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    • 32. 发明公开
    • GAS TURBINE ENGINE OPTIMIZATION CONTROL DEVICE
    • VORRICHTUNG ZUR OPTIMIERUNGSSTEUERUNG EINES GASTURBINENMOTORS
    • EP3034841A4
    • 2017-05-03
    • EP14836204
    • 2014-03-12
    • IHI CORP
    • FURUKAWA HIROYUKIKINOSHITA MOE
    • F02C9/44F02C7/057F02C9/16F02C9/18F02C9/22F02C9/28F02C9/54G05B13/02
    • F02C9/54F02C9/22F02C9/44F05D2270/20F05D2270/709G05B13/021G05B13/024
    • A gas turbine engine optimization control device estimates a specific fuel consumption (SFC) using a given control parameter of a variable mechanism, determines a change between a specific fuel consumption estimation value by the control parameter of the variable mechanism in a previous operation period and a specific fuel consumption estimation value by the control parameter of the variable mechanism in this operation period, determines a new control parameter (LPTVSV_SEARCHPOINT) of the variable mechanism with which the specific fuel consumption estimation value approaches a minimum, adds the new control parameter of the variable mechanism to a preset control parameter initial value, and sets the addition value to be a control parameter command value (LPTVSVREF) of the variable mechanism in a next operation period.
    • 燃气涡轮发动机优化控制装置使用可变机构的给定控制参数来估计特定燃料消耗量(SFC),并且确定由前一运行时间段中的可变机构的控制参数产生的特定燃料消耗量估计值和 根据该运转期间中的可变机构的控制参数来设定燃料消耗率推定值,决定使燃料消耗率推定值接近最小的可变机构的新的控制参数(LPTVSV_SEARCHPOINT),将该变量的新的控制参数 机制转换为预设的控制参数初始值,并将相加值设置为下一个操作周期中可变机构的控制参数命令值(LPTVSVREF)。
    • 37. 发明公开
    • Method and system for modeling the performance of a gas turbine engine
    • Verfahren und System zur Modellierung der Leistung einer Gurburbine
    • EP2336918A1
    • 2011-06-22
    • EP10252149.9
    • 2010-12-17
    • United Technologies Corporation
    • Yerramalla, Sampath K.Butler, Steven WayneDonat, William
    • G06F17/50G05B17/02
    • G06F17/5009F02C9/00F05D2270/709G05B17/02G06F2217/16
    • A method for modeling the performance of a gas turbine engine is provided. The method includes the steps of: 1) providing a processor; 2) inputting flight condition parameter data and engine output parameter data into a gas turbine engine model operating on the processor, which model includes a physics-based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residuals from the engine output parameter data and the estimated engine output parameter data; 3) partitioning the flight condition parameter data and residuals into training data and testing data; 4) performing a correlation reduction on the training data, which analysis produces correlation adjusted training data; 5) performing an orientation reduction on the correlation adjusted training data, which reduction produces orientation adjusted training data; 6) reviewing the orientation adjusted training data relative to at least one predetermined criteria, and iteratively repeating the steps of performing a correlation reduction and an orientation reduction using the orientation adjusted training data if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data; 7) producing estimated corrections to the orientation adjusted training data using one or more neural networks; 8) evaluating the neural adjusted data using the partitioned testing data; and 9) modeling the performance of the gas turbine using the estimated corrections to the orientation adjusted training data.
    • 提供了一种用于对燃气涡轮发动机性能进行建模的方法。 该方法包括以下步骤:1)提供处理器; 2)将飞行条件参数数据和发动机输出参数数据输入到在处理器上运行的燃气涡轮发动机模型中,该模型包括基于物理的发动机模型,其使用飞行状态参数数据来产生估计的发动机输出参数数据,并且确定残差 从发动机输出参数数据和估计的发动机输出参数数据; 3)将飞行条件参数数据和残差划分为训练数据和测试数据; 4)对训练数据进行相关性降低,该分析产生相关调整训练数据; 5)对相关调整的训练数据进行取向减少,该减少产生取向调整的训练数据; 6)相对于至少一个预定标准检查取向调整训练数据,并且如果不满足标准,则迭代地重复执行使用定向调整训练数据进行相关减小和取向减少的步骤,并且如果满足标准输出 方向调整训练数据; 7)使用一个或多个神经网络来产生针对所调整的训练数据的估计校正; 8)使用分区测试数据评估神经调整数据; 以及9)使用所估计的校正校正训练数据对燃气轮机的性能进行建模。