It is often required in engineering practice to carry out series of numerical experiments, for example to analyze dynamical behavior and sensitivity of mechanical system or to find out optimal parameters of the system. The built-in module UM Experiments includes a set of tools (scanning, optimization, approximation), for advanced analysis of dynamics of mechanical systems.

All tools automate fulfillment of series of numerical experiments, record course of experiments and save results of experiments on a hard disk for posterior analysis. Thus, the designer is released from monotonous execution of series of numerical experiments "manually" what saves working hours and removes errors, which people unfortunately incline to do. In other words, the researcher defines the design of experiments for scanning and approximation or parameters, its limits and precision and goal function for optimization. Then the project is started and executed automatically. Current process statistics is available during the execution: number of experiments done, time left. Series of numerical experiments are resistant to shut-down of power supply. In that case all results are saved on a hard disk and results of the latter experiments are only lost. There is a possibility to plot an oscillogram of any saved performance of dynamical behavior of a mechanism. Moreover, the designer can plot so-called summary graphs and surfaces. All implemented tools have no limitation in number of parameters. In other words, they all are multi-parametrical. Please, note that the demo version allows describing the projects of scanning (optimization, approximation) with no more than one parameter. Dimensions of projects are set by the designer so as to solve the specify problem. But on the other hand the designer has to take into account calculating efforts that are necessary for the project. Every tool has its own merits and demerits. However they all give the designer possibilities to solve quite many problems devoted to optimization of mechanical systems.

After the execution of series of numerical experiments for scanning the problem of choosing the optimal parameters arises. How can the designer find out the best solution from lots of alternatives? It becomes much more difficult in the case of conflicting criteria. In order to help the designer to formalize her/his opinion the special tool, which helps the designer to sort alternatives according to her/his opinion about optimality, was developed. This tool is based on the analytic hierarchy process by Tomas L. Saaty. It is a multi-criteria method, which supports weighting of criteria, conflicting criteria and verification of correctness of expert's opinion.

There is a special extension of the module - service of distributed calculations. It allows using all computational powerful of a network for execution of series of numerical experiments that decreases time efforts correspondingly. This possibility is very easy and effective to use in computer centers and laboratories. Server of distributed calculations is based on using TCP/IP that allows employing any computer not only in local network, but also in Intra- and Internet for the needs of your project.

See also:
   1. Getting Started: UM Experiments Module
   2. User's Manual: UM Experiments Module
   3. Installation of the client part of the service of distributed calculations um40client.exe

Designer describes parameters to scan (as a rule, it is geometrical, inertia, stiffness and damping parameters), limits and step size for each parameter and starts series of numerical experiments. Scanning saves time history of all selected dynamical performances on a hard disk. These performances are available after the execution of the project. There are special possibilities for scanning dynamical behavior of railway vehicles: with various rail and wheel profiles, various tracks (tangent tracks and curves), various railway track irregularities. Scanning gives us full information about the response surface and the global optimum. It is usually enough to solve an optimization problem. On the other hand, scanning is very time-consuming process so as it is not practically used for problems with dimension more than 4-5. Scanning supports service of distributed calculations that decreases time efforts considerably.

This tool is based on a number of classical optimization methods: Hook-Jeevse's, Nealder-Mead's, Pawell's etc. The advantage of this tool is quite low computational efforts. On the other hand the tool has some disadvantages. Firstly, there is always a possibility that the method stops in one of the local optimums and does not find the global one. Secondly, all what the designer has after this kind of optimization does not give the shape of the objective function, it gives just several points on it. So the designer cannot get general overview of the dynamical behavior of the system. However it is well-known that objective functions of real mechanical systems are quite smooth, otherwise it would be high parameter sensitivity of the system that should not take place in real systems.