By optimizing the cost function to achieve approximation to represent both the content and the style, numerical meshes can be generated and optimized. We apply the NST to generate meshes for rough fractures with asperities formed in rock, a network of fractures embedded in rock, and a sand ...
1. Here, x(t) is the external force, as the summation of wave and wind loads, and y(t) is the ship response function as the output that is transferred to the Laplacian domain, and H(s) denotes the Laplacian of the TF. As such, the mass-spring-damper state equation can be ...
This objective function reflects the error between TMM and FEM results, and the conicity which makes the objective function minimum is the optimized conicity under certain combination of ratios. The range of conicity is from 0 to π/2. Since the optimization of each combination of ratios has sin...
Check for missing argument or incorrect argument data type in call to function 'specifyCoefficients'. The model is defined as follows: obj.tm_SS = createpde('thermal','steadystate'); gdm = [r0;r9;r1;r2;r3;r4;r5;r6;r7;r8]';
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s Single Equation Wall Function [66]. In this work, the boundary layer is assumed to be fully turbulent; hence, no laminar-turbulent transition model is used. A Gauss upwind Laplacian scheme is used, with linear interpolation of the diffusion term. A structured C-grid mesh defines the ...
Specifically, the PIN diodes function as switches to allow voltage to accumulate on C1 during only the negative cycle of the input voltage. Voltage doubling on C2 results from the use of C1 as an additional voltage source during the positive cycle of the input voltage. Moreover, C2 reduces ...
(13-16) Assign the random positions and velocities to the particles of randomly generated population and Determine fitness function (RRSS) Initialize, Pbest, and determine Gbest Update velocities and positions according to equations (28) & (29) Evaluate the fitness of each particle (RRSS) and...
Thus, solving one function referred to as a source that characterizes a problem can yield knowledge that can be reused to solve other related function referred to as target. Re-purposing such shared knowledge, especially in cases of complex optimization, aids in faster convergence, more accurate ...
Additionally, Chen and Akasaka [18] proposed a 3D CNN model to predict the velocity and pressure fields by calculating the unsigned distance function of space to represent the vehicle shape. However, learning such model is confined to a single perspective, failing to treat all parts of the ...