Nodal and mesh analysis simplification by introducing a theorem-based preliminary step

This brief proposes a new and simplified method for the analysis of linear circuits that combines the classical mesh-current or node-voltage method with a recently-stated theorem. The beforehand application of such a theorem, which involves the insertion of an open or short circuit, in a circuit with N meshes or nodes results in a system of N- 1 linear equations for N- 1 unknowns, instead of N equations for N unknowns obtained using the conventional approach. Therefore, if the circuit is analyzed in matrix form, the resulting coefficient matrix is square of order N- 1, instead of N , thus facilitating the hand calculations. Examples of circuit analysis are provided to demonstrate the applicability and advantages of the proposed analysis method in comparison with the conventional approach.Peer ReviewedPostprint (published version

A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

This paper introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on Gaussian process regression (GPR). As opposed to classical surrogate modeling techniques, GPR inherently provides information on the model uncertainty. The main contribution of this work is twofold. First, it describes how, in an UQ scenario, the model uncertainty can be combined with the uncertainty of the input design parameters to provide confidence bounds for the statistical estimates of the system outputs, such as moments and probability distributions. These confidence bounds allows assessing the accuracy of the predicted statistics. Second, in order to deal with dynamic multi-output systems, principal component analysis (PCA) is effectively employed to compress the time-dependent output variables into a smaller set of components, for which the training of individual GPR models becomes feasible. The uncertainty on the principal components is then propagated back to the original output variables. Several application examples, ranging from a trivial RLC circuit to real-life designs, are used to illustrate and validate the advocated approach

Bi-Level Optimization Considering Uncertainties of Wind Power and Demand Response

Recently, world-wide power systems have been undergone a paradigm change with increasing penetration of renewable energy. The renewable energy is clean with low operation cost while subject to significant variability and uncertainty. Therefore, integration of renewables presents various challenges in power systems. Meanwhile, to offset the uncertainty from renewables, demand response (DR) has gained considerable research interests because of DR’s flexibility to mitigate the uncertainty from renewables. In this dissertation, various power system problems using bi-level optimization are investigated considering the uncertainties from wind power and demand response. In power system planning, reactive power planning (RPP) under high-penetration wind power is studied in this dissertation. To properly model wind power uncertainty, a multi-scenario framework based on alternating current optimal power flow (ACOPF) considering the voltage stability constraint under the worst wind scenario and transmission N-1 contingency is developed. The objective of RPP in this work is to minimize the VAR investment and the expected generation cost. Benders decomposition is used to solve this model with an upper level problem for VAR allocation optimization and generation cost minimization as a lower problem. Then, several problems related wind power and demand response uncertainties under power market operation are investigated. These include: an efficient and effective method to calculate the LMP intervals under wind uncertainty is proposed; the load serving entities’ strategic bidding through a coupon-based demand response (CBDR) with which a load serving entity (LSE) may participate in the electricity market as strategic bidders by offering CBDR programs to customers; the impact of financial transmission right (FTR) with CBDR programs is also studied from the perspective of LSEs; and the stragegic scheduling of energy storages owned by LSEs considering the impact of charging and discharging on the bus LMP. In these problems, a bi-level optimization framework is presented with various objective functions representing different problems as the upper level problems and the ISO’s economic dispatch (ED) as the lower level problem. The bi-level model is addressed with mathematic program with equilibrium constraints (MPEC) model and mixed-integer linear programming (MILP), which can be easily solved with the available optimization software tool

Modeling for the Computer-Aided Design of Long Interconnects

L'abstract è presente nell'allegato / the abstract is in the attachmen

Broadband modeling of on-chip transformers for silicon RFICs

Magnetically coupled passive transformers are increasingly integrated on-chip for various analog and radio frequency (RF) applications including direct current (DC) isolation, impedance transformation/matching, and conversion between single-ended and differential signals. A primary motivation for the on-chip integration of transformers is the overall size reduction and reduced cost. However, the performance of on-chip transformers is adversely affected by increased losses in the conductors and silicon substrate as well as limited values of achievable inductance, coupling coefficient and quality factor. Consequently, accurate, broadband transformer models compatible with transient circuit simulation are needed for analog and RF integrated circuit design. This thesis presents a new two-step methodology for automated broadband model generation for monolithic transformers integrated on silicon substrates. First, an equivalent circuit model (ECM) in single or multi-PI topology is extracted from available four-port scattering parameters obtained by electromagnetic full-wave simulation or measurement. To further enhance the broadband characteristics of the extracted ECM, an automated augmentation method based on linear least squares fitting is adapted. The new augmentation procedure adds physically motivated circuit elements to the ECM resulting in a guaranteed stable and passive circuit model with improved accuracy over a broad frequency range. The modeling algorithm has been implemented in a MATLAB code. A typical SPECTRE model extraction over the frequency range of 0.1-10 GHz takes less than 4 minutes on an Intel-based 2.13 GHz Xeon computer. The new extraction approach is validated for a variety of transformer configurations built on standard BiCMOS and CMOS processes with stacked and interleaved topologies, different shapes, and various turns ratios, sizes and process parameters. The extracted models show good agreement over a broad frequency range with the corresponding measurement data obtained by 3-D full wave electromagnetic simulation

