微型資料浮標觀測波浪及MSS的比對分析與演算流程的改善

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林志宗(Chih-Tsung Lin) 查詢紙本館藏

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水文與海洋科學研究所

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微型資料浮標觀測波浪及MSS的比對分析與演算流程的改善

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摘要(中)

本文利用國立中央大學團隊以及「流浪者科技有限公司」共同開發的全固態的微型資料浮標，採用微電機系統作為慣性感測的主要測量系統，其主要測量項目包含波浪以及風速風向的相關海表參數，是全球首個利用浮標計算海面平均坡度(Mean Square Slope，MSS)並反演風速的研究方法，對目前海上的實際風速測量有很大的貢獻。
受海況變化及極端氣候的影響，易導致儀器損率，為增加儀器生存率，資料浮標目前以微型化為最大趨勢，並撤銷測量風速使用的外露式風速計。本研究所使用之微型資料浮標可用來觀測風速風向與波浪，其觀測原理以及演算法可分為：(A) 慣性感測系統測量傾度數值，並以傾度譜為依據計算MSS (Mean Square Slope) 做為風速參數，再進行風速風向分析。(B) 慣性感測系統測量垂向加速度數值，計算加速度頻譜，經傳遞函數轉換得水位譜後進行波浪分析。
上述兩項觀測項目皆透過國立成功大學的大型斷面水槽進行分析，分為以時序列及頻譜分析的實驗來驗證可行性，實驗方法採用一系列的造波實驗檢測以及嚴格的資料品管分析後，分別對於水面傾斜度以及波浪參數進行修正，才運用於實地的測量。
水槽實驗中微型資料浮標與波高計測得之傾度與水位相互比較後發現，在傾度方面以時序的平均相對誤差來看，當水面傾斜角度小於15°時，平均相對誤差約為5.6%，水面傾斜角度超過15°之後，平均相對誤差迅速增加到約9.4%，另外由頻率域上可觀測兩儀器間的傳遞係數，當兩者測量比值為一時可作為補償係數。水位方面則採用垂向加速度譜與水位譜同時比較兩儀器之間的傳遞係數，同樣當兩者儀器頻譜測量比值為一時，可將其視為補償的係數，藉由定量分析各波浪參數經過傳遞函數之校正於造波水槽的波浪比對結果顯示：微型資料浮標波高與週期之均方根誤差分別為0.038m與0.363s。
實地測量主要收集海面平均坡度(Mean Square Slope，MSS) 、示性波高、平均週期等，利用MSS作為風速計算的依據，同時採用本身已經過校驗的儀器做相互映證，獲得浮標實際測量的可行性，本研究於冬季季風近岸海域其他原理之波浪儀比對結果顯示趨勢符合，波浪與週期之均方根誤差分別小於0.613m與1.15s。MSS演算法經資料品管及波譜在高頻斜率的校正，觀測之MSS量值及其與風速之相關性與文獻一致，表示微型資料浮標於MSS的觀測可應用於海面風速之觀測且利用波齡參數可進一步降低風速估計之誤差，與中央氣象局之風速測站測量測出的結果，約有2.39m/s 的均方根誤差。
目前微型資料浮標應用於現場實驗時發現，浮標易受繫纜的繩張力限制，當發生異常波浪或碎波時，會因為浮標整體質量較小而產生大幅度的離水跳動，進而造成觀測結果偏差，為此未來將持續改善錨定式的繫纜需求，並佈放漂流式的浮標來降低整體誤差來源。
關鍵字：微型資料浮標、海表粗糙度、海面平均坡度

摘要(英)

