Semi-physical Model Research Articles

Diagnostics of the nonlinear static models of a diode

The main problems of the nonlinear programming (stiffness of the problem and a rapid decrease in the optimization accuracy with increasing dimension of the minimized functional) are analyzed using an example of the simulation of a semiconductor diode. The dimension reduction of the numerical-optimization problem and the synthesis of semiphysical models are proposed for the diagnostics (parametric optimization).

Dielectric properties of brine-wetted snow on first-year sea ice

Abstract We present measurements, empirical models and a semi-physical dielectric mixture model for the dielectric constant and dielectric loss of brine-wetted snow on first-year sea ice. Experimental data include snow temperatures from − 15.9° to 0.0 °C and snow salinities from 0.1 to 12 parts/thousand. We compare dielectric measurements at 50 MHz with brine volume fraction, brine saturation and snow temperature. We present a dielectric mixture model for brine-wetted snow, with depolarization factors and conductivity parameters fitted to experimental data. The dielectric constant of snow increases by a factor of 78.65 times the brine volume fraction. The dielectric loss and conductivity also increase with brine volume fraction. We provide a theoretical treatment of the frequency dispersion of brine-wetted snow between 10 MHz and 40 GHz. The dielectric properties of brine-wetted snow are highly sensitive to changes in brine volume fraction and snow temperature.

System Properties and Control of Turbocharged Diesel Engines with High- and Low-Pressure EGR

An extension of the air- and exhaust-system with a two path EGR-system consisting of a conventional cooled high-pressure EGR and a cooled low-pressure EGR has the potential to significantly reduce the NOx-emissions. Model based control structures and model based controller calibration can handle the increasing complexity of the air-system. An automated controller calibration based on a semi-physical mean value model of reduced complexity is presented. The system configuration of a two path EGR system is introduced. Its static and dynamic properties are investigated. Additional to the static couplings in the classical air-path new couplings appear. Also the system's dynamic properties are investigated. A decentralised gain scheduled PI(D)-control approach is chosen to control the variables air mass flow rate, high-pressure EGR mass flow rate and charge-air pressure. The controller maps depend on the engine operation point and are calibrated by a local linearisation of the semi-physical model. Couplings resulting on the low-pressure EGR-path are compensated by a semi-physical feed forward control.

Modeling and Identification of a Small-scale Magnetorheological Damper

Magnetorheological (MR) dampers are promising devices for vibration mitigation in structures due to their low cost, energy efficiency, and fast response. To use these dampers efficiently it is necessary to have models that describe their behavior with a sufficient precision. However, because a precise modeling of these devices using the laws of physics is an arduous task, semi-physical models are used to describe their behavior instead. Two of these models are explored in this article: a normalized version of the Bouc-Wen model and the Dahl friction model. A methodology for identification is proposed, and the obtained models are tested and validated experimentally.

Discrete-event modelling of fire spreading

We deal here with the application of discrete-event System Specification (DEVS) formalism to implement a semi-physical fire spread model. Currently, models from physics finely representing forest fires are not efficient and still under development. If current softwares are devoted to the simulation of simple models of fire spread, nowadays there is no environment allowing us to model and simulate complex physical models of fire spread. Simulation models of such a type of models require being easily designed, modified and efficient in terms of execution time. DEVS formalism can be used to deal with these problems. This formalism enables the association of object-oriented hierarchical modelling with discrete-event techniques. Object-oriented hierarchical programming facilitates construction, maintenance and reusability of the simulation model. Discrete-events reduce the calculation domain to the active cells of the propagation domain (the heated ones).

Identification of a magnetorheological damper: Theory and experiments

Magnetorheological (MR) dampers are promising devices for vibration mitigation in structures due to their low cost, energy efficiency and fast response. To use these dampers efficiently it is necessary to have models that describe their behavior with a sufficient precision. However, a precise modeling of these devices using the laws of physics is an arduous task so that semi-physical models are used instead to describe their behavior. Two of these models are explored in this paper: a normalized version of the Bouc-Wen model and the Dahl friction model. A methodology for identification is proposed, and the obtained models are tested and validated experimentally.

