System Model Validation Research Articles

Calibration and validation of a solar thermal system model in Modelica

Recent advancements in the domain of modeling physical processes offer opportunities to use equation based modeling environments, such as Modelica, for the simulation of building heating, ventilation, and air-conditioning (HVAC) systems. The current work demonstrates Modelica capabilities in a case study of real building solar thermal system simulation. The simulated system is part of an innovative ENERGYbase building, designed according to the so called Passivhaus standard. Model calibration and validation procedure is developed to include optimization based parametric adjustments of component models using the monitoring data during a single week. The calibrated system adequately reproduces half a year of real system operation. Future work will concentrate on application of the developed calibration and validation methodology in the whole year overall building energy simulation.

SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identication, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Dierential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identication simultaneously while DE is utilized only for linear parameter identication. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling prob- lems used in the literature. The results obtained show the eectiveness of the proposed method.

Validation of subject-specific cardiovascular system models from porcine measurements

A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE).Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (Pao, Ppa) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), Pao, and Ppa measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models.The inputs (GEDV, SV, Pao, Ppa) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (∼5–10%). These results validate the potential for implementing this approach in the intensive care unit.

Research on Simulation and Modeling of Split Double-Rotor Motor Based on Indirect Vector Control

A novel vector control system of the split double-rotor motor based on indirect vector control principle has been proposed. The mathematic models of the primary machine and the secondary machine are set up respectively. Through the coupling of the two models, the system model of the split double-rotor motor is build. Matlab software is used for the simulation and analysis of this indirect vector control system. According to the simulation results, the validity of system model is proved with nicer static and dynamic capability, and without steady-state error in stable state. These are important to analyze and design the double-rotor motor. Furthermore, the results provide the basis for simulation and apply of double-rotor motor in HEV.

Research on System Evaluation of Top Management-Team Based on the Method of Analytical Hierarchy Process and Rough Set

Top management team (TMT) who are in charge of the right of decision-making and control, determine the rise and fall of the enterprise. Therefore, it is important to research systematically TMT. This paper, on the basis of research results at home and abroad, establishes evaluation index system of TMT from four levels: productivity, TMT composition, adaptability and effectiveness. By use of the theory of FAHP and Rough Set, it builds up evaluation model of TMT system, and inspects the validity and effectiveness of index system and evaluation model of TMT system through the way of empirical study.

Co-Modeling of Embedded Networks Using SystemC and SDL

Today, SDL and SystemC are two very popular languages for embedded systems modeling. SDL has specific advanced features that make it good for reflection of the multi-object systems and interactions between modules. It is also good for system model validation. The SystemC models are better suitable for tracing internal functions of the modeled modules. The hypothetical possibility of combined use of these two languages promises a number of benefits for researchers. This article specifically addresses and discusses the integration of SDL and SystemC modeling environments, exchange the data and control information between the SDL and SystemC sub-modules and the real-time co-modeling aspects of the integrated SDL/SystemC system. As a result, the mechanisms of SDL/SystemC co-modeling is presented and illustrated for an embedded network protocols co-modeling case study. The article gives an overview and description of a co-modeling solution for embedded networks protocols simulation based on experience and previous publications and research.

Simulation algorithm and validation of a photovoltaic water pumping system model using long-term field data

A photovoltaic (PV) water pumping system has been modelled using TRNSYS considering a power supply, a direct current (DC)/alternating current (AC) inverter, an AC induction motor, a centrifugal pump, a well, a water storage tank, and an overflow protection device. The model emulates actual pumping head curves, uses numerical methods to determine pump-system working points, and includes a storage tank as a component in the system. It simulates PV-powered water pumping systems, constant voltage tracking, voltage frequency modulation, and maximum power point tracking algorithms; variable inverter frequency with variable motor efficiency, variable inverter frequency with constant motor efficiency, and constant inverter frequency with variable motor efficiency algorithms. It also has the facility to calculate the accumulated unused part of the PV energy owing to the pump reaching its maximum capacity limit. Model validation was accomplished by comparing simulated results with both the laboratory measurements and long-term field data from Jordan; good match and accuracy have been achieved.

Assessment of the controlling envelope of a model-based multivariable controller for vapor compression refrigeration systems

Abstract This paper explores the controlling characteristics of a first-principles model-based controller specially developed for vapor compression refrigeration systems. Mathematical sub-models were put forward for each of the system components: heat exchangers (condenser and evaporator), variable-speed compressor and variable-orifice electric expansion device. The dynamic simulation model was then used to design a multivariable controller based on the linear-quadratic-Gaussian technique using a Kalman filter for the estimator design. A purpose-built testing apparatus comprised of a variable-speed compressor and a pulse-width modulated expansion valve was used to collect data for the system identification, and model and controller validation exercises. It was found that the model reproduces the experimental trends of the working pressures and power consumption in conditions far from the nominal point of operation (±30%) with a maximum deviation of ±5%. Additional experiments were also performed to verify the ability of the controller of tracking reference changes and rejecting thermal load disturbances. It was found that the controller is able to keep the refrigeration system running properly when the thermal load was changed from 340 to 580 W (460 W nominal), and the evaporator superheating degree was varied from 9.5 °C to 22 °C (16.6 °C nominal).

