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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97271完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰 | zh_TW |
| dc.contributor.advisor | I-Hsuan Hong | en |
| dc.contributor.author | 陳孟寰 | zh_TW |
| dc.contributor.author | Meng-Huan Chen | en |
| dc.date.accessioned | 2025-04-02T16:13:47Z | - |
| dc.date.available | 2025-04-03 | - |
| dc.date.copyright | 2025-04-02 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2025-02-26 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97271 | - |
| dc.description.abstract | 近年來，由於永續發展及淨零碳排的目標，海洋能源相關議題逐漸獲得重視，洋流能更是當中極具潛力的發電形式。然而，受限於水下渦輪機的建置成本及資源限制，物理實驗難以頻繁及重複進行。本研究設計了不同的抽樣方法進行電腦模擬，使用人工神經網路(artificial neural network, ANN)建構後設模型(surrogate model)，捕捉模擬器的輸入輸出關係，並採用啟發式演算法(metaheuristic)搜尋最佳參數。在本研究的數值分析案例中，後設模型與電腦模擬在測試集的平均百分比誤差小於5%，最佳參數模擬結果與物理實驗的誤差可達到小於14%。 | zh_TW |
| dc.description.abstract | In recent years, marine energy issues have increasingly garnered attentions due to the goal of sustainable development and net-zero carbon emissions, with ocean current energy emerging as a highly promising form. Physical experiments, constrained by the high implementation cost and limited resources, are challenging to conduct frequently and repetitively. This study designs various sampling methods to perform computer simulations and applies artificial neural networks (ANN) to build a surrogate model, capturing the input-output relationships of the simulator. Additionally, we employ metaheuristic algorithms to search for optimal parameters of the simulator. In the investigated numerical examples, the average percentage error between the surrogate model and computer simulation on the test set is less than 5%. The error between the simulation results with optimal parameters and the physical experiments can reach less than 14%. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-04-02T16:13:47Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-04-02T16:13:47Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 目次 v 圖次 vii 表次 ix 第一章 緒論 1 第二章 模擬參數最佳化 6 2.1 研究背景 6 2.2 抽樣方法 10 2.2.1 拉丁超立方抽樣 10 2.2.2 Halton序列 11 2.2.3 重心法Voronoi鑲嵌抽樣 12 2.3 人工神經網路 14 2.4 啟發式演算法 16 2.4.1 基因演算法 16 2.4.2 粒子群演算法 18 第三章 數值分析 20 3.1 輸入資料設定 20 3.1.1 抽樣設定 20 3.1.2 神經網路設定 21 3.1.3 啟發式演算法設定 21 3.2 輸入參數組合分析 22 3.3 電腦模擬結果分析 24 3.4 最佳參數模擬結果分析 26 3.5 結果綜合探討 29 第四章 結論 41 參考文獻 42 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 電腦模擬 | zh_TW |
| dc.subject | 水下渦輪機 | zh_TW |
| dc.subject | 抽樣方法 | zh_TW |
| dc.subject | 啟發式演算法 | zh_TW |
| dc.subject | 後設模型 | zh_TW |
| dc.subject | Marine Turbines | en |
| dc.subject | Computer Simulation | en |
| dc.subject | Surrogate Model | en |
| dc.subject | Metaheuristic | en |
| dc.subject | Sampling Methods | en |
| dc.title | 連續型後設模型求解模擬器參數之應用 | zh_TW |
| dc.title | Application of Continuous Surrogate Model for Solving Simulator Parameters | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 蘇哲平 | zh_TW |
| dc.contributor.coadvisor | Che-Ping Su | en |
| dc.contributor.oralexamcommittee | 陳文智;羅弘岳 | zh_TW |
| dc.contributor.oralexamcommittee | Wen-Chih Chen;Hong-Yueh Lo | en |
| dc.subject.keyword | 水下渦輪機,抽樣方法,電腦模擬,後設模型,啟發式演算法, | zh_TW |
| dc.subject.keyword | Marine Turbines,Sampling Methods,Computer Simulation,Surrogate Model,Metaheuristic, | en |
| dc.relation.page | 46 | - |
| dc.identifier.doi | 10.6342/NTU202500750 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-02-27 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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