GREY BOX MODELLING – BRANCHES AND EXPERIENCES

This paper deals with methods and experiences of incorporating a priori knowledge into mathematical models of industrial processes and systems. Grey box modelling has been developed in several directions and can be grouped into branches depending on the way a priori knowledge is handled. In this paper we divide grey box modelling into the following branches; constrained black box identification, semi-physical modelling, mechanistic modelling, hybrid modelling and distributed parameter modelling. Experiences from case studies demonstrate the different branches of grey box modelling procedures. In the applications, the grey box models have been used for model based control, soft sensors, process supervision and failure detection. Further, distributed parameter modelling presents a specific challenge in that it is difficult to distinguish model reduction errors from model-data discrepancies. By estimating the model reduction error and forming hypothesis tests based on the estimate, the problem can be dealt with effectively.

Semi-physical Neural Network Model in Detecting Engine Transient Faults using the Local Approach

This paper investigates detection of an air leak fault in the intake manifold subsystem of an automotive engine during transient operation. Previously, it was shown that integrating the local approach with an auto-associative neural network model of the engine, significantly increased the sensitivity of fault detection. However, the drawback then is that the computational load is naturally dependent on the network complexity. This paper proposes the use of the available physical models to pre-process the original signals prior to model building for fault detection. This not only extracts existing relationships among the variables, but also helps in reducing the number of variables to be modelled and the related model complexity. The benefits of this improvement are demonstrated by practical application to a modern spark ignition 1.8 litre Nissan petrol engine.

Dynamic loading of overhead lines by adaptive learning techniques and distributed temperature sensing

The need for dynamic loading of overhead lines requires reliable assessment models that should be able to predict both the evolution of the hot-spot temperature and the associated maximum allowed duration, at any load level and on the basis of actual conductor thermal state and forecasted environmental conditions. In order to address this problem, a novel identification semi-physical modelling architecture that combines knowledge coming from expertise with empirical evidence provided by observations is proposed. This is performed by integrating an analytical thermal model, which estimates qualitatively the conductor hot-spot temperature, and an adaptive corrective algorithm, based on a local learning theory and aimed at enhancing the estimation accuracy. The corrective algorithm is continuously adjusted by field data acquired through distributed fibre-optic sensor based on stimulated Brillouin scattering. To assess the performances of the proposed methodology, the main results of experimental studies obtained on a laboratory overhead line are presented and discussed.

Solute and Second Phase Evolution during Industrial Processing of AA3103

The complete evolution of solute content and second phases during full-scale industrial processing of AA3103 sheets has been measured. During pre-heating, dispersoids, which appear as plates or small polyhedra grow and the Mn solute content decreases. During subsequent breakdown rolling the dispersoid number-density increases significantly. The measured decrease of solute Mn after hot rolling and coil cooling is attributed to constituent particle growth, whereas the solute depletion during the final back-annealing is mainly caused by the growth of the dispersoids. These observations are compared to the predictions obtained by a semi-physical model for precipitation. Although simulations have been performed without any retro-fitting, for hot rolling the results compare quantitatively well with experiment, while for coil cooling and back annealing the modelled Mn solute depletion is underestimated. The precipitation process is found to be very sensitive to the microstructure, which illustrates the importance of coupling precipitation models with work hardening and softening models to obtain reliable predictions.

Modeling and identification of solar energy water heating system incorporating nonlinearities

Prediction accuracy is a fundamental modeling requirement. This work explores different models for a solar domestic water heating system located near Viña del Mar, Chile, in order to improve model prediction accuracy over some existing alternatives. The main approach is semi-physical modeling, which combines phenomenological modeling and system identification. The former helps to organize in a conceptual form the available system knowledge, and the latter allows to adjust that knowledge into a particular model structure for a working system. Thus, with semi-physical modeling we take advantage of a basic property of classical identification models: they are linear-in-the-parameters, but may contain nonlinear regressors. Hence, while physical knowledge suggests nonlinear data regressors, system identification adjusts linear weighting parameters. The models proposed here incorporate nonlinearities based on physical system knowledge and they include, among other inputs and disturbances, air wind speed ( v) and air relative humidity (RH), signals not usually considered in these model structures. Specifically, this work shows model predictive accuracy of storage tanks temperature for three model types: semi-physical, state-space and, a combination of semi-physical and a feedforward neural network with one hidden layer and eight neurons. The best models found here, according to prediction accuracy, are of semi-physical nature, and are obtained using stepwise regressor elimination algorithms which retain disturbances such as v and RH. Additionally, final models are validated with classical statistical tests such as AIC and correlation analysis.

