Real-time detection and control of the isolator shock-train leading edge (STLE) is important to the performance of high-speed air-breathing engines, such as dual-mode scramjets. Typically, the STLE location is determined using wall static-pressure measurements, but there are often restrictions on the placement and overall number of the pressure transducers, reducing the viability and accuracy of such approaches. To address these issues, we introduce the adaptive pressure profile (APP) method for estimating the STLE location. This method does not require extensive prior characterization of the isolator or engine model. Instead, it uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile and subsequently uses this deduced profile to estimate the STLE location in a data-driven manner. The APP method works well in situations with sparse transducer placement. It produces accurate estimates when the STLE location is 1) not bounded by two or more transducers or 2) between two transducers that are several isolator duct heights apart. We demonstrate the efficacy of the APP method using simulations and experimental data from direct-connect isolator models. This validation shows that the APP method is accurate and robust for different flow regimes, transducer configurations, and model geometries.

隔离器冲击列车前缘 (STLE) 的实时检测和控制对于高速吸气式发动机（例如双模超燃冲压发动机）的性能非常重要。通常，STLE 位置是使用壁静压测量确定的，但压力传感器的放置和总数通常存在限制，从而降低了此类方法的可行性和准确性。为了解决这些问题，我们引入了用于估计 STLE 位置的自适应压力分布 (APP) 方法。这种方法不需要对隔离器或发动机模型进行大量的先验表征。相反，它使用来自少数传感器的实时压力测量来自适应地学习隔离器压力分布，然后使用此推导的分布以数据驱动的方式估计 STLE 位置。APP 方法在传感器放置稀疏的情况下效果很好。当 STLE 位置 1) 不受两个或多个换能器的限制或 2) 位于相距几个隔离器管道高度的两个换能器之间时，它会产生准确的估计值。我们使用来自直接连接隔离器模型的模拟和实验数据证明了 APP 方法的功效。此验证表明 APP 方法对于不同的流态、传感器配置和模型几何形状是准确和稳健的。我们使用来自直接连接隔离器模型的模拟和实验数据证明了 APP 方法的功效。此验证表明 APP 方法对于不同的流态、传感器配置和模型几何形状是准确和稳健的。我们使用来自直接连接隔离器模型的模拟和实验数据证明了 APP 方法的功效。此验证表明 APP 方法对于不同的流态、传感器配置和模型几何形状是准确和稳健的。