Prediction of Non-Uniform Distorted Flows, Effects on Transonic Compressor Using CFD, Regression Analysis and Artificial Neural Networks

Sohail, Muhammad; Hamdani, Hossein; Islam, Ashraful; Parvez, Khalid; Khan, Abdul Salam; Allauddin, Usman; Khurram, Muhammad; Elahi, Hassan

doi:10.3390/app11083706

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…Furthermore, it was demonstrated that making use of GBPNN provides the highest accuracy, followed by the SVM and BPNN. In another work, Sohail et al (2021) focused on the forecasting tool of transonic compressor instability. In this regard, they utilized a dataset obtained from CFD for supervised learning of an ANN.…”

Section: Characteristics Of Axial Compressormentioning

confidence: 99%

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Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Pakatchian

Ziamolki²,

Nazari³

2023

Front. Energy Res.

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A compressor is one of the key components of a gas turbine engine and its performance and characteristics significantly affect the overall performance of the engine. Axial flow compressors are one of the most conventional types of compressors and are widely used in turbine engines for large-scale power generation. Intelligent techniques are useful for numerical simulation, characterization of axial compressors, and predicting their performance. The present work reviews studies applying different intelligent methods for performance forecasting and modeling different aerodynamic aspects of axial compressors. Corresponding to the outcomes of the considered research works, it can be expressed that by using these methods, axial compressors can be characterized properly with acceptable exactness. In addition, these techniques are useful for performance prediction of the compressors. The accuracy and performance of these methods is impacted by several elements, specifically the employed method and applied input variables. Finally, some suggestions are made for future studies in the field.

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Section: Characteristics Of Axial Compressormentioning

confidence: 99%

“…FIGURE 3Inputs and outputs of the developed model for the compressor with distorted flow bySohail et al (2021).…”

mentioning

confidence: 99%

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Pakatchian

Ziamolki²,

Nazari³

2023

Front. Energy Res.

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“…The flow field in the transonic micro-axial flow compressor's tip clearance zone was previously studied under non-uniform flow circumstances. The research focused on the negative effects of a steady-state distorted pattern of total pressure inlet flow conditions, both under-design and off-design RPM [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Ahmed,

Sohail,

Javed

et al. 2023

Applied Sciences

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Parametric cycle analysis, an on-design engine study, specifies the required design characteristics that optimize engine performance. This study aimed to conduct a parametric analysis of a low-bypass turbofan engine with an afterburner, F100-PW229, and develop a technique for estimating its performance based on data using machine learning and deep learning. Commercially available gas turbine simulation software, GasTurb 14, was used to create a dataset of engine performance response variables and input design parameters. The effects of the Mach number, fan pressure ratio, altitude, turbine entry temperature, and bypass ratio on the specific thrust, propulsive efficiency, specific fuel consumption, and total fuel flow were investigated. Regression learning models and deep neural networks were then programmed on this dataset to predict responses for new input data. In MATLAB, a total of 24 regression models were trained with cross-validation, and the model with the least root mean square error was selected as the final model. The machine learning regression models produced reliable output parameter predictions, with the least root mean square error of 9.076 × 10−5. Among the numerous regression models tested, Gaussian process regression, the quadratic support vector machine, and the wide neural network emerged to be the most successful in predicting turbofan engine performance metrics. A multilayer perceptron model was coded in Python with two hidden layers that accurately predicted the performance parameters. The mean square error value on test data was found to be as low as 0.0046. In comparison to intensive computational simulations, machine learning and deep learning models offer an efficient method for conducting parametric analysis of turbofan engines.

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“…NN-based controllers are proposed by Yu et al [24] to ensure steady-state spacecraft formation when in orbit. Therefore, similarly to [25], surrogate and NN models are beneficial when problems are computationally intensive and already have numerous data sets available. For the purposes of this research, the numerical calculations are not computationally intensive; therefore, the benefits of such techniques are minimized and non-gradient-and gradient-based algorithms can be directly applied.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Optimization Techniques and Objective Functions Using Gas Generator and Staged Combustion LPRE Cycles

et al. 2022

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This paper compares various optimization techniques and objective functions to obtain optimum rocket engine performances. This research proposes a modular optimization framework that provides an optimum design for Gas Generator (GG) and Staged Combustion (SC) Liquid Propellant Rocket Engines. This process calculates the ideal rocket engine performance by applying seven different optimization techniques: Simulated Annealing (SA), Nelder Mead (NM), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Pigeon-Inspired Optimization (PIO), Genetic Algorithm (GA) and a novel hybrid GA-PSO technique named GA-Swarm. This new technique combines the superior search capability of GA with the efficient constraint matching capability of PSO. This research also compares objective functions to determine the most suitable function for GG and SC cycle rocket engines. Three single objective functions are used to minimize the Gross Lift-Off Weight and to maximize Specific Impulse and the Thrust-to-Weight ratio. A fourth multiobjective function is used to simultaneously maximize both Specific Impulse and Thrust-to-Weight ratio. This framework is validated against a pump-fed rocket, and results are within 1% of the actual rocket engine mass. The results of this research indicate that PSO and GA-Swarm produce optimum results for all objective functions. Finally, the most suitable objective function to use while comparing these two cycles is the Gross Lift-Off Weight.

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Prediction of Non-Uniform Distorted Flows, Effects on Transonic Compressor Using CFD, Regression Analysis and Artificial Neural Networks

Cited by 12 publications

References 33 publications

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Comparison of Optimization Techniques and Objective Functions Using Gas Generator and Staged Combustion LPRE Cycles

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