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旋轉紊性熱流場之晶格玻茲曼建模與模擬

為了解決Generic USB Hub的問題，作者王春生 這樣論述：

近二十年來，晶格玻茲曼方法（Lattice Boltzmann Method）憑藉其平行效率與邊界處理上的優勢逐漸成為替代傳統納維-斯托克斯求解器（Navier-Stokes Solvers）來建模與模擬紊性熱流場之強有力工具。雖然晶格玻茲曼模型在靜態條件下已經獲得大量關注，但其在旋轉狀態下的研究卻還處於初級階段。因此本文基於旋轉坐標首度提出了一種新的晶格玻茲曼模型以用來對旋轉系統之紊性熱流場進行大渦模擬（Large Eddy Simulation）。大渦模擬之次格點模型為改善剪力的司馬格林斯基模型（Shear-Improved Smagorinsky Model）。由於此模型中的應變率可以

直接透過非平衡態分佈函數就地算出，因此其整過計算過程完全局部化。為了驗證以上提出之方法，本文藉其模擬了壓差驅動且具跨向旋轉與熱傳之紊性平行板流。基於摩擦速度與平行板半高之雷諾數（Reτ）固定為194而基於摩擦速度與平行板全高之旋轉數（Roτ）則從0變化至3.0。工作流體為空氣，其普朗特數（Pr）為0.71。計算結果包括平均速度、雷諾應力、均方根紊動速度、平均溫度、均方根紊動溫度以及紊性熱流密度。透過與前人直接模擬（Direct Numerical Simulation）之數據比較可以發現本研究結果與前人數據具有較高的一致性，這也驗證了本研究方法用於模擬旋轉紊性熱流場之可行性。旋轉紊性內流場在很

多工業應用中都扮演者非常重要的角色，如燃氣渦輪機、旋轉電極、化學反應器、分離器等。然而人們對這些設備中許多具挑戰性的物理現象，如速度峰值在正方形管道中偏向壓力壁（Pressure Wall）而在平行板中則偏向吸力壁（Suction Wall）等，並未完全了解。因此本文基於先前新方法對跨向旋轉方管之紊性全展流進行首次模擬研究。Reτ與Roτ分別固定為150與2.5而通道寬高比（AR）從1變化至6而後∞（平行板）。透過與前人直接模擬的數據進行比較，本方法在模擬具旋轉離心力與科氏力之紊性正方形管流（AR = 1）的可行性進一步得到驗證。隨著AR的增加，在方形管中首次發現存在著一個臨界AR = 4，當

寬高比低於此值時平均主流峰值偏向壓力壁而低於此值時其偏向吸力壁。此一臨界AR值也出現在表征管道中心紊流狀態之各向異性不變量圖（Anisotropic Invariant Map）中。藉助宏觀統御方程，本文從根本上闡明了平均主流峰值偏轉之物理機制以及臨界AR出現之原因。此外在所有的管流中，埃克曼層傳輸（Ekman Layer Transport）在空間和時間上皆持續，且對核心區之影響隨著AR的增加而減弱。為了進一步解釋流場對熱傳的影響，本文對以上AR = 1、4與∞管道在Pr = 0.71時進行了熱傳的研究。加熱方式為上下壁等溫而側壁絕熱。研究結果發現對所有方管其二次流被兩個逆向旋轉的側壁渦旋所

主導，此對渦旋顯著地促進了壓力壁兩角落附近的熱傳。相較於側壁渦旋，普朗特第二類二次流（Prandtl’s Secondary Flow of the Second Kind）對熱傳的貢獻則較小。關於熱傳的一個新發現是平均溫度分佈較純熱傳導結果之偏差在AR < 4時因埃克曼運動為負而在AR > 4時沿管道高度方向大部分區域為正。另外在所有參數中雷諾應力分量與熱傳之相關性最高，但該相關性會隨著AR的減少而下降。晶格玻茲曼方法的重要缺點之一就是均勻網格限制。如此一來，為了在高雷諾數下獲得精確的結果，需要對網格進行全局加密以解析到最小的流力尺度，這就意味著計算成本的驟升。為解決此問題，本文進一步發展了

多區域格點加密技術從而使晶格玻茲曼方法能夠在多重解析度格點上模擬三維流場和熱傳。該方法在同時包括外力與能量源項情況下使用一種三維縮放算法與二維雙三次插值來解決粗細格點間非平衡態函數的耦合問題。 隨後本文用新提出的加密方法模擬了三個基準算例，即三維通道強制對流、立方凹穴自然對流以及紊性平行板流，並將計算結果與前人數據進行比較，發現二者具有較好的一致性，這表明當前加密算法可準確模擬三維熱流場問題。

設計最佳化演算法預測蛋白質功能和辨認神經細胞影像

為了解決Generic USB Hub的問題，作者李光成 這樣論述：

The massive growth of protein sequence and neuron image datasets leads to the need of computation-based methods to predict and analyse their biological functions. To predict protein functions and recognize neurons images, machine-learning-based classifiers are regularly suggested. In present, the d

esired predictor of protein functions should provide both prediction efficiency and knowledge discovery. Meanwhile, the identification of informative features for recognizing neuron images is not easy due to a large number of available image features. This dissertation develops optimization methodol

ogies for both predicting protein sequences and recognizing neuron images based on an intelligent genetic algorithm (IGA).The scoring card method (SCM) is a simple and highly interpretable method for prediction and analysis of protein functions. The SCM calculates dipeptides propensity scores of an

interested protein function from the difference of dipeptide compositions between positive and negative sequences. The propensity scores of 400 dipeptides are optimized by IGA to enhance prediction accuracy while conserving the original characteristics of amino acid composition. A sequence score is

derived by utilizing these propensity scores to predict its protein function. Two SCM-based methods, SCMSOL and SCMCRYS, are proposed for prediction and analysis of protein solubility and crystallizability, and their tests accuracies are 84.3% and 76.1%, respectively, which are comparable to the sup

port vector machine based methods using the same dipeptide composition features. Moreover, the biological knowledge discovery and mutagenesis analysis for soluble and crystallizable proteins from the propensity scores are illustrated. The procedure of developing SCM-based methods for protein functio

n prediction can also be applied to design other methods for predicting protein functions with high prediction performance and high interpretable results.This dissertation also presents an automated neuron image feature identification system (Auto-NIFI) which is a user-friendly tool for automaticall

y extracting and identifying a small set of informative neuron image features utilizing an inheritable bi-objective combinatorial genetic algorithm (IBCGA). The feature selection of Auto-NIFI allows biologists to construct a suitable classifier for particular neuron image classification problems. To

identify neuron image features, Auto-NIFI provides a comprehensive set of image feature extraction modules together with the IBCGA feature selection modules. Notably, according to the huge collection of image feature extraction modules available in this tool, this system is also capable of applying

to a wide variety of biological image classification problems. Two methods, HCS-Neurons and DescNeuro, are proposed for neuron image classification. In the HCS-Neurons method, the usefulness of Auto-NIFI is demonstrated in identifying phenotypic changes in multi-neuron images upon response to drug

treatments of high-content screening. The identified three features of morphology were able to achieve an independent accuracy of 90.28% for recognizing neurons into six classes corresponding to six different nocodazole drug concentrations. By using the Auto-NIFI, DescNeuro can recognize a neuron in

the 3D Drosophila neuron database from a 2D image with promising recognition results.