Hydraulic Characteristics of Free Overfall-Impacted Channel Flow over Sloping Bed

近年來為了河道治理或取水，經常於河道中設置跌水工、攔河堰或防砂壩等水利工程設施構造物，且因工程設施上下游產生高差，而高速水流流動所產生之水舌沖擊力最具沖擊能量及沖刷潛勢，常導致水工結構物之毀損破壞。依據台灣60條較大河川資料統計，河道平均坡度小於7%共43條，占全部60條河川之72%。為探究當渠床坡度改變，其相關水力沖擊特性參數為何，本文由不同的渠床坡度（S=0%、3%、6%、9%）、改變跌水高度（H=0.15m、0.20m、0.25m、0.30m）及配合不同單寬流量（q=0.0076~0.0402cms/m）進行單階自由跌水渠槽試驗，且在自由跌水下游渠床埋設壓力量測系統，以不干擾流場下量測下游之縱向渠床壓力水頭分佈，進一步分析其水力沖擊特性參數。為使設計者更為便捷及設計後圖示之展示快速，將利用自由跌水渠槽試驗所獲得的經驗式，藉由VB（Visusal Basic）程式語言，建構自由跌水工設計之視窗化，且應用類神經網路（Artificial Neural Network, ANN）模式，推估模擬自由跌水渠槽試驗所得水力沖擊特性參數，並藉試驗資料點作驗證，以評估其精確性。 研究結果發現，在渠槽試驗方面，最大沖擊壓力水頭（Hpd）、沖擊位置（Ld）、單寬沖擊力（Fe）及能量損失（△E）與渠床坡度（S）呈正相關；而水墊區水深（Yp）、沖擊角度（θ）及尾水深（Y1）與渠床坡度（S）為負相關。視窗化設計方面，開發出可以呈現自由跌水之水力沖擊特性參數，設計結果示意圖及計算表單之視窗化模組。倒傳遞類神經網路模式方面，在推估自由跌水渠槽試驗所得水力沖擊特性參數，都有良好之成效。研究結果對於在自由跌水相關參數設計及推估模擬上，提供一便利且精確的方法。In recent years, the hydraulic structures crossing the river have been widely used in both natural and artificial channels to process the water resource management. These structures usually lead to a sudden vertical change of channel slope and induce a free over-fall flow, and the large impact force of a free-falling nappe due to free over-fall flow usually damages the hydraulic structure. According to statistics of the sixty largest rivers in Taiwan, there are 43 rivers (72 percent of the 60 rivers) with average slope less than 7 percent. This study used the pressure transducers, which did not disturb the flow field, were set up to measure the pressure distributions along the streamwise direction downstream of the free over-fall. The experiment includes the different drops as 0.15 m, 0.20 m, 0.25 m and 0.30 m with the range of discharges 0.0076-0.0402 cms/m for different bed slopes as 0 %, 3 %, 6 % and 9 %. Furthermore, with application of window-based design of free over-fall, the instant design information can be acquired conveniently in a short time by computer. Besides, the experimental data will be trained and validated by the artificial neural network (ANN). The experimental results indicate that the maximum impact pressure head (Hpd), the impact position (Ld), the unit width of the impact force (Fd) and the energy loss (△E) appear to be proportional to the bed drop of the downstream channel (S). The depth of pool (Yp), the nappe impact angle (θ) and the depth of tailwater (Y1) is inversely proportional to the bed drop of the downstream channel (S). Besides, this study developed a window-based design module of single step free over-fall through Visual Basic program and the figures of hydraulic impact parameters and a list table of computed result can be displayed. Moreover, the test results showed that the artificial neural network method provided accurate estimations for the hydraulic impact parameters of free overfall flow.謝 誌 I 中 文 摘 要 II Abstract III 目錄 V 圖目錄 VIII 表目錄 XI 附圖目錄 XIII 符號說明 XV 第一章 緒 論 1 1-1 研究動機 1 1-2 研究目的 2 1-3 本文組織 3 第二章 文獻回顧 5 2-1 自由跌水沖擊特性參數 5 2-1-1 壓力水頭分佈及沖擊位置 6 2-1-2 水墊區水深及尾水深 11 2-1-3 沖擊角度及沖擊力 14 2-1-4 能量變化 16 2-2 類神經網路 19 2-3 視窗化研究 22 第三章 理論分析 23 3-1 壓力水頭分佈模式 23 3-2 自由跌水沖擊特性參數 25 3-2-1 沖擊位置 25 3-2-2 沖擊力 27 3-2-3 沖擊角度 30 3-3 水流能量變化 30 第四章 渠槽試驗與結果 32 4-1 試驗設計 32 4-1-1 試驗設備與佈置 32 4-1-2 試驗步驟與條件 35 4-2 渠槽試驗結果分析 39 4-2-1 自由跌水下游渠床壓力分佈 39 4-2-2 自由跌水沖擊特性參數 47 4-3 力學觀點探討自由跌水 60 4-3-1 自由跌水沖擊力 60 4-3-2 水流能量變化 68 4-4 坡度效應 72 第五章 視窗化設計 74 5-1 視窗化建構 74 5-1-1 水力特性參數數值演算 74 5-1-2 視窗化架構 76 5-1-3 現地資料調查 79 5-2 模式操作與結果 81 5-2-1 視窗化操作 83 5-2-2 VB模式與試驗結果比較 85 5-3 實例演算 92 第六章 類神經網路模式與應用 99 6-1 類神經網路自由跌水沖擊特性模式 99 6-1-1 人工神經元模型 99 6-1-2 類神經網路學習過程 100 6-1-3 多層感知機 101 6-2 類神經網路應用 104 6-2-1 資料正規化 104 6-2-2 類神經網路訓練條件 105 6-2-3 類神經網路訓練與模擬 108 6-3 類神經網路結果與模式比較 110 第七章 結論與建議 117 7-1 結論 117 7-2 建議 123 參考文獻 124 附圖 128 簡歷及學術著作 14