Comparison of semi-physical and black-box bushing model for vehicle dynamics simulation

Since bushing components have highly nonlinear characteristics according to the amplitude and the frequency, it is hard to derive a mathematical modeling of the bushing. In this paper, a semi-physical bushing model and a black-box bushing model are derived and compared. A Bouc-Wen hysteretic model is employed to derive a semi-physical model and an artificial neural network algorithm is used to make a black-box bushing model. A 3-axes MTS rubber test machine is used to capture the bushing characteristics. Sine excitation tests based on the different frequencies and different amplitudes are performed to find out the bushing characteristics. Random test results are also used to generate the coefficients of a semi-physical model and weighting factors of the black-box model. Random simulations are carried out to verify the bushing models and the results are compared with each other according to the numerical efficiency and accuracy.

A semi-physical tire model for a vehicle dynamics analysis of handling and braking

For a semi-physical model, this paper first presents its basic concept, transient steer effect and driving conditions. The model experimentally formulates vertical force as a function of tire deflection, camber angle and lateral force. In order to describe longitudinal and lateral forces as functions of tire characteristic parameters such as stiffness and friction, the basic formulations of the UA tire model are re-used. For self-aligning and overturning moments, the application points of three forces are first experimentally formulated, and then the moments are expressed as functions of the three forces and their application points. All characteristic parameters are experimentally determined as functions of load, camber angle, slip angle, slip angle sweep rate and/or slip ratio for cornering and/or driving/braking. For the purpose of validation, the model is implemented into ADAMS and then its simulation results are compared with measurement data for various test conditions.

INVERSION OF A SEMI-PHYSICAL DISPERSION MODEL

This study proposes to examine the performances of an inverse dynamic model resulting from the fusion of deterministic modeling and statistical learning. An inverse semi-physical or gray-box model is then carried out using a recurrent neural network (NN). The suggested model concerns in particular a pollutant dispersion phenomenon governed by a partial differential equation (PDE), on a basic mesh. This technique leads to the realization of a neural network inverse problem solver (NNIPS). The network is structured by the discrete reverse-time state form of the direct model. The performances are numerically analyzed in terms of generalization, regularization and training effort.

MULTI-MODAL SIMULATION FOR MICROSTRUCTURAL AND PROPERTY PREDICTION FOR AL ALLOY HOT ROLLING

This study presents the application of FE modelling and intelligent systems techniques to the prediction of microstructural mapping for aluminium alloys. Here, the material within each finite element is defined using a set of non-linear physically-based equations. These are then solved in real-time using a semi-physical grey-box model. This model is based on a neuro-fuzzy approach and it has been embedded within the FE technique. The model maps the evolution of the internal state variables (i.e. dislocation density, subgrain size and subgrain boundary misorientation) and their effect on the recrystallisation behaviour of the stock. This paper presents the model development, the integration between the numerical techniques, and the application of the technique to a hot rolling operation using an Aluminium, 1wt% Magnesium alloy. In addition, the developed model includes a CA (Cellular Automata) level to represent inhomogeneous nature of microstructural beheviour.

