Dear Ayman,

Computational simulation analysis offers significant advantages over experimental methods in several key areas:

The increasing availability of advanced computational tools, like machine learning frameworks and high-performance computing, is shifting researchers’ preferences toward simulation analysis. These tools provide accessible, scalable solutions (e.g., CFD or neural network-based models) that offer high accuracy with lower resource demands, especially for complex systems where physical experiments are logistically challenging. This trend is evident in fields like environmental engineering, where I’ve seen simulations outperform traditional methods in predicting environmental critical episodes.

Cost, time, and feasibility are rarely the deciding factors in choosing whether to go experimental or computer modeling. Marketers pushing expensive software want you to think that their product can answer every question but these don't assure accuracy or that you get the optimal outcome.

Computational simulation models have many advantages, but are based in real data usually obtained first by experimental methods.

In my experience as engineer, I remember two different cases related with the election between the use of a computational model or develop a set of experimental tests.

The first one refers to the calculation of the thermal balance of a power plant. At that time, the thermal balances were done by hand iterations and each iteration took several days to complete it and understand the real behavior of the plant. Also, at that time, computational models began to appear to solve this problem so we developed our own model that performed a large number of iterations in minutes.

The second one, refers to the calculation of the flow and cavitation coefficients of the multi-hole plates. There was no information in the technical literature about this subject, so several real tests of this type of plates were carried out in the plant, getting the searched coefficients.