Effect of the Hydraulic Characteristics of Detention Dam with Compound Outlet to Detention Volume

為避免集水區下游之洪流災害，通常於上游設置滯洪壩形成滯洪池，藉由洪水之暫時停儲於滯洪區，以降低暴雨洪峰流量、控制流心及有效調節泥砂流出量。鑑於滯洪容積大小取決於入流歷線、洪峰消減程度、出流口型式、出流口流量公式及渠床坡度，本研究改變四種渠床坡度，配合五種不同複合式出流口滯洪壩及四種不同入流歷線特徵值，進行變量流歷線試驗，以探討渠床坡度效應對滯洪水理特性之影響，且於定量流試驗釐定出流口流量公式，獲致下列成果： 1.經理論推導與實驗分析，本研究提出當h(出流口水深)￡H(出流口斷面最大水深)時，複合式出流口流量公制推估式為 ，其中Q為出流口流量，g為重力加速度，b、p、m分別為梯形斷面之底寬、高度及側坡水平比值，且當m=0時轉換為矩形者，以為滯洪特性中出流歷線推求之依據。 2.出流歷線與入流歷線型態 、洪峰消減度k、滯洪壩出流口型式m、渠床坡度S及出流口流量公式有極密切關係，研究獲致具體成果並提出推估模式。 3.有關滯洪池設計之最小滯洪容積，由文中經驗模式可表成 ，其中 為入流歷線特徵值、k為洪峰消減度、m為梯形斷面之側坡水平比值、S為渠床坡度、Qim為入流歷線尖峰流量、Tip為入流歷線尖峰時間，作為設計規劃之參考。In order to avoid disasters downstream, it is usual that detention dam is constructed upstream for peak runoff reduction, and control of streamline and sediment. The size of detention volume is the function of inflow hydrograph, peak inflow reduction, the form of outlet devices, discharge formula of outlets and channel bed slope. Consequently, four types of channel bed slope, five kinds of compound outlets and four characteristic parameters of inflow hydrograph are combined to simulate the variation of detention volume and to explore the effect of channel bed slope for detention efficiency by experiments. Also, discharge formulas of different compound outlets are obtained by steady flow experimental. The results of this study are outlined as follows: 1.Discharge formulas of different compound outlets are obtained through theoretical and experimental analyses. The conjectural equation in metric system is given by while h(outlet depth)≦H(biggest depth in the section of outlet), where is the discharge of outlet, g is a gravitational acceleration, b is the breadth at bottom of trapezoidal section, p is the height of trapezium, side slopes of m (horizontal): 1 (vertical) of the trapezoidal section, and it may transfer into rectangular section when m=0. Outflow hydrographs can be found by this equation and inflow hydrograph behind detention characteristics. 2.Outflow hydrograph is found to be highly related to the inflow hydrographs , forms of outlets m, channel bed slope S and discharge formula of outlets. The specific result and formula of outflow hydrograph is established by these parameters. 