Simulation In Data Science📊📈👨🏻‍💻🌐

What is Simulation?

Data simulation is taking a large amount of data and using it to simulate or mirror real-world conditions to either predict a future instance, determine the best course of action or validate a model.

Benefits of simulation in data science:

• Tune up performance
• Optimize a process
• Improving safety
• Testing theories
• Training staffs
• Gain Insights for Process Improvement
• Reduce the cost of R & D by providing a virtual environment to play on

Similar technologies like simulation:

Digital twin is a digital representation of a physical object, process or service, it can be a replica of a physical world object such as a jet engine or wind farm, or even larger items such as buildings or even whole cities.

Why is Simulation Used?

• It provides a safe virtual environment to play around with different parameters
• Plant people can use simulation to assess the performance of an existing system or predict the performance of a planned system, comparing alternative solutions and designs
• It can be used as an alternative to testing theories and changes in the real world, which can be costly

What all can be simulated?

Any system or process that has a flow of events can be simulated (if you can draw a flowchart of the process, you can simulate it)

Types of Simulation

1. Discrete Event Simulation

• Modeling a system as it progresses through time

• It is a process of defining a complex system as an ordered sequence of well-defined events
• Common applications of DES include stress testing, modeling procedures, and processes in various industries, such as manufacturing and healthcare

Example :

An office building uses electricity based on the passing of time. Any electrical device in the building turning on or off is one discrete event that can affect this function. Instantaneous state changes in running equipment, such as a cooling fan’s speed change or a desk lamp’s brightness adjustment, are also considered discrete events.

The following qualities are a minimal need for an efficient DES process:

• Predetermined beginning and ending points, which may be distinct occurrences or time instants.
• A way to keep track of how much time has passed since the procedure started.
• A rundown of all distinct events that have happened since the procedure started.
• A list of distinct occurrences that are anticipated or pending (if known) before the process is anticipated to end.
• A visual, numerical, or tabular representation of the function that DES is presently doing.

2. Dynamic Simulation

• Modeling a system as it progresses through space

***https://upload.wikimedia.org/wikipedia/commons/e/e7/TRUE_Procedural_Animation.gif***

In this example the crank is driving, we vary both the speed of rotation, its radius, and the length of the rod, and the piston follows.

Example :

Dynamic process models are utilized to conduct multiple what-if scenarios with only minor modifications. For a pressure relief scenario, for example, the dynamic model can be used to evaluate multiple relieving cases such as fire, blocked discharge, utility failure, runaway reaction, or control valve failure. With the help of an event scheduler, multiple processes and upset scenarios can be programmed into the same model and then executed separately. Event schedulers are a standard feature of commercial dynamic simulation programs.

3. Process Simulation

• Modeling physical interactions between two or more systems
• Process simulation is a model-based representation of chemical, physical, biological, and other technical processes and unit operations in software

For more clear picture: ***https://upload.wikimedia.org/wikipedia/commons/4/43/Dwsim_20_windows.jpg***

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