research results based on simulation are approximate results that may indicate the impact of a certain parameter's modification on the system's performance. Mathematical methods may prove the model's mechanism. If we a mathematical model and its simulation, we could be able to test and analyze the results easily.

In academic institutions , researchers issues directions by simulating their idea or by way of mathematical models, there are lots of constraints may be in the form of funds or in the form of time or any other administrative problem for conducting experiments. Commercial r&d houses may think differently. Therefore, simulations play important role in the life of researchers from academic institutions.

It appears as though there are some questions about the importance and/or validity of simulations. I just gave an opinion on the merits of simulation (https://www.researchgate.net/post/What_are_the_purposes_of_the_simulation_methods_applications_in_engineering); you may want to check it out. Furthermore, with regards to accuracy, depending on the complexity of the problem there may not be an analytical solution and thus a numerical approximation is sought to try to determine the performance of the proposed methodology. Simulations represent a cost-effective (in time and money) strategy to test solutions. I believe that, ultimately, a simulation should lead to a successful physical implementation. I often tell control theory and instrumentation students that simulations are an important part of the design process; however, when it comes to building the actual system there are many factors that influence the overall system performance. Thus the experience gained from practical work can be used to go back to the simulation stage, to include factors that may not had been taken into account. Accurate knowledge of system/process performance can then be used to enhance future simulation results.

Sometimes it is not possible to describe the behavior of a system using closed form solutions or through mathematical expressions. The reasons might be that either the system is too complex to analyze or an analytical model simply approximates its performance. In the first case, one can study the performance of the system by carrying out simulations. In the second case, one can validate the results obtained using the analytical model with those obtained by carrying out simulations. Simulations provide an alternate means of studying the behavior or performance of a system without actually deploying it. If the performance of the system comes out to be satisfactory, one may recommend it to be deployed physically. If the performance of the system does not come out to be satisfactory, one may rethink incorporating necessary modifications to done to improve the performance of the system without incurring a huge cost involved in physical deployment.

On the other hand, if one has carried out simulations, one may think of "Can I describe the performance of the system using an analytical model?" Thereafter, one can see the variations among the values obtained using analytical model and through simulations.

(A good simulator is that tries to create an environment that is close to the environment of deployment.)

25 to30 years back, I could developed ," One cycle inrush blocking relay........and microprocessor based one cycle relay "by simulation and mathematical modeling for 220KV and 132KV CT performance, practically by prototype, it was not possible.

In my opinion, with 14 years of experience in the field of telecommunications, with installation and commissioning of equipment operation. These compared to 5 years in teaching in engineering, I conclude that simulations are a way of seeing things in a controlled and ideal environment. I think the simulation is valid and enriching educational way in certain areas.

But NO they are a concrete way, for those situations where environmental conditions are often capricious and random. Where at the same time requires robust solutions by designers and engineers in real life practice.

Research results based on simulations are useful in the real world only if the models used in the simulations truly represent the real world system being simulated. If this is the case, then simulation studies will provide a cost effective means of assessing the performanc of the system under sonsideration. The extent to which the modelled system represents the real world system will depend on the competence of the researcher perofrming the modelling task.

Poobie Govender - Durban University of Technology

With 20+ years of experience in EE:

it is not important whether you have simulation results, closed form solutions or mathematical expression ('model' in general) - - they are invalid until validated by experiment.

The reason is that whatever model you use tends to simplify things. Or may even be faulty. Thus validation by experiment is required to check for correctness.

Too often I heard of problems 'solved' in simulation; the problems are still available as reality proofed the simulation results to be faulty.

From the engineering perspective, I totally agree with you "U. Dreher". Because, engineering is all about solving real life problems, with real life scenarios. Simulation and analytical approaches abstract plenty of "real life" complexities and parameters. However, science can only evolve by breaking up the "big" problems into smaller ones. Take an example the Euclidean Geometry, without the abstraction of objects into simple lines and points, all engineering would never progress. Knowing that, the concept of perfect lines and points does not exist in the real world.

In Electrical.Computer science,Electronics and Communication(digital signal processing), simulation based research has given very good results, Cleaver researchers has solved, many problems,with software based simulation techniques.without spending,for prototype modeling, Refer my paper," Microprocessor based one cycle relay............." I developed 25 years back, the first wave shape approached relay, totally on results obtained by mathematical modeling and simulation.

To get first hand information about the system, simulation is the ideal choice. With reconfiguration systems playing pivotal role, the basic hardware has to accommodate new technologies, which is why Software Defined Radio has come into existence. This as pointed by many researchers lead to cost-effective solutions.

U Dreher wrote:

"Too often I heard of problems 'solved' in simulation; the problems are still available as reality proofed the simulation results to be faulty."

It also happens that simulation says that the system will not work, when in reality the physical implementation does work.

'The map is not the territory.'

Simulation can occasionally be useful, but consider the human factor: people who make a living on simulation will advocate it.

Simulations require two things:

1. Verification -- steps taken to ensure the internal workings of the simulation are correct.

2. Validation -- steps taken to show that the outputs of the simulation are rational and correct.

The second step is crucial. Take Anthropogenic Global Warming (AGW) theory for instance. Many AGW simulations made temperature predictions that have been grossly different from empirical temperature measurements. In this case, the simulations usefulness is severely limited to one conclusion: temperature variations on earth are not well understood, and the prosect of hidden variable(s) cannot be discounted.

And large amounts of money are being made on Anthropogenic Global Warming simulations..

Al Gore gets paid tens of thousands of dollars to fly around in a private jet polluting the air to warn about simulated AGW.

The conclusion that simulations are worth luxurious compensation, regardless of their validity, is empirically validated.