A MACHINE LEARNING STUDY TO PREDICT WIND-DRIVEN WATER RUNBACK CHARACTERISTICS PERTINENT TO AIRCRAFT ICING PHENOMENA - Chapter 2 - dissertation contents

Aug 5, 2026

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A dissertation develops machine learning methods to predict wind-driven water runback characteristics relevant to aircraft icing phenomena. The research integrates learning models with detailed datasets and evaluates performance using multiple error metrics. It presents training and prediction procedures, including forecasting evolution of the front contact point and the film thickness distribution. Comparisons among LightGBM, MLP, and a ConvLSTM-autoencoder architecture support discussion of results and conclusions for improved icing-related flow prediction.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages147

Reading Time6 h 10 min

AccessFree to download

Chapter 1. General Introduction

Aircraft Icing

Machine Learning in Fluid Mechanics

Motivation and Dissertation Outline

Chapter 2. A Machine Learning Study to Predict Wind-Driven Water Runback Characteristics Pertinent to Aircraft Icing Phenomena

Introduction

Machine Learning Methods and Datasets

Results and Discussions

Conclusions

Chapter 3. A Physics-Guided Fourier Neural Operator for the Prediction of Water Film Flow for Aircraft Icing Applications

A machine learning study of wind-driven runback/flow-off multiphase flows pertinent to aircraft icing phenomena by Jincheng Wang A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Aerospace Engineering Program of Study Committee: Hui Hu, Co-major Professor Ping He, Co-major Professor Anupam Sharma Jue Yan Zhengyuan Zhu The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2024 Copyright © Jincheng Wang, 2024. All rights reserved. DEDICATION This dissertation is dedicated to my beloved wife, Liujing, and my parents, who have been an unwavering source of support and encouragement throughout this journey. Nobody ever figures out what life is all about, and it doesn't matter. Explore the world. Nearly everything is really interesting if you go into it deeply enough. From Richard P. Feynman TABLE OF CONTENTS Page LIST OF FIGURES ...................................................................................................................... vii LIST OF TABLES ......................................................................................................................... xi ACKNOWLEDGMENTS ............................................................................................................ xii ABSTRACT................................................................................................................................. xiii CHAPTER 1. GENERAL INTRODUCTION........................................................................... 1 1.1 Aircraft Icing................................................................................................................... 1 1.1.1 Icing Physics ............................................................................................................... 1 1.1.2 Environmental Parameters for Aircraft Icing ............................................................. 3 1.1.3 Icing Effect on Aerodynamic Performance of Aircraft .............................................. 5 1.1.4 Icing Effect on Different Phases of Flight .................................................................. 6 1.1.5 Wind-driven Runback Water Transport and Film Dynamics ..................................... 8 1.1.6 Predictions on Aircraft Icing..................................................................................... 10 1.1.7 Aircraft Ground Icing and Ground Deicing Technique ............................................ 10 1.2 Machine Learning in Fluid Mechanics ......................................................................... 13 1.2.1 Machine Learning Fundamentals.............................................................................. 13 1.2.2 Machine Learning Applications in Aircraft Icing..................................................... 17 1.3 Motivation for Current Research .................................................................................. 19 1.4 Outline of the Dissertation ............................................................................................ 21 References ................................................................................................................................. 22 CHAPTER 2. A MACHINE LEARNING STUDY TO PREDICT WIND-DRIVEN WATER RUNBACK CHARACTERISTICS PERTINENT TO AIRCRAFT ICING PHENOMENA ..... 31 Abstract ..................................................................................................................................... 31 2.1 Introduction .............................................................................

FAQs

What problem does the dissertation address?

It addresses predicting wind-driven water runback/flow-off multiphase behavior that is pertinent to aircraft icing phenomena, with emphasis on measurable flow characteristics.

Which machine learning models are investigated?

The work includes LightGBM, an MLP model, and a ConvLSTM-autoencoder architecture, trained and validated using experimental datasets.

What key outputs are forecasted in the study?

The dissertation forecasts the evolution of the front contact point and the film thickness distribution associated with the icing-relevant water film dynamics.

Aug 5, 2026

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