Modeling flame propagation speed and quenching distance of aluminum dust flame with spatially random distribution of particles

In this research combustion of aluminum dust particles in a quiescent medium with spatially discrete sources distributed in a random way was studied by a numerical approach.A new thermal model was generated to estimate flame propagation speed in a lean/rich reaction medium.Flame speed for different particle diameters and the effects of various oxidizers such as carbon dioxide and oxygen on flame speed were studied.Nitrogen was considered the inert gas.In addition,the quenching distance and the minimum ignition energy(MIE) were studied as a function of dust concentration.Different burning time models for aluminum were employed and their results were compared with each other.The model was based on conduction heat transfer mechanism using the heat point source method.The combustion of single-particle was first studied and the solution was presented.Then the dust combustion was investigated using the superposition principle to include the effects of surrounding particles.It is found that larger particles have higher values of quenching distance in comparison with smaller particles in an assumed dust concentration.With the increase of dust concentration the value of MIE would be decreased for an assumed particle diameter.Considering random discrete heat sources method,the obtained results of random distribution of fuel particles in space provide closer and realistic predictions of the combustion physics of aluminum dust flame as compared with the experimental findings.

Model of Random Polygon Particles for Concrete and Mesh Automatic Subdivision

In order to study the constitutive behavior of concrete in mesoscopic level, a new method is proposed in this paper. This method uses random polygon particles to simulate full grading broken aggregates of concrete. Based on computational geometry, we carry out the automatic generation of the triangle finite element mesh for the model of random polygon particles of concrete. The finite element mesh generated in this paper is also applicable to many other numerical methods.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

RandWPSO-LSSVM optimization feedback method for large underground cavern and its engineering applications

According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment.

Quadric SFDI for Laplacian Discretisation in Lagrangian Meshless Methods

In the Lagrangian meshless(particle)methods,such as the smoothed particle hydrodynamics(SPH),moving particle semi-implicit(MPS)method and meshless local Petrov-Galerkin method based on Rankine source solution(MLPG_R),the Laplacian discretisation is often required in order to solve the governing equations and/or estimate physical quantities(such as the viscous stresses).In some meshless applications,the Laplacians are also needed as stabilisation operators to enhance the pressure calculation.The particles in the Lagrangian methods move following the material velocity,yielding a disordered(random)particle distribution even though they may be distributed uniformly in the initial state.Different schemes have been developed for a direct estimation of second derivatives using finite difference,kernel integrations and weighted/moving least square method.Some of the schemes suffer from a poor convergent rate.Some have a better convergent rate but require inversions of high order matrices,yielding high computational costs.This paper presents a quadric semi-analytical finite-difference interpolation(QSFDI)scheme,which can achieve the same degree of the convergent rate as the best schemes available to date but requires the inversion of significant lower-order matrices,i.e.3×3 for 3D cases,compared with 6×6 or 10×10 in the schemes with the best convergent rate.Systematic patch tests have been carried out for either estimating the Laplacian of given functions or solving Poisson’s equations.The convergence,accuracy and robustness of the present schemes are compared with the existing schemes.It will show that the present scheme requires considerably less computational time to achieve the same accuracy as the best schemes available in literatures,particularly for estimating the Laplacian of given functions.

Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment - AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.

Random Batch Particle Methods for the Homogeneous Landau Equation

We consider in this paper randombatch particlemethods for efficiently solving the homogeneous Landau equation in plasma physics.The methods are stochastic variations of the particle methods proposed by Carrillo et al.[J.Comput.Phys.:X 7:100066,2020]using the random batch strategy.The collisions only take place inside the small but randomly selected batches so that the computational cost is reduced to O(N)per time step.Meanwhile,our methods can preserve the conservation of mass,momentum,energy and the decay of entropy.Several numerical examples are performed to validate our methods.

Reconstruction of solid oxide fuel cell electrode microstructure and analysis of its effective conductivity

The effective conductivity （aeff） of solid oxide fuel cell （SOFC） electrode is an important parameter for predicting the ohmic loss in SOFC. This paper investigates the effective conductivity of SOFC electrodes recon- structed numerically by packing spherical particles in a computational domain, followed by a dilation process to simulate the sintering procedure. The effects of various parameters on the effective conductivity of the electrodes are investigated, including material composition, porosity, particle size and contact angle. Results show that the effective conductivity ratio （aeff/ao） of the computed con- ducting phase is mainly affected by its total volume frac- tion （VF） in electrode （including the porosity）. The effective conductivity can be improved by increasing the VF, electrode particle size or the contact angle between electrode particles. Based on the numerical results, the conventional percolation model for the calculation of O＇eft is improved by adjusting the Bruggeman factor from 1.5 to 2.7. The results are useful for understanding the microstructure properties of SOFC composite electrode and for subsequent electrode optimization.

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.

Two timescales for longitudinal dispersion in a laminar open-channel flow

At small dimensionless timescales T（= tD/H^2）, where t is the time, H is the depth of the channel and D is the molecular diffusion coefficient, the mean transverse concentration along the longitudinal direction is not in a Gaussian distribution and the transverse concentration distribution is nonuniform. However, previous studies found different dimensionless timescales in the early stage, which is not verified experimentally due to the demanding experimental requirements. In this letter, a stochastic method is employed to simulate the early stage of the longitudinal transport when the Peclet number is large. It is shown that the timescale for the transverse distribution to approach uniformity is T= 0.5, which is also the timescale for the dimensionless temporal longitudinal dispersion coefficient to reach its asymptotic value, the timescale for the longitudinal distribution to approach a Gaussian distribution is T= 1.0, which is also the timescale for the dimensionless history mean longitudinal dispersion coefficient to reach its asymptotic value.