reflected by mechanical fragility. Compared to a sphere of same diameter, the protective effect was absent with shorter cylinders, whereas for longer ones it appeared enhanced. Bead milling based RBC fragility testing could present a useful tool for creating, and studying effects of different shear stress types in inducing hemolysis.

Modelling effect of sericitization of plagioclase on the 40 K/ 40 Ar and 40 Ar/ 39 Ar chronometers: implication for dating basaltic rocks and mineral deposits

Abstract The 40 Ar/ 39 Ar technique is the most commonly used technique to date basaltic rocks. For basaltic rocks older than about 30 Ma, the dating of plagioclase separates is preferred over groundmass as the latter is susceptible to containing cryptic alteration due to fluid circulations, difficult if not impossible to remove during sample preparation. Alteration under such metamorphic conditions progressively forms K-rich sericite after plagioclase. Owing to its transparency, plagioclase allows a distinction to be made optically between partially–completely altered grains and fresh grains. However, practice shows that grains that contain less than about 1% of sericite are hard to identify under the stereomicroscope. Owing to the high K 2 O content ( c. 10 wt%) of sericite, such compromised grains can have dramatic effects on the age determination of plagioclase. Here, we investigate and quantify the effect of sericite on the 40 Ar/ 39 Ar age determination of plagioclase using a numerical model with multiple variable parameters. We show that the most influential parameter is the time difference between the crystallization of plagioclase and the sericitization event. We also show that for some continental flood basalts, even 0.1 wt% of sericite can bias the apparent age of a plagioclase separate by several hundred thousand years. The presence of sericite can be identified using a combination of Ca/K ratios, age spectra, and 39 Ar and 37 Ar degassing curves obtained during a conventional 40 Ar/ 39 Ar step-heating procedure. When the age of the fresh plagioclase and its Ca/K ratio are known, the percentage of sericitization and the age of the alteration event can be estimated. Ultimately, above approximately 65% of sericitization, the apparent age measured on the altered plagioclase is within ±1% of the age of the alteration event, with implications for accurately dating low-temperature metamorphism and mineral deposit formations. Supplementary material: Further details of calculation are available at www.geolsoc.org.uk/SUP18609 .

An overview of CFD modelling of small-scale fixed-bed biomass pellet boilers with preliminary results from a simplified approach

The increasing global energy demand and mounting pressures for CO2 mitigation call for increased efficient utilization of biomass, particularly for heating domestic and commercial buildings. The authors of the present paper are investigating the optimization of the combustion performance and NOx emissions of a 50kW biomass pellet boiler fabricated by a UK manufacturer. The boiler has a number of adjustable parameters including the ratio of air flow split between the primary and secondary supplies, the orientation, height, direction and number of the secondary inlets. The optimization of these parameters provides opportunities to improve both the combustion efficiency and NOx emissions.When used carefully in conjunction with experiments, Computational Fluid Dynamics (CFD) modelling is a useful tool for rapidly and at minimum cost examining the combustion performance and emissions from a boiler with multiple variable parameters. However, modelling combustion and emissions of a small-scale biomass pellet boiler is not trivial and appropriate fixed-bed models that can be coupled with the CFD code are required. This paper reviews previous approaches specifically relevant to simulating fixed-bed biomass boilers. In the first part it considers approaches to modelling the heterogeneous solid phase and coupling this with the gas phase. The essential components of the sub-models are then overviewed. Importantly, for the optimization process a model is required that has a good balance between accuracy in predicting physical trends, with low computational run time.Finally, a simple combustion model written within a FLUENT user defined function is described, together with the preliminary modelling results obtained for modelling the combustion of wood pellets in a 50kW fixed-bed biomass pellet boiler.

Active Noise Control Algorithm Based on a Neural Network and Nonlinear Input-Output System Identification Model

The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below.

Existence of periodic solutions for a [formula omitted]-Laplacian neutral functional differential equation with multiple variable parameters

By means of Mawhin’s continuation theorem, a kind of p-Laplacian neutral functional differential equation with multiple variable parameters as follows: ( φ p ( ( u ( t ) − ∑ i = 1 n c i ( t ) u ( t − r i ) ) ′ ) ) ′ = f ( u ( t ) ) u ′ ( t ) + α ( t ) g ( u ( t ) ) + ∑ j = 1 n β j ( t ) g ( u ( t − γ j ( t ) ) ) + e ( t ) , is studied. The interesting thing is that the coefficient c is not a constant, by analyzing some properties of operator A : C T → C T , [ A x ] ( t ) = x ( t ) − ∑ i = 1 n c i ( t ) x ( t − r i ) and then using Mawhin’s continuation theorem, some new results on the existence of periodic solutions are obtained.

Models of R.C. structures for probabilistic serviceability analysis

Crack appearance or excessive deflection in a structure depends on multiple variable parameters, and the structural serviceability estimate may be improved with the aid of probabilistic analysis, which calls for suitable models. Members are subdivided into finite elements with distinct characteristics. For cracking and deflection analyses different thicknesses of the finite elements are taken, according to the behavior of the structure. Probabilities of crack appearance in a member are evaluated as function of the variable lengthwise strength of the concrete, its mean strength within the structure population, and the live load. The probabilities of member deflections are evaluated in the same manner as for cracking, the deflections are evaluated by summation of products curvature-abscissa for each finite elements. Some numerical analyses show very differing reliability of different real concrete structures. Proposed models allow the real behavior of structure as well as the variation of material characteristics and loads to be taken into account.