In this paper, the team of National Central University and "DRIFTER TECHNOLOGY CO., LTD." jointly developed a fully solid state micro data buoys, called "Miniature Wave Buoys (MWBs)", using MEMS (Micro Electro Mechanical Systems) as the main measurement system of inertial sensing, and the main measurement items include wind speed and direction and wave related sea surface parameters. It is the first research method in the world that uses buoys to calculate Mean Square Slope (MSS) and invert the wind speed.
The above two observation projects were analyzed in a large cross-sectional flume at National Cheng Kung University, divided into time series and spectrum analysis experiments to verify the feasibility. The experimental method used a series of wave generation experiments and rigorous data quality control analysis to correct the water surface inclination and wave parameters before applying them to field measurements. In order to increase the survival rate of the instrument, the MWBs are now miniaturized and the exposed anemometer used for wind speed and direction measurement has been removed. The MWBs used in this study can be used to observe wind speed, wind direction and waves. The observation principle and algorithm can be divided into: (A) The inertial sensing system measures the inclination value and calculates the MSS (Mean Square Slope) as the wind speed parameter based on the inclination spectrum, and then performs wind speed and wind direction analysis. (B) The inertial sensing system measures the vertical acceleration value, calculates the acceleration spectrum, and converts the water level spectrum by the transfer function to perform the wave analysis.
The above two observation projects were analyzed in a large cross-sectional flume at National Cheng Kung University (NCKU), divided into time series and spectrum analysis experiments to verify the feasibility. The experimental method used a series of wave generation experiments and rigorous data quality control analysis to correct the water surface inclination and wave parameters before applying them to field measurements.
In the water tank experiment, it was found that the average relative error of the time series was about 5.6% when the inclination angle of the water surface was less than 15°, and the average relative error increased rapidly to about 9.4% after the inclination angle of the water surface exceeded 15°. In addition, the transmission coefficient between the two instruments can be observed in the frequency domain, and when the ratio of the two measurements is one, it can be used as a compensation coefficient. The results of the wave comparison between the microdata buoy wave height and period were 0.038m and 0.363s, respectively, by quantitative analysis of the wave parameters corrected by the transfer function.
The field measurement mainly collects Mean Square Slope (MSS), indicative wave height, mean period, etc., and uses MSS as the basis for wind speed calculation, and at the same time uses its own calibrated instrument for cross-checking to obtain the feasibility of actual buoy measurement. The MSS algorithm was corrected by the data quality control and the high-frequency slope of the wave spectrum, and the observed MSS values and their correlation with the wind speed were consistent with the literature, indicating that the MSS observation of the microdata buoy can be applied to the observation of the wind speed at the sea surface and the error of wind speed estimation can be further reduced by using the wave age parameter. The results of the wind speed measurement from the central meteorological office have a root mean square error of about 2.39m/s.
At present, when the micro data buoy is applied to field experiments, it is found that the buoy is easily limited by the rope tension of the mooring line, and when abnormal waves or broken waves occur, the overall mass of the buoy is small and produces a large jump out of the water, which causes deviations in the observation results.

關鍵字(中)

★ 微型資料浮標
★ 海面平均坡度
★ 海表粗糙度

關鍵字(英)

★ Miniature Wave Buoy
★ Mean Square Slope
★ Sea Surface Roughness

論文目次

中文摘要 I
Abstract III
致謝 V
目錄 VI
圖目錄 VIII
表目錄 XX
符號說明 XXI
1 第一章緒論 1
1.1 海氣象浮標的發展與背景 1
1.2 微型資料浮標的特點 8
1.3 研究目的 9
1.4 本文組織 9
2 第二章微型資料浮標架構 10
2.1 浮標構型與系統架構 10
2.1.1 微電腦處理平台系統 12
2.1.2 衛星定位系統 13
2.1.3 慣性感測器系統 13
2.1.4 通訊與線上監控系統 16
2.1.5 供電系統 17
2.1.6 溫度感測器 18
2.2 參數演算方法 22
2.2.1 波浪與波浪頻譜特性 22
2.2.2 波浪參數之計算原理及方法 23
2.2.3 海面平均坡度 25
2.2.4 海面平均坡度之計算方法 31
2.3 微型資料浮標品管流程 33
2.3.1 波浪參數品管流程 33
2.3.2 MSS品管流程 34
3 第三章實驗室水槽校驗 37
3.1 實驗目的 37
3.2 實驗方法與流程 37
3.3 造波條件 40
3.4 設備及儀器 42
3.5 實驗結果 44
3.5.1 波浪參數 44
3.5.2 平均坡度 52
4 第四章微型資料浮標用於現場實測 58
4.1 桃園永安觀測站實驗 58
4.1.1 實驗佈置 58
4.1.2 實驗設備與儀器 59
4.1.3 拖曳係數、粗糙長度與風速之計算方式 61
4.1.4 實驗結果 65
4.2 新北白沙灣實驗 77
4.2.1 實驗佈置 77
4.2.2 實驗設備與儀器 79
4.2.3 實驗結果 81
5 第五章結論與建議 118
5.1 結論 118
5.2 建議 120
6 參考文獻 121

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指導教授

錢樺(Hwa Chien)

審核日期

2021-8-17

推文