鎶ュ憡棰樼洰錛歍he basics of TEMOM for Brownian Coagulation
銆€銆€鎶ュ憡鏃墮棿錛?018騫?鏈?鏃?0:30
銆€銆€鎶ュ憡鍦扮偣錛氫話浠崡妤?07浼氳瀹?br />銆€銆€鎶ュ憡浜猴細璋㈡槑浜紝鍗庝腑縐戞妧澶у鍓暀鎺堛€?996騫淬€?004騫淬€?007騫村湪姝︽眽縐戞妧澶у銆佹姹夌鎶€澶у銆佹禉姹熷ぇ瀛﹀垎鍒幏寰楀伐瀛﹀澹€佺澹拰鍗氬＋瀛︿綅銆?996騫?鏈?2001騫?鏈堝伐浣滀簬瀹濋挗鑲′喚榛勭煶鍒嗗叕鍙革紝2007騫?鏈?2009騫?鏈堬紝鍦ㄥ崕涓鎶€澶у鍔ㄥ姏宸ョ▼涓庡伐紼嬬儹鐗╃悊鍗氬＋鍚庢祦鍔ㄧ珯浠庝簨鍗氬＋鍚庣爺絀跺伐浣滐紝鍏朵腑鍒嗗埆浜?008騫?鏈?2009騫?鏈堝拰2009騫?鏈堬紞8鏈堝湪棣欐腐鐞嗗伐澶у鏈烘宸ョ▼緋諱綔璁塊棶瀛﹁€咃紝2011騫?鏈堣嚦2012騫?鏈堝伐浣滀簬緹庡浗鐗規媺鍗庡窞绔嬪ぇ瀛︼紝鐜板伐浣滀簬鍗庝腑縐戞妧澶у鐓ょ噧鐑у浗瀹墮噸鐐瑰疄楠屽錛屽壇鏁欐巿銆傚彂琛?0浣欑瘒SCI璁烘枃銆?br />銆€銆€鎶ュ憡鍐呭錛欼n coal combustion process, aerosol dynamics plays an important role. In 2008, Yu and Lin have proposed a moment-based approach, termed as the Taylor series expansion method of moment (TEMOM), to approximate the population balance equation which governs the coal particles in combustion flows. This approach makes no prior assumption on the shape of particle size distribution; the derivation of TEMOM is completely based on Taylor series expansion. In 2012, my collaborators and I developed a compact and fast MATLAB program coupled with TEMOM to simulate the effect of coherent structures on the particle Brownian coagulation in the temporal mixing layer (JAS, 2012). The results show that the particle coagulation within the eddy core has an obvious wave-like distribution similar to the large-scale eddy structures, which indicate that the relative growth rate is slow at long times, and the system bottleneck is the advection and diffusion.
銆€銆€The self-similarity also implies that the effect of coagulation and effect of mixing by advection and diffusion on particle growth could be modeled separately. Since the TEMOM moment method was proposed in 2008, it has been widely discussed by scholars all around the world. They proposed some fundamental problems for TEMOM model, such as the uniqueness of the expansion, the effectiveness for the closuring fractional moments, the choice of the expansion point, the convergence of the analytical solutions, and the inverse problem, etc. Some problem has been clariRied taking the TEMOM model for Brownian coagulation in the free molecule regime as an example (HMT, 2014). And most of the fundamental problems can be attributed to the error analysis of TEMOM model; based on the lognormal size distribution assumption, the system errors are obtained for different TEMOM models due to Brownian coagulation (AST, 2016).
銆€銆€嬈㈣繋騫垮ぇ甯堢敓鍙傚姞錛?/p>
鏈虹數瀛﹂櫌
2018騫?鏈?鏃?/p>
