We use a potential flow solver to investigate the aerodynamic aspects of flapping flights in enclosed spaces. The enclosure effects are simulated by the method of images. Our study complements previous aerodynamic ana...We use a potential flow solver to investigate the aerodynamic aspects of flapping flights in enclosed spaces. The enclosure effects are simulated by the method of images. Our study complements previous aerodynamic analyses which considered only the near-ground flight. The present results show that flying in the proximity of an enclosure affects the aerodynamic performance of flapping wings in terms of lift and thrust generation and power consumption. It leads to higher flight efficiency and more than 5% increase of the generation of lift and thrust.展开更多
InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples.These samples are selected in accordance wit...InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples.These samples are selected in accordance with the probability distribution function,known from the partition function of equilibrium state.As the stochastic error of the simulation results is significant,it is desirable to understand the variance of the estimation by ensemble average,which depends on the sample size(i.e.,the total number of samples in the set)and the sampling interval(i.e.,cycle number between two consecutive samples).Although large sample sizes reduce the variance,they increase the computational cost of the simulation.For a given CPU time,the sample size can be reduced greatly by increasing the sampling interval,while having the corresponding increase in variance be negligible if the original sampling interval is very small.In this work,we report a few general rules that relate the variance with the sample size and the sampling interval.These results are observed and confirmed numerically.These variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo methods.The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.展开更多
文摘We use a potential flow solver to investigate the aerodynamic aspects of flapping flights in enclosed spaces. The enclosure effects are simulated by the method of images. Our study complements previous aerodynamic analyses which considered only the near-ground flight. The present results show that flying in the proximity of an enclosure affects the aerodynamic performance of flapping wings in terms of lift and thrust generation and power consumption. It leads to higher flight efficiency and more than 5% increase of the generation of lift and thrust.
基金supported in part by the King Abdullah University of Science and Technology(KAUST)Center for Numerical Porous Media.In addition,S.Sun would also like to acknowledge the support of this study by a research award from King Abdulaziz City for Science and Technology(KACST)through a project entitled”Study of Sulfur Solubility using Thermodynamics Model and Quantum Chemistry”.
文摘InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples.These samples are selected in accordance with the probability distribution function,known from the partition function of equilibrium state.As the stochastic error of the simulation results is significant,it is desirable to understand the variance of the estimation by ensemble average,which depends on the sample size(i.e.,the total number of samples in the set)and the sampling interval(i.e.,cycle number between two consecutive samples).Although large sample sizes reduce the variance,they increase the computational cost of the simulation.For a given CPU time,the sample size can be reduced greatly by increasing the sampling interval,while having the corresponding increase in variance be negligible if the original sampling interval is very small.In this work,we report a few general rules that relate the variance with the sample size and the sampling interval.These results are observed and confirmed numerically.These variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo methods.The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.
