Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Simulating airfoil self-noise using ANNs

The noise generated by an airfoil is due to the interaction between a turbulent airflow and the aircraft's airfoil blades. Predicting the acoustic field in these situations requires an aeroacoustics methodology that can operate in complex environments. Additionally, the method that is used must avoid the formulation of coarse hypotheses regarding geometry, compactness, and the content of the frequency of sound sources. The prediction of the sound generated by a turbulent flow must, therefore, correctly model both the physical phenomena of sound propagation and the turbulence of the flow. Since these two phenomena manifest energy and scales of very different lengths, the correct prediction of the sound generated by a turbulent flow is not easy to model.

Aircraft noise is a crucial topic for the aerospace industry. The NASA Langley Research Center has funded several strands of research to effectively study the various mechanisms of self...