Analytical design of effective thermal conductivity for fluid-saturated prismatic cellular metal honeycombs

A comparative optimal design of fluid-saturated prismatic cellular metal honeycombs （PCMHs） having different cell shapes is presented for thermal management applications. Based on the periodic topology of each PCMH, a unit cell （UC） for thermal transport analysis was selected to calculate its effective thermal conductivity. Without introducing any empirical coefficient, we modified and extended the analytical model of parallel-series thermal-electric network to a wider porosity range （0.7 ~ 0.98） by considering the effects of two-dimensional local heat conduction in solid ligaments inside each UC. Good agreement was achieved between analytical predictions and numerical simulations based on the method of finite volume. The concept of ligament heat conduction efficiency （LTCE） was proposed to physically explain the mechanisms underlying the effects of ligament configuration on effective thermal conductivity （ETC）. Based upon the proposed theory, a construct strategy was developed for designing the ETC by altering the equivalent interaction angle with the direction of heat flow： relatively small average interaction angle for thermal conduction and relatively large one for thermal insulation.

A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials

In this paper,a novel mixed wavelet-learning method is developed for predicting macroscopic effective heat transfer conductivities of braided composite materials with heterogeneous thermal conductivity.This innovative methodology integrates respective superiorities of multi-scale modeling,wavelet transform and neural networks together.By the aid of asymptotic homogenization method(AHM),off-line multi-scalemodeling is accomplished for establishing thematerial databasewith highdimensional and highly-complexmappings.Themulti-scalematerial database and the wavelet-learning strategy ease the on-line training of neural networks,and enable us to efficiently build relatively simple networks that have an essentially increasing capacity and resisting noise for approximating the high-complexity mappings.Moreover,it should be emphasized that the wavelet-learning strategy can not only extract essential data characteristics from the material database,but also achieve a tremendous reduction in input data of neural networks.The numerical experiments performed using multiple 3D braided composite models verify the excellent performance of the presentedmixed approach.The numerical results demonstrate that themixedwaveletlearningmethodology is a robustmethod for computing themacroscopic effective heat transfer conductivities with distinct heterogeneity patterns.The presentedmethod can enormously decrease the computational time,and can be further expanded into estimating macroscopic effective mechanical properties of braided composites.

Solutions of the modification heating conduction equations of a kind of laser thermal effect

This paper has solved the Chester modified heat conduction equation of the different relaxation time r value under different temperature conditions, different boundary conditions and the different initial conditions by different means of methods. These solutions can help to obtain temperature field of laser thermal effects.