Preface

Chapter 7 157

Utilization of Digital Twins and Other Numerical Relatives for Efficient Monte Carlo Simulation in Structural Analysis by Bernt Johan Leira, Arifian Agusta and Sebastian Thöns

II

The Monte Carlo method is a numerical technique to model the probability of all possible outcomes in a process that cannot easily be predicted due to the interference of random variables. It is a technique used to understand the impact of uncertainty, ambiguity, and risk in forecasting models. This technique, also known as the Stochastic Simulation Technique, is now an established method used routinely in a wide variety of disciplines such as industry, nuclear engineering, medicine, economics, and risk analysis. However, this technique is not without complications, one of which is the amount of computer time required to achieve sufficient precision in the simulations and evaluate their accuracy. This book is organized into three sections and presents the general principles of the Monte Carlo method with an emphasis on techniques to decrease simulation time and increase accuracy.

Section 1 discusses the major fields of application of the Monte Carlo method in medicine. It covers a variety of topics, including medical physics, dosimetry, radiation protection, diagnostic radiology, radiotherapy, and nuclear medicine.

Section 2 introduces the theory and application of the Monte Carlo method in material science. This method is now widely applied in the design of complex materials and has become a vital tool in the field.

Section 3 provides practical information needed to support simulation and analysis of structures by numerical models and introduces techniques to reduce the computation time for even larger and more complex models.

Each section is subdivided into chapters and the implementation of the Monte Carlo method in each section is illustrated in several examples.

Chapter 1 introduces sampling techniques for a standard Monte Carlo method that could enable fast simulation of signals from optical coherence tomography (OCT) imaging systems. The chapter presents a standard Monte Carlo method for simulating OCT signals and sampling implementations that reduce computational time.

Chapter 2 discusses the calculation of the dosimetric parameters of encapsulated radioactive materials. In this chapter, Monte Carlo simulations are performed to determine the dosimetric parameters of the Palladium-103 brachytherapy seed. It also investigates the dose distributions along the central axis of COMS eye plaques loaded with the seeds. The chapter also examines the effects of plaque backing and polymeric insert on dose distribution at critical ocular structures.

Chapter 3 reviews the physics of small radiation fields, cavity theory, and the methodology of small field dosimetry. Different types of commercial dosimeters used in small field dosimetry are introduced and the importance of accurate small field dosimetry is discussed. This chapter also focuses on the application and importance of Monte Carlo techniques used in the field and presents recommendations of the Code of Practice for dosimetry of small radiation fields.

Chapter 4 describes the general concepts and basis of Monte Carlo modeling, introduces some available codes, and discusses the validation and reliability of Monte Carlo codes. It also examines the limitations on cross-section library and random number generators. The chapter presents a comparison between two Monte Carlo codes, XRMC and Geant4, and examines the validation between them. Experimental data applied to mammography are also presented. Finally, the chapter discusses the considerations in choosing a Monte Carlo computer toolkit and raises important issues on validation and reliability tests.

Chapter 5 presents algorithms, techniques, and general rules for Monte Carlo simulation in the plasma-enhanced chemical vapor deposition method. This is currently the method of choice for producing hydrogenated amorphous silicon thin films, which are promising materials for flat panel display transistors, solar cells, and electronic devices. But the reactions during plasma deposition are complex and not possible to observe directly, hence Monte Carlo simulation is a powerful tool to study thermodynamic properties.

Chapter 6 introduces the application of local information entropy in cluster Monte Carlo algorithms. The cluster Monte Carlo methods are very efficient in analysis of critical phenomena, for example, transformation from a ferromagnetic to a paramagnetic phase. However, in some conditions below the Curie point, this method produces incorrect results. To solve this problem, the chapter introduces a new simulation procedure that is efficient, leading to physically reliable results, especially for multiphase magnetic composites.

Chapter 7 deals with the analysis of structures. In general, structure analysis involves large and complex numerical models that require extensive computation efforts. One way to avoid this problem is to introduce simplified numerical models. This chapter discusses various types of approximate models and illustrates application of response surface techniques for an offshore jacket structure in combination with the Monte Carlo technique.

Section 1

Medicine

1

This book is an excellent contribution of numerous scientists and researchers from all around the world. I hope it encourages readers, scientists, and researchers to look deeper into the Monte Carlo Method and opens up several possibilities for further novel development.

As an editor, I express heartfelt appreciation to each and every contributing author and technical reviewer because this book could not have been written without their effort.

I would like to give very special thanks to IntechOpen for their support in editing this book, especially Ms. Sara Bacvarova. Her help with the publication process and friendly and prompt responses to my queries motivated me to work hard during eight months of preparing this book.

Section 1 Medicine

Chapter 1

Abstract

workstations.

1. Introduction

3

Monte Carlo Methods for

Ivan T. Lima Jr and Sherif S. Sherif

Simulation of Optical Coherence

We describe two importance sampling techniques for a standard Monte Carlo (MC) method that could enable fast simulation of signals from optical coherence tomography (OCT) imaging systems. These OCT signals are generated due to diffusive reflections from either multilayered or arbitrary shaped, turbid media, for

example, tissue. Such signals typically consist of ballistic and quasi-ballistic components, of scattered photons inside the medium, in addition to photons that undergo multiple scattering. We show that MC simulation of these OCT signals using importance sampling reduces its computation time on a serial processor by up to three orders of magnitude compared to its corresponding standard implementation. Therefore, these importance sampling techniques enable practical simulation of OCT B-scans of turbid media, for example, tissue, using commonly available

Keywords: optical coherence tomography, Monte Carlo simulation,

Optical coherence tomography (OCT) is a non-invasive sub-surface imaging technique that has experienced significant growth in biomedical applications [1, 2]. OCT systems could be implemented with a low-coherence light source and a mechanical scanning sub-system (time-domain OCT). More advanced systems use a low-coherence light source with a spectrometer or a wavelength-swept source (frequency-domain OCT). OCT has an imaging depth that could reach up to 3 mm, depending on the optical properties of the tissue, and it also has one to two orders of magnitude higher resolution than ultrasound imaging. OCT could also produce images inside the body when it is implemented using optical fiber probes. Unlike X-ray or gamma-ray imaging, OCT is safe for biological tissues because it utilizes

The signal obtained by an OCT imaging system consists of ballistic and quasi-ballistic (Class I diffuse reflectance) photons, in addition to multiply scattered

photons (Class II diffuse reflectance), that are reflected from tissue [3].

light transport in turbid media, importance sampling

non-ionizing radiation mainly in the infrared spectrum.

1.1 OCT signal simulation using a Monte Carlo method

Tomography of Turbid Media