Relationship between dielectric permittivity and water content for vadose zone materials of volcanic origin

Seven materials, encompassing a young volcanic soil, its parent material, two older buried soils (Palaeosols), and an underlying older volcanic deposit were investigated in a laboratory study to determine the relationship between dielectric permittivity (ϵc) and water content (θ). Empirical 3rd-order polynomial models generally fitted the data better than semi-physical 3-phase mixing models and normalised conversion functions. Based on their different dielectric response, which presumably is mainly due to their different bulk density (and thus porosity), the materials could be grouped into Ignimbrite materials and Palaeosol materials. Relative to the Ignimbrites, Palaeosols showed a particularly muted response at the low end of dielectric permittivity measurements, which could be due to a particularly high amount of bound water in these allophane-rich materials and/or to greater microstructural and phase configuration effects. Applying the Topp equation resulted in substantial underestimations of the water contents of both groups. A relationship previously developed on New Zealand sandy volcanic soils fitted our Ignimbrite materials well, but still underestimated the water contents of the Palaeosols substantially. Normalised conversion functions described both material groupings reasonably well, demonstrating that bulk density differences can explain a substantial part of the variation of ϵc–θ relationships between different materials.

Tire model TMeasy

This paper describes the semi-physical tire model TMeasy for vehicle dynamics and handling analyses, as it was applied in the ‘low frequency tire models’ section of the research programme tire model performance test (TMPT). Despite more or less weak testing input data, the effort for the application of TMeasy remains limited due to its consequent ‘easy to use’ orientation. One particular feature of TMeasy is the wide physical meaning of its smart parameter set, which allows to sustain the identification process even under uncertain conditions. After a general introduction, the modelling concept of TMeasy is compactly described in this paper. Taking the standard tire interface (STI) to multibody simulation system (MBS) software into account, the way to apply TMeasy is briefly shown. This includes three selected examples of application. The final comments of the authors on TMPT describe the experiences and earnings received during the participation in that programme.

Development and evaluation of a semi-physical crown fire initiation model

Development and evaluation of a semi-physical crown fire initiation model

A revised empirical model to estimate solar radiation using automated surface weather observations

The transition from manual to automated weather observations at US National Weather Service Offices has compromised the ability to use these data as a means for estimating global horizontal and direct solar radiation. The creation of long term model-derived solar radiation climatologies continues to rely on the in situ cloud data that these observations provide, since homogeneous and readily available satellite data does not span the transition. An existing semi-physical solar radiation model is revised to allow for the estimation of hourly solar radiation based on these observations. Model evaluation reveals that errors in solar radiation estimates are comparable to other contemporary solar radiation models that estimate global horizontal solar radiation on both daily (10–15% mean absolute error) and hourly (15–19% mean absolute error) timescales. Hourly mean absolute errors are similar for different sky conditions, while daily percent errors are similar between seasons. Model updates also allow for accurate estimates of solar radiation in various climate regimes; regional patterns in model bias are not evident.

RF Performance and Modeling of Si/SiGe Resonant Interband Tunneling Diodes

The RF performance of two different Si-based resonant interband tunneling diodes (RITD) grown by low-temperature molecular beam epitaxy (LT-MBE) were studied. An RITD with an active region of B /spl delta/-doping plane/2 nm i-Si/sub 0.5/Ge/sub 0.5//1 nm i-Si/P /spl delta/-doping plane yielded a peak-to-valley current ratio (PVCR) of 1.14, resistive cutoff frequency (f/sub r0/) of 5.6 GHz, and a speed index of 23.3 mV/ps after rapid thermal annealing at 650/spl deg/C for 1 min. To the authors' knowledge, these are the highest reported values for any epitaxially grown Si-based tunnel diode. Another RITD design with an active region of 1 nm p+ Si/sub 0.6/Ge/sub 0.4//B /spl delta/-doping plane/4-nm iSi/sub 0.6/Ge/sub 0.4//2 nm i-Si/P /spl delta/-doping plane and annealed at 825/spl deg/C for 1 min had a PVCR of 2.9, an f/sub r0/ of 0.4 GHz, and a speed index of 0.2 mV/ps. A small signal model was established to fit the measured S/sub 11/ data for both device designs. Approaches to increase f/sub r0/ are suggested based on the comparison between these two diodes. The two devices exhibit substantially different junction capacitance/bias relationships, which may suggest the confined states in the /spl delta/-doped quantum well are preserved after annealing at lower temperatures but are reduced at higher temperature annealing. A comprehensive dc/RF semi-physical model was developed and implemented in Agilent advanced design system (ADS) software. Instabilities in the negative differential resistance (NDR) region during dc measurements were then simulated.