English

In-flight icing is a major concern in aircraft safety and a non-negligible source of incidents and accidents, and is still a serious hazard today. It remains consequently a design and certification challenge for aircraft manufacturers. The aerodynamic performance of an aircraft can indeed degrade rapidly when flying in icing conditions, leading to incidents or accidents. In-flight icing occurs when an aircraft passes through clouds containing super cooled water droplets at or below freezing temperature. Droplets impinge on its exposed surfaces and freeze, causing roughness and shape changes that increase drag, decrease lift and reduce the stall angle of attack, eventually inducing flow separation and stall. This hazardous ice accretion is prevented by the use of dedicated anti-icing systems, among which hot-air-types are the most common for turbofan aircraft. A three-dimensional unsteady thermodynamic simulation model is used to describe the dynamic response of an aircraft wing anti-icing system. This computational fluid dynamics based model involves a complete wing segment including thermal anti-icing bay inside the leading edge. The unsteady, integrated internal/external thermal flow simulation is presented with heat conductivity through the solid skin in a fine mesh. The calculated skin temperature results are satisfactory in their good match with flight test data. The presented research work indicates a strong potential of using computational fluid dynamics in dynamic wing anti-icing system model development and validation.

A method for determining the robustness of bio-molecular oscillator models

BackgroundQuantifying the robustness of biochemical models is important both for determining the validity of a natural system model and for designing reliable and robust synthetic biochemical networks. Several tools have been proposed in the literature. Unfortunately, multiparameter robustness analysis suffers from computational limitations.ResultsA novel method for quantifying the robustness of oscillatory behavior to parameter perturbations is presented in this paper. This method relies on the combination of Hopf bifurcation and Routh-Hurwitz stability criterion, which is widely applied in control system design. The proposed method is employed to calculate the robustness of two oscillating biochemical network models previously analyzed in the literature. The robustness bounds here obtained are tighter than what was previously obtained in the literature for both models.ConclusionThe method here proposed for quantifying the robustness of biochemical oscillator models is computationally less demanding than similar multiparamter variation techniques available in the literature. It also provides tighter bounds on two models previously analyzed in the literature.

A glucose-insulin pharmacodynamic surface modeling validation and comparison of metabolic system models

Metabolic system modeling for model-based glycaemic control is becoming increasingly important. Few metabolic system models are clinically validated for both fit to the data and prediction ability. This research introduces a new additional form of pharmaco-dynamic (PD) surface comparison for model analysis and validation. These 3D surfaces are developed for 3 clinically validated models and 1 model with an added saturation dynamic. The models include the well-known Minimal Model. They are fit to two different data sets of clinical PD data from hyperinsulinaemic clamp studies at euglycaemia and/or hyperglycaemia. The models are fit to the first data set to determine an optimal set of population parameters. The second data set is used to test trend prediction of the surface modeling as it represents a lower insulin sensitivity cohort and should thus require only scaling in these (or related) parameters to match this data set. This particular approach clearly highlights differences in modeling methods, and the model dynamics utilized that may not appear as clearly in other fitting or prediction validation methods. Across all models saturation of insulin action is seen to be an important determinant of prediction and fit quality. In particular, the well-reported under-modeling of insulin sensitivity in the Minimal Model can be seen in this context to be a result of a lack of saturation dynamics, which in turn affects its ability to detect differences between cohorts. The overall approach of examining PD surfaces is seen to be an effective means of analyzing and thus validating a metabolic model's inherent dynamics and basic trend prediction on a population level, but is not a replacement for data driven, patient-specific fit and prediction validation for clinical use. The overall method presented could be readily generalized to similar PD systems and therapeutics.

A model-driven multivariable controller for vapor compression refrigeration systems

A model-driven controller for vapor compression refrigeration systems is presented herein. Mathematical sub-models were developed for each of the system components: heat exchangers (condenser and evaporator), variable-speed compressor and variable-orifice electric expansion device. The overall system simulation model was used to design a MIMO controller based on the linear-quadratic Gaussian method using a state observer of the Kalman filter type. A purpose-built testing apparatus comprised of a variable-speed compressor and a pulse-width modulated expansion valve was used to collect data for the system identification and model validation exercises. It was found that the model reproduces the experimental trends of the working pressures in conditions far from the operation point (±30%) with a maximum deviation of ±5%. Additional experiments were also performed to verify the ability of the controller of tracking reference changes and rejecting thermal load disturbances as high as 15%.

