Abstract:
A system and method of controlling clearance in a turbomachine (10) includes adjusting the machine case cooling (33) air in response to the difference between the desired clearance and the actual clearance. An accurate estimate of the actual clearance is made with a real time mathematical model on-board engine controller (40). The model computes thermal growth of the turbomachine components (41, 43, 50) each with a difference equation derived from a closed form solution to the 1 order differential equation obtained through the application of 1 law of thermodynamics. The resulting equation is conveniently formulated in terms of equivalent time constant and steady state growth both correlated with thermo-physical characteristics of multiple fluid streams (48) exchanging heat with the component (41, 43, 50). The solution is applied over a time step of the control software. Approximating coefficients are strategicay placed in the model to allow calibration of the model to a particular version of the engine hardware.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.
Abstract:
Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.