Survey of Advancements in Jet-Engine Thermodynamic Simulation

Sanghi, Vivek; Lakshmanan, B.; Sundararajan, V.

doi:10.2514/2.5644

Cited by 20 publications

(16 citation statements)

References 43 publications

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“…The two partial derivatives needed for the Newton solver are given by Eqs. (23) and (24). These two derivatives are augmented with the two following additional derivatives:…”

Section: Multidisciplinary Derivativesmentioning

confidence: 96%

“…To date, gradient-free methods have been successful because the number of design variables required to formulate the problem has been kept relatively small, but the integration of propulsion analysis with viscous CFD requires a large number of design variables, which makes gradient-based optimization a requirement [22]. Sandhi et al [23] identified many different options in their survey of propulsion modeling tools, but none computed analytic derivatives. Of the tools identified, numerical propulsion system simulation (NPSS) [24] is the most recently developed and is widely used.…”

mentioning

confidence: 99%

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Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

Gray

Chin

Hearn

et al. 2017

Journal of Propulsion and Power

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“…The two partial derivatives needed for the Newton solver are given by Eqs. (23) and (24). These two derivatives are augmented with the two following additional derivatives:…”

Section: Multidisciplinary Derivativesmentioning

confidence: 96%

mentioning

confidence: 99%

Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

Gray

Chin

Hearn

et al. 2017

Journal of Propulsion and Power

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“…The high fidelity non realtime model is used to generate the appropriate data to train these models. This is by no means an all inclusive list [17], but it can be used as a good reference lor other real-time engine modeling applications.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System

Kestner

Tai

Mavris

2011

Journal of Engineering for Gas Turbines and Power

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This paper presents a computationally efficient methodology for generating training data for a transient neural network model of a tip-jet reaction drive system for potential use as an onboard model in a model based control application. This methodology significantly reduces the number of training points required to capture the transient peiformance of the system. The challenge in developing an onboard model for a tip-jet reaction drive system is that the model has to operate over the whole flight envelope, to account for the different dynamics present in the system, and to adjust to system degradation or potential faults. In addition, the onboard model must execute in less time than the update interval of the controller. To address these issues, a computationally efficient training methodology and neural network surrogate model have been developed that captures the transient peiformance of the tip-jet reaction system. As the number of inputs to a neural net^'ork becomes large, the computational time needed to generate the number of training points required to accurately represent the range of operating conditions of the system may become quite large also. A challenge for the tip-jet reaction drive system is to minimize the number of neural network training points, while maintaining the high accuracy. To address this issue, a novel training methodology is presented which first trains a steadystate neural network model and uses deviations from steady-state operating conditions to define the transient portion of the training data. The combined results from both the transient and the steady-state training data can then be used to create a single transient neural network of the system. The results in this paper demonstrate that a transient neural network using this new computationally efficient training methodology has the potential to be a feasible option for use as an onboard real-time model for model based control of a tip-jet reaction drive system.

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“…The literature on the dynamic modelling of gas turbines is substantial. Sanghi et al [11] appear to have undertaken the most recent review of the field, and the reader is directed to this work for a detailed discussion of the numerous approaches in the literature. Broadly speaking, dynamic gas turbine models can be classified as either phenomenological (e.g.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Physics-Based, Dynamic Model of a Gas Turbine

Wiese

Blom

Brear

et al. 2013

Volume 4: Ceramics; Concentrating Solar Power Plants; Controls, Diagnostics and Instrumentation; Education; Electric Power; Fan

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This paper presents and validates a physics-based, dynamic model of a gas turbine. The model is an extension of that proposed by Badmus et al. [1], such that representation of a complete gas turbine is achieved. It includes new models of several gas turbine components, in particular the turbine and compressor, and also applies a well known method for prescribing boundary conditions [10] to the gas path. This model first uses data from a previously published, static model of the same gas turbine to determine this dynamic model’s many so-called ‘forcing terms’. A least-squares optimisation is then undertaken to estimate the shaft inertia and the thermal inertia of system components using transient test data. Importantly, these optimised results are all close to physically reasonable estimates. Further, they show that the shaft dynamics are only significant for a short period at the start of most transients, after which the dynamic effects of thermal storage are dominant. The complete gas turbine model is then validated against transient test data. Whilst the simulated traces demonstrate some steady-state error arising from the static model [12], the overall system dynamics appear to be captured well. Since steady-state error can be integrated out in a control system, this suggests that the proposed dynamic model is appropriate for use in a model-based, gas turbine controller.

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Survey of Advancements in Jet-Engine Thermodynamic Simulation

Cited by 20 publications

References 43 publications

Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System

Development and Validation of a Physics-Based, Dynamic Model of a Gas Turbine

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