Python For Mechanical And Aerospace Engineering
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Python For Mechanical And Aerospace Engineering
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Author : Alex Kenan
language : en
Publisher: Alex Kenan
Release Date : 2021-01-01
Python For Mechanical And Aerospace Engineering written by Alex Kenan and has been published by Alex Kenan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-01 with Computers categories.
The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io
Python For Mechanical And Aerospace Engineering
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Author : Alexander Kenan
language : en
Publisher:
Release Date : 2020-12-13
Python For Mechanical And Aerospace Engineering written by Alexander Kenan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-13 with categories.
Learn some of Python's most popular libraries like BeautifulSoup, Matplotlib, Numpy, Requests, and Tkinter by scraping a website for aluminum alloy information, graphing satellite orbits, creating a GUI to convert different units, plotting airfoil coordinates, publishing programs to PDF, and more!
Python For Mechanical And Aerospace Engineering
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Author : Alexander Kenan
language : en
Publisher:
Release Date : 2020-12-13
Python For Mechanical And Aerospace Engineering written by Alexander Kenan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-13 with categories.
Learn some of Python's most popular libraries like BeautifulSoup, Matplotlib, Numpy, Requests, and Tkinter by scraping a website for aluminum alloy information, graphing satellite orbits, creating a GUI to convert different units, plotting airfoil coordinates, publishing programs to PDF, and more!
Computational Nuclear Engineering And Radiological Science Using Python
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Author : Ryan McClarren
language : en
Publisher: Academic Press
Release Date : 2017-10-27
Computational Nuclear Engineering And Radiological Science Using Python written by Ryan McClarren and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-27 with Technology & Engineering categories.
Computational Nuclear Engineering and Radiological Science Using Python provides the necessary knowledge users need to embed more modern computing techniques into current practices, while also helping practitioners replace Fortran-based implementations with higher level languages. The book is especially unique in the market with its implementation of Python into nuclear engineering methods, seeking to do so by first teaching the basics of Python, then going through different techniques to solve systems of equations, and finally applying that knowledge to solve problems specific to nuclear engineering. Along with examples of code and end-of-chapter problems, the book is an asset to novice programmers in nuclear engineering and radiological sciences, teaching them how to analyze complex systems using modern computational techniques. For decades, the paradigm in engineering education, in particular, nuclear engineering, has been to teach Fortran along with numerical methods for solving engineering problems. This has been slowly changing as new codes have been written utilizing modern languages, such as Python, thus resulting in a greater need for the development of more modern computational skills and techniques in nuclear engineering. Offers numerical methods as a tool to solve specific problems in nuclear engineering Provides examples on how to simulate different problems and produce graphs using Python Supplies accompanying codes and data on a companion website, along with solutions to end-of-chapter problems
Machine Learning For Engineers
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Author : Ryan G. McClarren
language : en
Publisher: Springer Nature
Release Date : 2021-09-21
Machine Learning For Engineers written by Ryan G. McClarren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-21 with Technology & Engineering categories.
All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.
Finite Element Methods In Civil And Mechanical Engineering
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Author : Arzhang Angoshtari
language : en
Publisher: CRC Press
Release Date : 2020-12-09
Finite Element Methods In Civil And Mechanical Engineering written by Arzhang Angoshtari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-09 with Technology & Engineering categories.
The finite element method is widely employed for numerical simulations in engineering and science due to its accuracy and efficiency. This concise introduction to the mathematical theory of the finite element method presents a selection of applications in civil and mechanical engineering including beams, elastic membranes, the wave equation, heat transfer, seepage in embankment, soil consolidation, incompressible fluids, and linear elasticity. Jupyter notebooks containing all Python programs of each chapter can be downloaded from the book's companion website. Arzhang Angoshtari is an assistant professor and Ali Gerami Matin is a graduate student, both in the department of Civil and Environmental Engineering at the George Washington University, USA. Their research interests cover theoretical and computational mechanics and finite element methods.
Uncertainty Quantification And Predictive Computational Science
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Author : Ryan G. McClarren
language : en
Publisher: Springer
Release Date : 2018-11-23
Uncertainty Quantification And Predictive Computational Science written by Ryan G. McClarren and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-23 with Science categories.
This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.
Programming The Raspberry Pi Getting Started With Python
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Author : Simon Monk
language : en
Publisher: McGraw Hill Professional
Release Date : 2012-11-23
Programming The Raspberry Pi Getting Started With Python written by Simon Monk and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-23 with Computers categories.
Program your own Raspberry Pi projects Create innovative programs and fun games on your tiny yet powerful Raspberry Pi. In this book, electronics guru Simon Monk explains the basics of Raspberry Pi application development, while providing hands-on examples and ready-to-use scripts. See how to set up hardware and software, write and debug applications, create user-friendly interfaces, and control external electronics. Do-it-yourself projects include a hangman game, an LED clock, and a software-controlled roving robot. Boot up and configure your Raspberry Pi Navigate files, folders, and menus Create Python programs using the IDLE editor Work with strings, lists, and functions Use and write your own libraries, modules, and classes Add Web features to your programs Develop interactive games with Pygame Interface with devices through the GPIO port Build a Raspberry Pi Robot and LED Clock Build professional-quality GUIs using Tkinter
Substructuring In Engineering Dynamics
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Author : Matthew S. Allen
language : en
Publisher: Springer
Release Date : 2019-08-01
Substructuring In Engineering Dynamics written by Matthew S. Allen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-01 with Technology & Engineering categories.
This book reviews the most common state-of-the art methods for substructuring and model reduction and presents a framework that encompasses most method, highlighting their similarities and differences. For example, popular methods such as Component Mode Synthesis, Hurty/Craig-Bampton, and the Rubin methods, which are popular within finite element software, are reviewed. Similarly, experimental-to-analytical substructuring methods such as impedance/frequency response based substructuring, modal substructuring and the transmission simulator method are presented. The overarching mathematical concepts are reviewed, as well as practical details needed to implement the methods. Various examples are presented to elucidate the methods, ranging from academic examples such as spring-mass systems, which serve to clarify the concepts, to real industrial case studies involving automotive and aerospace structures. The wealth of examples presented reveal both the potential and limitations of the methods.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Shailendra Kadre
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Shailendra Kadre and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory