Effects of Lower Heat Value Fuel on the Operations of Micro-Gas Turbine

The characteristics of fuel from biomass, coal and some waste materials are lower heat value and different compositions. The lower heat value fuel (LHVF) can be used on power engine such as boiler, gas engine and gas turbine. Some laboratory and pilot work have been done, but the work done on micro-gas turbine is still limited. The characteristics of LHVF can cause the operations change of micro-gas turbine designed for nature gas. Some possible adjustment and modification methods were mentioned for the use of LHVF on micro-gas turbine. One kind of representative LHVF was chosen and the operations of micro-gas turbine were analyzed. The temperature field and the non-uniformity scale of temperature distribution of combustor were calculated using FLUENT. The feasibility of different adjustment and modification methods were analyzed according to the efficiency, output power and the non-uniformity scale of temperature distribution.

Compressor and Turbine Multidisciplinary Design for Highly Efficient Micro-gas Turbine

Multidisciplinary design optimization （MDO） is widely employed to enhance turbomachinery compo- nents efficiency. The aim of this work is to describe a complete tool for the aero-mechanical design of a radial in- flow turbine and a centrifugal compressor. The high rotational speed of such machines and the high exhaust gas temperature （only for the turbine） expose blades to really high stresses and therefore the aerodynamics design has to be coupled with the mechanical one through an integrated procedure. The described approach employs a fuUy 3D Reynolds Averaged Navier-Stokes （RANS） solver for the aerodynamics and an open source Finite Element Analysis （FEA） solver for the mechanical integrity assessment. Due to the high computational cost of both these two solvers, a meta model, such as an artificial neural network （ANN）, is used to speed up the optimization design process. The interaction between two codes, the mesh genera- tion and the post processing of the results are achieved via in-house developed scripting modules. The obtained results are widely presented and discussed.

Limitations of Lattice Boltzmann Modeling of Micro-Flows in Complex Nanopores

The multiscale transport mechanism of methane in unconventional reservoirs is dominated by slip and transition flows resulting from the ultra-low permeability of micro/nano-scale pores,which requires consideration of the microscale and rarefaction effects.Traditional continuum-based computational fluid dynamics(CFD)becomes problematic when modeling micro-gaseous flow in these multiscale pore networks because of its disadvantages in the treatment of cases with a complicated boundary.As an alternative,the lattice Boltzmann method(LBM),a special discrete form of the Boltzmann equation,has been widely applied to model the multi-scale and multi-mechanism flows in unconventional reservoirs,considering its mesoscopic nature and advantages in simulating gas flows in complex porous media.Consequently,numerous LBM models and slip boundary schemes have been proposed and reported in the literature.This study investigates the predominately reported LBM models and kinetic boundary schemes.The results of these LBM models systematically compare to existing experimental results,analytical solutions of Navier-Stokes,solutions of the Boltzmann equation,direct simulation of Monte Carlo(DSMC)and information-preservation DSMC(IP_DSMC)results,as well as the numerical results of the linearized Boltzmann equation by the discrete velocity method(DVM).The results point out the challenges and limitations of existing multiple-relaxation-times LBM models in predicting micro-gaseous flow in unconventional reservoirs.

Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm

Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model.However,convective parameterization contains numerous parameters whose values are in large uncertainties.In particular,it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction.To address these issues,a micro-genetic(micro-GA)algorithm is coupled to the Kain-Fritsch(KF)convective parameterization scheme(CPS)in the WRF to improve the quantitative precipitation forecast(QPF).The optimization focuses on two parameters in the KF scheme:the convective time scale and the conversion rate.The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function.Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected,and for each case the parameter values are adjusted to achieve the best QPF skill.Significant improvements in QPF are evident with an increase in the average equitable threat score(ETS)by 5.8%for the first case,and by 18.4%for the second case.The results demonstrate that the micro-GAKF coupling system is effective in optimizing the parameter values,which affect the applicability of CPS in a high-resolution model,and therefore improves the rainfall prediction in both ETS and spatial distribution.