Some Issues in the Testing of Computer Simulation Models

The testing of simulation models has much in common with testing processes in other types of application involving software development. However, there are also important differences associated with the fact that simulation model testing involves two distinct aspects, which are known as verification and validation. Model validation is concerned with investigation of modelling errors and model limitations while verification involves checking that the simulation program is an accurate representation of the mathematical and logical structure of the underlying model. Success in model validation depends upon the availability of detailed information about all aspects of the system being modelled. It also may depend on the availability of high quality data from the system which can be used to compare its behaviour with that of the corresponding simulation model. Transparency, high standards of documentation and good management of simulation models and data sets are basic requirements in simulation model testing. Unlike most other areas of software testing, model validation often has subjective elements, with potentially important contributions from face- validation procedures in which experts give a subjective assessment of the fidelity of the model. Verification and validation processes are not simply applied once but must be used repeatedly throughout the model development process, with regressive testing principles being applied. Decisions about when a model is acceptable for the intended application inevitably involve some form of risk assessment. A case study concerned with the development and application of a simulation model of a hydro-turbine and electrical generator system is used to illustrate some of the issues arising in a typical control engineering application. Results from the case study suggest that it is important to bring together objective aspects of simulation model testing and the more subjective face- validation aspects in a coherent fashion. Suggestions are also made about the need for changes in approach in the teaching of simulation techniques to engineering students to give more emphasis to issues of model quality, testing and validation

Some Issues in the Testing of Computer Simulation Models

The testing of simulation models has much in common with testing processes in other types of application involving software development. However, there are also important differences associated with the fact that simulation model testing involves two distinct aspects, which are known as verification and validation. Model validation is concerned with investigation of modelling errors and model limitations while verification involves checking that the simulation program is an accurate representation of the mathematical and logical structure of the underlying model. Success in model validation depends upon the availability of detailed information about all aspects of the system being modelled. It also may depend on the availability of high quality data from the system which can be used to compare its behaviour with that of the corresponding simulation model. Transparency, high standards of documentation and good management of simulation models and data sets are basic requirements in simulation model testing. Unlike most other areas of software testing, model validation often has subjective elements, with potentially important contributions from face- validation procedures in which experts give a subjective assessment of the fidelity of the model. Verification and validation processes are not simply applied once but must be used repeatedly throughout the model development process, with regressive testing principles being applied. Decisions about when a model is acceptable for the intended application inevitably involve some form of risk assessment. A case study concerned with the development and application of a simulation model of a hydro-turbine and electrical generator system is used to illustrate some of the issues arising in a typical control engineering application. Results from the case study suggest that it is important to bring together objective aspects of simulation model testing and the more subjective face- validation aspects in a coherent fashion. Suggestions are also made about the need for changes in approach in the teaching of simulation techniques to engineering students to give more emphasis to issues of model quality, testing and validation

Digital Twin for Legacy Systems: Simulation Model Testing and Validation

In this paper, an approach to incorporate a digitaltwin for legacy production systems is presented. Hardware-in-the-loop setups are routinely used by manufacturing companiesto carry out virtual commissioning. However, manufacturingcompanies having online legacy production systems are stillstruggling to incorporate a digital twin due to the absence ofverified and validated simulation models. Companies that usevirtual commissioning as a part of their engineering tool chain,usually perform offline verification of the simulation model.This approach is typically based on visual inspection and is atedious task as each aspect of the model has to be visuallyvalidated. For legacy systems, only assessing the behaviorvisually in the absence of updated documents can result in anincorrect simulation model, i.e. simulating incorrect behaviorwith respect to the specification. Due to this, such simulationmodels cannot be incorporated in the engineering tool chain,as the simulated results can lead to improper decisions and caneven cause equipment damage. This paper presents a platformand an approach, based on model-based testing, that is a firststep for manufacturing companies to incorporate a validatedsimulation model for existing online production systems thatwill serve as a digital twin

ASPECTS ABOUT SIMULATED MODEL TRUSTINESS

Nowadays, grace of computing possibilities that electronic computers offer and namely, big memory volume and computing speed, there is the improving of modeling methods, an important role having complex system modeling using simulation techniques. These osimulation model, validation, sensitivity analysis

Modulasi Digital Menggunakan Matlab

In telecommunication systems, the determination of the modulation system is an important method in the process of sending information from transmitter to receiver. In the simulation that is done using QPSK modulation system (Quadrature Phase Shift Keying), 8-QAM (Quadrature Amplitude Modulation), 16-QAM (16-Quadrature Amplitude Modulation) that uses AWGN (Additive White Gaussian Noise) channel in the transmission system uses MATLAB software. The execution of the simulation is aimed to describe how the characteristics of the waveform of each block of the modulator, to add noise in the AWGN channel and demodulator. Performance of modulation system testing is used BER (bit error ratio) method. Looking of the faults of comparison bits results of before and after the transmitted bits by using Monte Carlo simulation model. Testing on this simulation using the input data as much as 10.000 data symbols randomly and the level of Eb/No that is various for each modulation used. Performance results BER with the level of Eb / No at 1 dB of the simulated system modulation on the BER values obtained for QPSK 0.0570, 8-QAM at 0.1085 while the 16-QAM at 0.1582 and then the performance of QPSK modulation is the best. If the Eb / No is increased to 8 dB then the becomes BER QPSK smaller modulation is equal to 0.00035, the 8-QAM BER obtained at 0.0076, while the 16-QAM modulation to be 0.013

DENGUE OUTBREAK: A SYSTEM DYNAMICS APPROACH

Dengue is an endemic disease that occurs across Malaysia with a large number of fatality cases being recorded each year. The Ministry of Health Malaysia has came out with the national dengue strategic plan in controlling the disease outbreak. Therefore, this study aims to develop a system dynamics model of dengue to help policy makers in simulating the effectiveness of the public health intervention. Simulation model can show the successes and failures of past policies, as well as predicting the consequences of selected policy proposals before their implementation. From this, future research can be done in order to enhance the existing intervention or to prompt new strategy in overcoming the problem

Modeling Diffusion of Information in an Increasingly Complex Digital Domain

Offering entertainment, discussion, and information, social media provides users with a stimulating online experience. Within the last five years, it has also become an increasingly popular medium for the consumption of news. News outlets publish articles and reports through social media, and by doing so influence their users in a way that corresponds with the outlet’s political leaning. Because social media outlets provide users with tailored content, the prevalence of biased news reporting reinforces the user’s political values and polarizes their beliefs. This thesis attempts to examine the relationships that give rise to this political polarization in social media and discusses possible opportunities to mitigate it

Microwave remote sensing of soil water content

Microwave remote sensing of soils to determine water content was considered. A layered water balance model was developed for determining soil water content in the upper zone (top 30 cm), while soil moisture at greater depths and near the surface during the diurnal cycle was studied using experimental measurements. Soil temperature was investigated by means of a simulation model. Based on both models, moisture and temperature profiles of a hypothetical soil were generated and used to compute microwave soil parameters for a clear summer day. The results suggest that, (1) soil moisture in the upper zone can be predicted on a daily basis for 1 cm depth increments, (2) soil temperature presents no problem if surface temperature can be measured with infrared radiometers, and (3) the microwave response of a bare soil is determined primarily by the moisture at and near the surface. An algorithm is proposed for monitoring large areas which combines the water balance and microwave methods