Predictive modeling of infrared detectors and material systems

Detectors sensitive to thermal and reflected infrared radiation are widely used for night-vision, communications, thermography, and object tracking among other military, industrial, and commercial applications. System requirements for the next generation of ultra-high-performance infrared detectors call for increased functionality such as large formats (> 4K HD) with wide field-of-view, multispectral sensitivity, and on-chip processing. Due to the low yield of infrared material processing, the development of these next-generation technologies has become prohibitively costly and time consuming. In this work, it will be shown that physics-based numerical models can be applied to predictively simulate infrared detector arrays of current technological interest. The models can be used to a priori estimate detector characteristics, intelligently design detector architectures, and assist in the analysis and interpretation of existing systems. This dissertation develops a multi-scale simulation model which evaluates the physics of infrared systems from the atomic (material properties and electronic structure) to systems level (modulation transfer function, dense array effects). The framework is used to determine the electronic structure of several infrared materials, optimize the design of a two-color back-to-back HgCdTe photodiode, investigate a predicted failure mechanism for next-generation arrays, and predict the systems-level measurables of a number of detector architectures

Probabilistic Structures Analysis Methods (PSAM) for select space propulsion system components

The basic formulation for probabilistic finite element analysis is described and demonstrated on a few sample problems. This formulation is based on iterative perturbation that uses the factorized stiffness on the unperturbed system as the iteration preconditioner for obtaining the solution to the perturbed problem. This approach eliminates the need to compute, store and manipulate explicit partial derivatives of the element matrices and force vector, which not only reduces memory usage considerably, but also greatly simplifies the coding and validation tasks. All aspects for the proposed formulation were combined in a demonstration problem using a simplified model of a curved turbine blade discretized with 48 shell elements, and having random pressure and temperature fields with partial correlation, random uniform thickness, and random stiffness at the root

Coriolis effects in bladed discs

New aero-engine architectures are currently being developed to satisfy the increasing demand for fuel-efficiency and lower noise, pushing the boundaries of todays design practice. These unproven designs require additional effort to ensure safety to high-cycle fatigue and flutter, widely acknowledged as a main risk for turbomachinery components. This calls for a new focus on phenomena that have been little investigated in the past due to their minor relevance for traditional designs, like the Coriolis effect. The Coriolis effect can cause an increase in the number of resonance frequencies, and generate global travelling-wave modes that can affect performance and flutter stability. Experimentally validated prediction and analysis methods are essential to ensure the accurate evaluation of the impact of the Coriolis effect on future engine designs. The major finite element (FE) software packages were systematically assessed, and proven to provide reliable simulations of the dynamics of bladed discs when the Coriolis effect is included. Experimental modal tools for the detection and identification of the Coriolis effect are also needed, to provide accurate interpretation of the data for model validation and updating. For this purpose, a dedicated rotating test rig was designed and manufactured. A novel Multiple Input Multiple Output testing framework was developed, based on the use of an array of strain gauges and piezoelectric actuators in combination with a poly-reference identification method, for the extraction of the full set of modal parameters arising in a bladed disc from the Coriolis force. The new technique allowed the successful recovery of Campbell diagrams, damping and strain mode shapes. Left displacement eigenvectors, which appear in the FRF formulation due to the Coriolis effect, could also be extracted and validated for the first time. An accurate comparison was conducted between the measurement data and the FE results, and confirmed the reliability of the new approach.Open Acces

Quantum simulation with periodically driven superconducting circuits

Superconducting quantum circuits have made tremendous advances in realizing engineered quantum dynamics for quantum simulation and quantum information processing over the past two decades. Technological developments in the field of superconducting circuits have raised them to be the leading platform for implementing many-qubit systems. This thesis introduces a sequence of concepts for engineering spin-lattice Hamiltonians in analog quantum simulation with superconducting circuits. Our approach to quantum simulation is to engineer driving schemes that lead to implementations of the desired models. The applications of this approach are in our work mainly centered around two types of systems: interesting many-body topological quantum systems, namely Kitaev’s toric code and honeycomb model and two-body systems that can be employed as building blocks of larger quantum simulators or quantum computers. In the first part of this thesis, we make a proposal for an analog implementation of the toric code in superconducting circuits. We also discuss a realistic implementation of this model on an eight qubit lattice. In the second part, we present our analog approach for implementing arbitrary spinspin interactions in linearly and nonlinearly coupled superconducting qubits. Our proposed toolbox has the potential of easy generalization to a variety of systems and interactions. Lastly, based on our two-body toolbox, we set forward two possible implementations of the Kitaev honeycomb model and show that the engineered two-body interactions work - with good accuracy - in this many-body model