The hydraulic actuation of multi-body structures through large scale motions. Part 1: Development and validation of system model

Modern engineering design is leading towards structures that are complex and lightweight. These structures often contain flexible and rigid components actuated through large displacements by a non-linear hydraulic system. Due to the increased system complexities, there is a need to define structural models that can be easily coupled to models of the hydraulic system for use in the design of suitable controllers. The current paper develops a modular system model composed of rigid and flexible structural components coupled directly to a non-linear hydraulic system. The resulting model allows for changes to be made to the hydraulic and structural components in an independent manner such that the entire system may be incorporated in a single simulation domain. A structural damping matrix is introduced that allows a control system designer to assign realistic modal damping ratios to well established modes, and higher damping to modes with significant uncertainty. This allows for increased steady-state accuracy and model run-time efficiency, which is beneficial to the controller design process presented in Part 2. The system modelling approach is applied to a hydraulically actuated experimental rig for validation purposes.

Dynamic fuel cell stack model for real-time simulation based on system identification

The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R&D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests.

Unsteady Thermodynamic Computational Fluid Dynamics Simulations of Aircraft Wing Anti-Icing Operation

A three-dimensional unsteady thermodynamic simulation model is developed to describe the dynamic response of an aircraft wing anti-icing system. This computational fluid dynamics based model involves a complete wing segment including a thermal anti-icing bay inside the leading edge. The unsteady, integrated internal/external thermal flow simulation is presented with heat conductivity through the solid skin in a structured mesh. The calculated skin temperature results are satisfactory in their good match with flight test data. The presented research work indicates a strong potential of using computational fluid dynamics in dynamic wing anti-icing system model development and validation.

Joint Dynamics Modeling and Parameter Identification for Space Robot Applications

Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS) for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm) to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ) experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS). The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.

Validation of a geographic information system model for mapping the risk of fasciolosis in cattle and buffaloes in Cambodia

Maps showing gradations of risk of fasciolosis due to Fasciola gigantica in Cambodia were produced using geographic information systems (GIS) technology in conjunction with determinants of fasciolosis. A comparison between levels of risk predicted by the maps and field measurements of prevalence in 11 provinces ( n = 1406) showed general agreement, which suggested the epidemiological determinants and weightings used to produce the maps were appropriate. However, due to logistical constraints, prevalence was measured at the provincial level and animals were not randomly sampled (and thus were unlikely to be representative of variability within provinces). To address this, additional field work was carried out to measure prevalence in more detail—faecal samples were collected from a randomly selected set of animals in four districts across a representative province for areas predicted to be at high risk ( n = 311), moderate risk ( n = 268) and no risk ( n = 262). As with the original field survey, the results show general agreement between prevalence and risk predicted by the maps, with the best fit found for areas predicted to be at high risk.

Implementation and Validation of Ground-Source Heat Pump System Models in an Integrated Building and System Simulation Environment

Despite the low energy consumption and lower maintenance benefits of ground-source heat pump (GSHP) systems, little work has been undertaken in detailed analysis and simulation of such systems. Long-term transient ground heat transfer significantly affects the performance of these systems. Annual and multi-year simulation consequently becomes an invaluable tool in the design of such systems—both in terms of calculating annual building loads and long-term ground thermal response. The EnergyPlus program, which makes use of variable time-step sizes in its simulation of building systems, was extended to allow multi-year simulations. Models of a water-source heat pump and a vertical borehole ground-loop heat exchanger have been implemented in Energy-Plus. The ground heat exchanger model uses Eskilson's “g-functions” to model response to time-varying heat fluxes and has been extended to include a computationally efficient variable time-step load aggregation scheme. The performance of this model has been compared with an analytical line source approximation. For a steady periodic input that included pulsated heat extraction, the model agreed with the analytical solution to within 2°C. The heat pump model was able to predict power and heat transfer rates over a wide range of operating conditions to within ±10% of published data. Experimental data from the Oklahoma State Hybrid Ground Source Heat Pump Laboratory have been used to validate both the heat pump and ground heat exchanger models. System simulation results were compared with five days of experimental data. The results showed an average error in the predicted ground heat transfer rate of less than 6% and average errors in the predicted heat pump power and the predicted source-side heat transfer rate of less than 3% and 4%, respectively. Using these models, it is possible to represent GSHP systems in a flexible way and examine their performance over the extended periods required for proper analysis.

Dual Flow Nets

This paper addresses the interrelation between control and data flow in embedded system models through a new design representation, called Dual Flow Net (DFN). A modeling formalism with a very close-fitting control and data flow is achieved by this representation, as a consequence of enhancing its underlying Petri net structure. The work presented in this paper does not only tackle the modeling side in embedded systems design, but also the validation of embedded system models through formal methods. Various introductory examples illustrate the applicability of the DFN principles, whereas the capability of the model to with complex designs is demonstrated through the design and verification of a real-life Ethernet coprocessor.

Power system model validation for power quality assessment applications using genetic algorithm

This paper presents an intelligent system for power quality assessment application. This system is used for power system model validation. A genetic algorithm (GA) based system for validating the power system model in capacitor switching studies has been developed. The problem formulation and the proposed solution are illustrated. The feasibility of the developed system for practical applications is demonstrated by evaluation studies.