Mechanics simulation is a powerful tool for designing, testing, and optimizing mechanical systems and components. But how do you manage a mechanics simulation project effectively? In this article, you will learn some practical tips and best practices for planning, executing, and reviewing your mechanics simulation project.
Before you start your mechanics simulation project, you need to have a clear idea of what you want to achieve and how you will measure your success. What are the main questions or problems that you want to address with your simulation? What are the assumptions and constraints that you have to consider? What are the expected outcomes and deliverables of your project? Having a well-defined scope and objectives will help you to avoid scope creep, prioritize your tasks, and communicate your expectations to your stakeholders.
state assumptions, define the physics, and choose key parameters in the model Not all simulations are FEA-based. It can be multi-physics. So, first step is to get a model and define the parameters in the simulation. Decoupling always helps in finding a better solution. also simulation needs to be validated.
Depending on your objectives and scope, you will need to select the most suitable simulation software and methods for your project. There are many types of mechanics simulation software available, such as finite element analysis (FEA), computational fluid dynamics (CFD), multibody dynamics (MBD), and discrete element method (DEM). Each software has its own advantages and limitations, so you need to evaluate them based on your needs and budget. You also need to decide on the level of detail and accuracy that you want to achieve with your simulation, and the trade-offs between speed and complexity.
Once you have chosen your simulation software and methods, you need to prepare your data and models for your simulation. This may involve collecting, cleaning, and validating your data, creating or importing your geometry and mesh, defining your material properties and boundary conditions, and setting up your solver and analysis parameters. You need to ensure that your data and models are consistent, reliable, and realistic, and that they reflect your objectives and scope. You also need to document your data and models, and keep track of any changes or updates.
After you have prepared your data and models, you are ready to run your simulation. Depending on the size and complexity of your simulation, this may take from minutes to hours or days. You need to monitor your simulation progress and performance, and check for any errors or warnings that may occur. You may also need to run multiple simulations with different scenarios or parameters, to test your hypotheses or optimize your design. You need to save and backup your simulation results, and organize them in a logical and accessible way.
The next step is to analyze and interpret your simulation results, and compare them with your objectives and expectations. You need to use appropriate tools and techniques to visualize, quantify, and evaluate your results, such as graphs, charts, tables, statistics, and reports. You need to identify and explain any trends, patterns, or anomalies that you find in your results, and relate them to your questions or problems. You also need to assess the validity and reliability of your results, and acknowledge any limitations or uncertainties that may affect your conclusions.
The final step is to communicate and apply your findings to your stakeholders and audience, such as your clients, colleagues, or supervisors. You need to present your findings in a clear, concise, and convincing way, using appropriate formats and media, such as slides, videos, or posters. You need to highlight the main insights and implications of your findings, and how they can help to solve your questions or problems, or improve your design or performance. You also need to provide recommendations and suggestions for further actions or improvements, based on your findings.
So far we have described the basic steps to having a solution, but in the simulation of real-world problems, there are two important steps missing: calibration and validation of the model. Calibration is when you modify your inputs to fit your output to a real-world data point. This step is performed in order to fine-tune your model. Now, the most important step is the validation phase, where you compare your model results with experimental data. The important distinction is that in the validation phase, the simulation analyst is blind to the results.