particle generation 中文意思是什麼

particle generation 解釋
粒子的產生
  • particle : n 1 顆粒，微粒；微量，極少量。2 【物、數】粒子，質點。3 【語法】虛詞，不變詞〈冠詞、副詞、介詞、...
  • generation : n. 1. 代〈約30年〉，世代，時代；同時代的人。2. 一代[一世]。3. 生殖，生育；發生，產生。4. 【數學】（面、體、線的）形成。5. 完善化階段，完善化方案，完善化的模型；發展階段。
  1. The generation of energetic ions during the interaction of a linear - polarized ultra - short ultra - intense laser pulse with solid targets are examined by particle simulation. three energetic ion populations are observed and the acceleration mechanisms are analyzed, respectively. the first population is pulled out from the target by the electron jet in front of the target

    模擬觀察到三群高能離子的產生，並對其加速機制一一進行了分析:在靶的前部，向外噴射的高能電子在靶前形成電子云，將一部分離子拉出靶面，形成第一群高能離子;激光驅動大量高能電子向靶內輸運，這些電子牽引靶前部的離子向前加速，形成第二群高能離子:高能電子很快穿透靶，在靶后形成電子云，加速靶后表面處的離子，形成第三群高能離子。
  2. Particle trouble is an important object in clean industry, related manufactory all get to know the damage caused by particle, monitor and control particle generation

    顆粒問題是潔凈行業重點關注的對象之一，相關產業都已經意識到顆粒污染帶來的危害，並且通過各種渠道監測並控制顆粒污染的發生。
  3. Certain discrepancy remain between the simulation results and the experiment results, not only caused by the turbulent model, but also by the simplification of the inlet boundary condition and the mesh generation. modeling gas - particles interaction flows is complex. in this thesis, gas - phase transport equations coupled with the gas - particle interaction are derived based on the dsm turbulent models to handle the interaction of momentum and kinetic energy of turbulence between the gas and particles

    分離器內的固體顆粒運動採用涉及湍流擴散影響的隨機軌道模型和確定軌道模型，同時在湍流模型中加入了顆粒影響的源項，在流場計算的基礎上，模擬了不同直徑的顆粒在分離器內的運動規律及顆粒分離效率，並同理論和實驗得到的數據進行了比較。
  4. The traffic model and a suit of differential equations presenting the status of the system are given first, from which an objective function is derived, and then the transmission is optimally controlled by the neural network which is characterized by nonlinear map and the particle swarm optimization algorithm which is characterized by stochastic optimization, namely the neural network is employed to generate variable rate of token generation, and the particle swarm optimization algorithm with inertia weight is employed to optimally train neural network in the form of finding a sub - optimal resolution in acceptable computation time

    本文給出了傳輸控制的系統模型及其系統各狀態的差分方程表示，由此推導出了系統的代價函數。然後利用神經網路的非線性映射的功能和基於概率尋優的粒子群優化演算法對系統進行優化控制，利用神經網路控制令牌桶的可變令牌產生速率，利用帶慣性權重的粒子群優化演算法對神經網路的權值進行優化訓練，使其在可以接受的時間內達到次優解。
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