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

Subtraction-noise projection in gravitational-wave detector networks

In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer (BBO) mission which seeks for a stochastic background of primordial GWs in the frequency range $\sim 0.1-1$Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer (TDI) network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that BBO will be sensitive to backgrounds with fractional energy densities below $\Omega=10^{-16}$Comment: 54 pages, 15 figure

Swing Dynamics as Primal-Dual Algorithm for Optimal Load Control

Frequency regulation and generation-load balancing are key issues in power transmission networks. Complementary to generation control, loads provide flexible and fast responsive sources for frequency regulation, and local frequency measurement capability of loads offers the opportunity of decentralized control. In this paper, we propose an optimal load control problem, which balances the load reduction (or increase) with the generation shortfall (or surplus), resynchronizes the bus frequencies, and minimizes a measure of aggregate disutility of participation in such a load control. We find that, a frequency-based load control coupled with the dynamics of swing equations and branch power flows serve as a distributed primal-dual algorithm to solve the optimal load control problem and its dual. Simulation shows that the proposed mechanism can restore frequency, balance load with generation and achieve the optimum of the load control problem within several seconds after a disturbance in generation. Through simulation, we also compare the performance of optimal load control with automatic generation control (AGC), and discuss the effect of their incorporation

Incorporating information from source simulations into searches for gravitational-wave bursts

The detection of gravitational waves from astrophysical sources of gravitational waves is a realistic goal for the current generation of interferometric gravitational-wave detectors. Short duration bursts of gravitational waves from core-collapse supernovae or mergers of binary black holes may bring a wealth of astronomical and astrophysical information. The weakness of the waves and the rarity of the events urges the development of optimal methods to detect the waves. The waves from these sources are not generally known well enough to use matched filtering however; this drives the need to develop new ways to exploit source simulation information in both detections and information extraction. We present an algorithmic approach to using catalogs of gravitational-wave signals developed through numerical simulation, or otherwise, to enhance our ability to detect these waves. As more detailed simulations become available, it is straightforward to incorporate the new information into the search method. This approach may also be useful when trying to extract information from a gravitational-wave observation by allowing direct comparison between the observation and simulations.Comment: 8 pages, 1 figur