Simulated model validation

Tao WQ, Min CH, Liu XL et al (2006) Parameter sensitivity examination and discussion of PEM fuel cell simulation model validation part I. Current status of modeling research and model development. J Power Sources 160 359-373... 

The effects of confinement due to matrix species on the PMF between colloids is very well seen in Fig. 1(c). At a small matrix density, only the solvent effects contribute to the formation of the PMF. At a higher matrix density, the solvent preserves its role in modulating the PMF however, there appears another scale. The PMF also becomes modulated by matrix species additional repulsive maxima and attractive minima develop, reflecting configurations of colloids separated by one or two matrix particles or by a matrix particle covered by the solvent layer. It seems very difficult to simulate models of this sort. However, previous experience accumulated in the studies of bulk dispersions and validity of the PY closure results gives us confidence that the results presented are at least qualitatively correct. 

Fang X, Stefan HG, Alam SR (1999) Simulation and validation of fish thermal DO habitat in north-central US lakes under different climate scenarios. Ecol Model 118 167-191... 

The validity of the model is tested against the experiment. A ISOOcc canister, which is produced by UNICK Ltd. in Korea, is used for model validation experiment. In the case of adsorption, 2.4//min butane and 2.4//min N2 as a carrier gas simultaneously enter the canister and 2.1//min air flows into canister with a reverse direction during desorption. These are the same conditions as the products feasibility test of UNICK Ltd. The comparison between the simulation and experiment showed the validity of our model as in Fig. 5. The amount of fuel gas in the canister can be predicted with reasonable accuracy. Thus, the developed model is shown to be effective to simulate the behavior of adsorption/desorption of actual ORVR system. 

The ORVR system is an important subsystem which reduces the contamination of evaporative fuel gas at gas station during the fueling. In this paper, a simulation model of adsoiption and desorption of evaporative fuel gas in canister of ORVR system is developed. From the comparison between the simulations and experiments, the validity of the developed model is verified and the dynamics can be predicted. This PDE model can be used to design the canister of ORVR system effectively for diverse climate and operating conditions. 

Decision analytic models, or simulation models of clinical decision analysis, usually involve the creation of a treatment decision/outcome tree based on a synthesis of expert opinion, sometimes using validated methods of canvassing opinion such as recruiting a Delphi panel (Hatziandreu et al, 1994 Einarson et al, 1995). The decision tree... 

The PBPK model for a chemical substance is developed in four interconnected steps (1) model representation, (2) model parametrization, (3) model simulation, and (4) model validation (Krishnan and Andersen 1994). In the early 1990s, validated PBPK models were developed for a number of toxicologically important chemical substances, both volatile and nonvolatile (Krishnan and Andersen 1994 Leung 1993). PBPK models for a particular substance require estimates of the chemical substance-specific... 

In comparing the May storms of 1978 and 1976, clearly the simulated concentration values in Figure 3 are more representative of what actually occurred than the observed values. This is not meant to be a criticism of the sampling program but an indication of how errors in observed data can exist and impact the model validation process. 

The implemented model must be tested with regard to correctness and completeness. Therefore, i.e., to validate the model and ensure the credibility of the simulation results, suitable scenarios with a broad spectrum of different events are reproduced with the model and compared to reality (or to expectations on reality). A model validated successfully can then be used for several systematic experiments (or as part of other applications, e.g., as part of a MES). 

Modeling and validation require the close cooperation of all parties involved in the project. Further success factors in simulation modeling include adequate planning experience, special experience with simulation tools, and the ability to think in abstract structures. 

Sargent, R.G. (1982) Verification and validation of simulation models, in Progress in Modeling and Simulation... 

Model validation requires confirming logic, assumptions, and behavior. These tasks involve comparison with historical input-output data, or data in the literature, comparison with pilot plant performance, and simulation. In general, data used in formulating a model should not be used to validate it if at all possible. Because model evaluation involves multiple criteria, it is helpful to find an expert opinion in the verification of models, that is, what do people think who know about the process being modeled ... 

Model validation is a process that involves establishing the predictive power of a model during the study design as well as in the data analysis stages. The predictive power is estimated through simulation that considers distributions of PK, PD, and study-design variables. A robust study design will provide accurate and precise model-parameter estimations that are insensitive to model assumptions. 

The sensitivity of diffusion-model output to variations in input has been assessed by workers at Systems Applications, Inc., and at the California Department of Transportation. In each case, reports are in preparation and are therefore not yet available. It is important to distinguish between sensitivity and model performance. True physical or chemical sensitivity that is reflected by the simulation-model equations is a valid reflection of reality. But spurious error propagation through improper numerical integration techniques may be r arded as an artificial sensitivity. Such a distinction must be drawn carefully, lest great sensitivity come to be considered synonymous with unacceptable performance. 

Direct numerical simulation is expected to play a more dominant role for analytical treatment of turbulent flames. In addition to capturing physical phenomena, the authors feel that a very powerful role of DNS is its capability for model validations. In fact, in most of our modeling activities, DNS has been the primary means of verifying specific assumptions and/or approximations. This is partially due to difficulties in laboratory measurements of some of the correlations and also in setting configurations suitable for model assessments. Of course, the overall evaluation of the final form of the model requires the use of laboratory data for flows in which all of the complexities are present. 

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