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首页> 外文会议>AIAA/ASME/SAE/ASEE joint propulsion conference;AIAA propulsion and energy forum >Using Density Estimation in Comparing Input Signals for Gas Turbine Engine Transient Models
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Using Density Estimation in Comparing Input Signals for Gas Turbine Engine Transient Models

机译：在比较燃气轮机瞬态模型的输入信号中使用密度估计

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Gas turbine engines are already complex and nonlinear systems. Nevertheless, gas turbines must become more complex and nonlinear over time to meet the more challenging requirements in the future. Consequently, the different gas turbine engine model types used in the gas turbine design and development processes must become more complex and nonlinear over time. However, simpler models will still be derived from the complex and nonlinear models for applications such as: controller design. Also, the complex and nonlinear models can be run in transient to generate data for the faster data driven transient models. An input signal is used either to test a derived simpler model or generate transient data for a data driven model. As the models get more complex and nonlinear, typical input signals which test the simpler models or create data for the data driven models may not be enough anymore. Conventionally, the input signals are designed in the frequency domain to excite the frequency range of interest. The designed input signals are composed of small perturbations about different operating points and transitions between these different operating points with relatively larger perturbations. How much perturbation is small or large is determined heuristically. However, comprehensive model testing or data generation requires certainty in covering the feasible state space boundary and interior. Concepts, such as boundary and interior do not have equivalents in the frequency domain. Therefore, a supplementary approach considering the feasible state space geometry is necessary for evaluating the designed input signals in terms of feasible state space coverage. This paper proposes using density estimation to evaluate the designed input signals. Among the density estimation methods, this paper used the K-Nearest-Neighbors (kNN) approach. kNN method calculates the smallest volume which encompasses k number of the closest neighboring points about a density estimation point in a given space. For the demonstration in gas turbines, the sea level static (SLS) condition was chosen. The approach can be extended to different flight conditions. First, the feasible state space boundary was determined at SLS by comparing how much of the feasible state space was enveloped by the steps and chops between idle and maximum thrust in different demand types. The signal which enveloped the most of the feasible state space was chosen as the boundary. Naturally, the signal path points of the boundary defining signal were selected as the boundary density estimation points. Then, a Latin hypercube space filling technique with a thousand points was used to populate the interior of the feasible state space with density estimation points. After determining the density estimation points, a small subset of the input signals in the gas turbine literature were evaluated using the kNN method and the selected density estimation points. Each signal's average closeness to the density estimation points were calculated. Receiving the expected results verified the proposed approach and observations were made to improve the tested signals further.
机译：燃气涡轮发动机已经是复杂的非线性系统。然而，燃气轮机必须随着时间的流逝变得更加复杂和非线性，以满足未来更具挑战性的要求。因此，随着时间的流逝，在燃气涡轮机设计和开发过程中使用的不同燃气涡轮发动机模型类型必须变得更加复杂和非线性。但是，仍然可以从复杂的非线性模型中获得更简单的模型，例如：控制器设计。同样，复杂的和非线性的模型可以瞬态运行，以生成用于更快的数据驱动的瞬态模型的数据。输入信号用于测试派生的简单模型或为数据驱动的模型生成瞬态数据。随着模型变得越来越复杂和非线性，测试简单模型或为数据驱动模型创建数据的典型输入信号可能已不再足够。常规上，在频域中设计输入信号以激发感兴趣的频率范围。设计的输入信号由围绕不同操作点的小扰动和具有较大扰动的这些不同操作点之间的过渡组成。启发式确定大小扰动的大小。但是，全面的模型测试或数据生成需要确定可行的状态空间边界和内部范围。边界和内部等概念在频域中没有等效项。因此，考虑可行状态空间的几何形状，需要一种考虑可行状态空间几何形状的补充方法来评估设计的输入信号。本文提出使用密度估计来评估设计的输入信号。在密度估计方法中，本文使用K最近邻（kNN）方法。 kNN方法计算最小体积，该最小体积包含围绕给定空间中的密度估计点的k个最邻近点。为了在燃气轮机中进行演示，选择了海平面静态（SLS）条件。该方法可以扩展到不同的飞行条件。首先，通过比较不同需求类型中的空转和最大推力之间的阶跃和斩波包络了多少可行状态空间，在SLS上确定了可行状态空间边界。选择包围了大部分可行状态空间的信号作为边界。自然地，选择边界定义信号的信号路径点作为边界密度估计点。然后，使用具有一千个点的拉丁超立方体空间填充技术，用密度估计点填充可行状态空间的内部。确定密度估计点后，使用kNN方法和选定的密度估计点对燃气轮机文献中的一小部分输入信号进行了评估。计算每个信号与密度估计点的平均接近度。收到预期结果，验证了所提出的方法，并进行了观察以进一步改善测试信号。

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