3.The empirical formula of minimum detention volume is given by where is the characteristic parameter of inflow hydrograph, k is the ratio of peak inflow reduction, side slopes of m (horizontal) : 1 (vertical) of the trapezoidal section, S is the channel bed slope, Qim is the peak inflow, Tip is the time peak of inflow. This formula can be used in preliminary desgins.摘要I 英文摘要II 圖目錄VI 表目錄XI 照片目錄XII 符號說明XIII 第一章 前 言1 1-1 研究動機1 1-2 研究目的2 1-3 本文架構3 第二章 文獻回顧5 2-1流量公式之相關研究5 2-2滯洪設施之相關研究10 2-3滯洪特性之相關研究12 2-4坡度效應之相關研究16 第三章 理論分析21 3-1滯洪現象21 1.歷線21 2. 出流口22 3. 滯洪特性23 4. 坡度效應24 3-2滯洪特性24 1.洪峰消減與稽延25 2.歷線特徵與洪峰消減26 3. 滯洪容積29 3-3複合式出流口流量公式12 1.流量公式推演32 2.無因次流量公式36 第四章 渠槽試驗43 4-1 試驗佈置43 4-2 試驗儀器43 4-3 試驗步驟45 4-4 試驗條件46 第五章 結果分析57 5-1 出流口流量公式57 1.流量係數57 2.出流口流量公式59 3.無因次量化62 5-2 流量歷線特性72 1.入、出流歷線試驗結果72 2. 入、出流歷線實際設計之比較73 3. 入、出流歷線特徵值79 5-3 滯洪池之水理特性93 1.洪峰消減93 2.洪峰稽延94 3.滯洪容積98 5-4 舉例說明121 第六章 結論與建議129 參考文獻133 附圖137 附表18

煙囪式出流口滯洪壩流量公式之實驗研究

The theoretical and experimental analysis were used for this research to investigate the discharge formula of detention dam with chimney outlet on hillslope. The outlet control section combine upper rectangle with inferior trapezoid. Theoretical discharge formulas were obtained from Bernoulli's equation, with the effective discharge coefficient for adjustment. Effective discharge coefficient and experimental discharge formulas were obtained by using the experiment of different chimney outlets. Under 5% channel bed slope, the discharge formulas of compound outlets with metric system are outlined as follows: (1) The water depth fall into trapezoidal section Q=2/3(0.505)2g ｛ bh-4/5mh ｝（ 0 ≦ h ≦ p ） (2) The water depth above trapezoidal section Q=2/3(0.505)2g ｛ bh-4/5m 〔 h+(h-p) 〕｝（ p < h ≦ H ） where Q(cms) is the discharge of outlet, h is the water depth of outlet, g is gravitational acceleration, b is the breadth at bottom of trapezoidal section, p is the height of trapezium, side slopes of m (horizontal): 1 (vertical) of the trapezoidal section, H is the height of outlet control section.本文旨在針對山坡地滯洪設施中，有關煙囪式出流口之流量公式做理論與實驗之 研究。所用之出流口係採上方矩形下方梯形斷面形狀之設計，並依據白努利方程式解析，引 入有效流量係數 Ce 為校正參數，推得理論流量公式；再配合不同梯形開口尺度進行渠槽試 驗，推算有效流量係數，提出實驗室經驗流量公式，期為山坡地開發設置滯洪設施之設計參 考。經由理論推導與實驗數據分析，獲致在渠床坡度 5% 之煙囪式出流口公制流量公式如下 ：（１）當水深位於梯形斷面內時 Q=2/3(0.505)2g ｛ bh-4/5mh ｝（ 0 ≦ h ≦ p ） （２）當水深高於梯形而達矩形斷面時 Q=2/3(0.505)2g ｛ bh-4/5m 〔 h+(h-p) 〕｝（ p < h ≦ H ） 式中Ｑ（ cms ）為出流口流量，h 為出流口水深， g 為重力加速度，b、p、m 分別為梯形 斷面之底寬、高度及側坡水平比值，H 為出流口斷面最大高度

