Level of detail and degree of fidelity of a model

“A simulator’s scope refers to its breadth”[28]: it means that the capabilities of the simulator is strictly related with what subset of the real world it is able to represent, with the number and the quality of its models for every real-world component or phenomenon, with the number and type of interactions and interfaces of the model that carry out to easily aggregate components and treat them as large elements. Clearly, the most detailed, complex simulators are broad and highly resolved. However, a deep difference passes between detail and fidelity. It is natural to think that modeling everything in excruciating detail will most closely match real performance: often it is wrong and detrimental. Sometimes a detailed simulator just turns out wrong, whereas a simpler, more easily understood simulator closely matches real performance.

Details may even produce “noise” that keeps away the user from seeing simple relationships. Adding detail can also demand more data, and the data may be wrong. Cause-effect relationships among objects in a simulator are crucial. The trick is to identify details that matter and ignore ones that don’t affect the results: following this way the simulator’s outputs assess an increasing degree of fidelity. The tendency is to simulate as much detail as possible given the time available, even if some of that detail is unnecessary.

The choice of a model and its level of detail changes in relation to:

 the product life cycle phase. in the first phase of a SE process the virtual models are the only

models involved in the simulation activities. Their core consists of few basic rules and equations that explain the main behavior of the modeled element, with low details and a degree of fidelity sufficient to make feasibility studies and have an idea of the evolution and the quantity of the main parameters for a preliminary sizing. During the design phases, the global architecture of the system and the environment is better rather than the details of a single model (e.g. the sensors, the actuators and the electrical devices as well as the effects of environment phenomena models update the global model wrt the feasibility phase). The control is based on model simplifying both the controller and the observer design (e.g. the design of the controller is made thanks to linearized equation of the system and verified on the non linearized model). In the verification phase, the level of detail of the models increases

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because all the equipment are well defined, the real capabilities of the system are investigated against the actual environmental conditions, and the control performances shall be guaranteed for different situations (e.g. disturbances action). The last system model has the highest possible degree of fidelity.

 the supportable computation complexity. This issue depends on the features and

performances of the simulation machine. As said before, a higher level of detail requires higher resources for support the operations of complex models, based on high order equations, accurate solvers and meticulous definition of the work logics. All these characteristics tend to decrease the speed of simulation and they can compromise real time activities. That leads to pay attention about the definition (for the developers) and the choice (for the users) of the detailed models, especially when SIL, CIL, and HIL are performed.

 the required accuracy of the results. Complex and detailed models generally produce

accurate results: it means that the values obtained for the state variables and other main parameters of the system take evolutions and trends that reflect the real behavior. However, apart for the validation purposes, the best accuracy is not necessary. Moreover, in relation to running session, particular performances and functions are more investigated than others: it should allow to the user the selection of models with various levels of detail within the same simulation architecture in order to reach different degrees of fidelity within the same run. For example, evaluating the performance of a chaser in the mating phase, position and attitude accuracy become the focus features that should be available with the highest fidelity for the users and analysts. At the same time it should be negligible to investigate the fuel consumption (and consequently the sloshing whose model can be at lowest level of detail or, even, removed).

 the costs & resources: virtual models cost and require less human resources and items with

respect to the physical ones. Programmatic requirements address and constraint the choice of the models

 the instrumentation and facilities availability. The choice depends on the availability and the in-house models. In particular for physical models (also for the virtual) it can be very cost effective and quick to use already available models wrt develop it as new. This is also because the model is just tested, integrate and, probably, validated with the simulator.

 the parametrization. One of the main goals for the model designer is to produce a flexible model and the parameterization are the main features that shall be sought. Parameterization is the process of deciding and defining the parameters necessary for a complete or relevant specification of a model. Most often, parameterization is a mathematical process involving the identification of a complete set of effective coordinates or degrees of freedom of the model, without regard to their utility in some design. In other words, the parameterization carries out to define constants and variables characterizing a model: a priori settable parameters in the case of constants (as calibration values) or parameters that evolve in time and determine the behavior of the model. Changing constant parameters, the intrinsic features of a model vary; changing variables parameters change the entire model property.

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