Francesco Ferranti

Rational macromodeling of 1D blood flow in the human cardiovascular system

In this paper, we present a novel rational macromodeling approach for the description of 1D blood flow in the human cardiovascular system, which is suitable for time-domain simulations. Using the analogy of the blood flow propagation problem with transmission lines and considering the hypothesis of linearized Navier-Stokes equations, a frequency-domain rational macromodel for each arterial segment has been built. The poles and the residues of each arterial segment macromodel have been calculated by means of the Vector Fitting technique. Finally, the rational macromodel of the whole cardiovascular system is obtained by properly combining the macromodels of the single arterial segments using an interconnect matrix. The rational form of the proposed cardiovascular model leads to a state-space or electrical circuit model suitable for time-domain analysis. The stability and passivity properties of the global cardiovascular model are discussed to guarantee stable time-domain simulations. The proposed macromodeling approach has been validated by pertinent numerical results. Copyright

Fast identification of Wiener-Hammerstein systems using discrete optimisation

A fast identification algorithm for Wiener-Hammerstein systems is proposed. The computational cost of separating the front and the back linear time-invariant (LTI) block dynamics is significantly improved by using discrete optimisation. The discrete optimisation is implemented as a genetic algorithm. Numerical results confirm the efficiency and accuracy of the proposed approach.

Hybrid Nonlinear Modeling Using Adaptive Sampling

This paper proposes a direct method for the extraction of empirical-behavioral hybrid models using adaptive sampling. The empirical base is responsible for the functionality over a wide range of variables, especially in the extrapolation range. The behavioral part corrects the errors of the empirical part in the region of particular interest, thus, it improves the accuracy in the desired region. Employment of response surface methodology and adaptive sampling allows full automation of the hybrid model extraction and assures its compactness. We used this approach to build a hybrid model composed of a robust empirical model available in CAD tools and a Radial Basis Functions interpolation model with Gaussian basis function. We extracted the hybrid model from measurements of a 0.15 μm GaAs HEMT and compared it with the pure behavioral and pure empirical models. The hybrid model yields higher accuracy while maintaining extrapolation capabilities. Additionally, the extraction time of the hybrid model is relatively low. We also show that a good accuracy level can be achieved with a small number of measurements.

A Tensor-Based Extension for the Multi-Line TRL Calibration

The multi-line TRL (M-TRL) method is an accurate calibration method for S-parameter measurements that is based on the measurement of multiple transmission lines and their subsequent pairing. In this paper, an alternative is proposed for the line pairing process that underspans the M-TRL calibration. A tensor decomposition is used to process the measurements of all lines simultaneously and avoid the selection of a reference line as in the classical approach. Numerical and experimental results confirm the accuracy, the statistical efficiency, and the robustness of the proposed method.

Effective Electrothermal Analysis of Electronic Devices and Systems with Parameterized Macromodeling

We propose a parameterized macromodeling methodology to effectively and accurately carry out dynamic electrothermal (ET) simulations of electronic components and systems, while taking into account the influence of key design parameters on the system behavior. In order to improve the accuracy and to reduce the number of computationally expensive thermal simulations needed for the macromodel generation, a decomposition of the frequency-domain data samples of the thermal impedance matrix is proposed. The approach is applied to study the impact of layout variations on the dynamic ET behavior of a state-of-the-art 8-finger AlGaN/GaN high-electron mobility transistor grown on a SiC substrate. The simulation results confirm the high accuracy and computational gain obtained using parameterized macromodels instead of a standard method based on iterative complete numerical analysis.

Nonlinear behavioral models of HEMTs using response surface methodology

In this paper, the response surface methodology is proposed to model nonlinear microwave devices using different sampling techniques. Each of the methods represents a distinct approach: exploration-oriented (Voronoi tessellation), nonlinearity-exploitation-oriented (LOcal Linear Approximation) and model-error-minimization-oriented. This allows to build accurate and compact global behavioral models of drain voltage at different harmonics of a 0.15 μm GaAs HEMT transistor with only few hundreds of samples. After choosing the best sampling technique, two types of global models are compared: Radial Basis Function and Kriging. It is shown that the modeling convergence depends on the model type, and better results are obtained using the Kriging model.

Parameterized Macromodeling for Efficient Analysis of Wideband Tower Grounding Structures

Field solvers are often needed to properly characterize the behavior of grounding structures in a wide frequency range. These solvers provide accurate results but at high computational cost. Design tasks, such as design optimization and sensitivity analysis, require repeated simulations for different values of system parameters (e.g., conductor geometry and soil parameters). Parameterized macromodels are suitable to speed up design tasks without compromising the accuracy and reliability of the results. These models allow describing the system behavior as a function of frequency (or time) and system parameters. In this paper, we propose a parameterized macromodeling approach for grounding structures to characterize the system impedance behavior as a function of frequency and system parameters. The method initially calculates a set of frequency-domain root macromodels for a set of system parameters combinations. An adaptive frequency sampling approach is introduced for reducing the required number of frequency samples. The model behavior at any desired intermediate parameters values can then be calculated by an enhanced interpolation scheme that uses scaling coefficients obtained via optimization. Numerical results for a tower grounding electrode example validate the proposed approach and show that it can efficiently and accurately predict the multidimensional system behavior in frequency and time domain.

Design of Experiments Using Centroidal Voronoi Tessellation

In this paper, the centroidal Voronoi tessellation (CVT) is proposed as a design of experiments (DoE) for the nonlinear modeling of active devices. Different method’s flavors are being described, allowing to maximize the total amount of information gathered during measurements. As a case study, the CVT designs have been tested for both simulation-based experiments of nonlinear test functions, as well as for the extraction of nonlinear transfer functions of a handset radio-frequency power amplifier. The use of CVT allowed to achieve lower interpolation error, and contrary to the most popular design in microwaves, i.e., factorial DoE, the proposed tessellation can handle any arbitrary number of samples.

Efficient behavioral model extraction of nonlinear active devices using adaptive sampling with compact nonlinearity measure

Description of nonlinear active devices is very complex, and depends on many input variables. Therefore, extraction of behavioral models based on traditional Designs of Experiments, such as factorial or Latin hypercube, may be unacceptably expensive in terms of sample evaluation time. In order to limit the total number of samples required to obtain accurate behavioral models, an adaptive sampling strategy may be used. It is based on surrogate models that are extracted for each sampling iteration. As nonlinear description consists also of many output variables, a common synthetic quantity is proposed to limit the surrogate modeling cost. It is defined as a total change of all the output quantities. The approach was evaluated in measurements of a 0.15 μm pHEMT model. The modeling accuracy is improved, while significant modeling-cost reduction can be observed.

Global adjoint sensitivity analysis of coupled coils using parameterized model order reduction

This paper presents a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils. It is based on the use of parameterized models for the electromagnetic matrices and the Krylov matrices of the original and corresponding adjoint systems, and congruence transformations. Numerical results confirm the efficiency and accuracy of the proposed method for global sensitivity analysis over the complete design space of interest.