[[alternative]]Computer Interface Technology and System Design for Blind Persons(I)

[[no]]計畫編號：NSC87-2213-E007-024[[date]]研究期間：199708~19980

Forecasting Model for Brown Planthopper Population Fluctuation and Its Effects on Rice Production in Taiwan

褐飛蝨(Nilaparvata lugens, Stål)為臺灣 最重要水稻害蟲，如防治不當常導致二期作 水稻產量及品質顯著下降。為達到水稻害蟲 經濟安全有效的管理，建立早期預警，作為 適時防治之依據，是為解決此一問題之重要 課題。本研究使用1988 至2012 年在嘉義分 所溪口農場所設置誘蟲燈每日捕捉褐飛蝨之 數據，利用1988-2003 年之數據分析，發現 資料在時間序列上存在高度的自我相關。首 先嘗試運用三情況門檻自我迴歸分析建立模 式，並以近9 年2004-2012 的資料來驗證預 測模式之準確性。模式之驗證分為長期與短 期預測，長期預測為進行長時間(約120-160 日)每日蟲數之預測，可評估二期作水稻生育 期間每日蟲數。結果顯示，族群發生動態與 實際趨勢大部分一致；短期預測之發展為評 估第1 至14 日後預測值之比較，結果顯示從 預測起始日第7 日後之預測值具有相當高的 穩健性，說明此模式可有效運用於7 日後之 族群發生動態趨勢。期望能以此研究的結 果，提供未來運用於防治決策的參考。 The brown planthopper (Nilaparvata lugens, Stål) is an important pest insect which affects rice production in Taiwan. Incorrect control strategies will reduce the quality and yield of rice production. The brown planthoppers can migrate to Taiwan every year from neighboring areas. Moreover, the immigration time and population abundance are often changeable. By a system of long-term monitoring of the insect, a time-series population fluctuation of brown planthopper can be recorded. The forecasting system for the outbreak time of brown planthoppers will provide early warning and information on chemical application for safe rice production. In this study, population fluctuations based on daily data collected from paddy fields and traps were monitored in Chiayi County from 1988 to 2012. Owing to the autocorrelation of the data, we analyzed them by using the three-regime threshold autoregressive (TAR) statistical model of time series. Firstly, the data from 1988 to 2003 was used to establish the prediction model. Secondly, the data from 2004 to 2012 were employed to test the validity of the predicted model. A long-term forecast provided 120-160 days of prediction after the first prediction date was used to estimate daily forecasting data in the second crop season. Results showed that most of the forecasting trends are near the trends of the observed data. For short-term forecasting, we used the results of one-day forecasting to those of fourteen-day forecasting to describe the precision of the forecasting model. The results indicated that the trend of seven-day forecasting is recommended. That is, the forecasting model could effectively estimate population fluctuations seven days in advance. Accordingly, the results of this study are applicable to be included in the plant protection measures