Experimental study of the injection angle impact on the column Waves: Wavelength, frequency and drop size

Kasmaiee, Si.; Tadjfar, Mehran

doi:10.1016/j.expthermflusci.2023.110989

Cited by 11 publications

(1 citation statement)

References 32 publications

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“…The initial study on modeling float characteristics using an ML algorithm is done by predicting the drag force of the float using input data obtained from a numerical simulation [8]. Additionally, the current application of ML in the aerospace field not only employs numerical data but also incorporates numerical data alongside image data [9]. To simplify the discussion, this paper focuses on the numerical data only.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio

Fahmi,

Fajar,

Atmaja

et al. 2024

IJ-AI

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Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

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Section: Introductionmentioning

confidence: 99%