Simulation Blocks for TOSSIM-T2"

We develop several hardware and software simulation blocks for the TinyOS-2 (TOSSIM-T2) simulator. The choice of simulated hardware platform is the popular MICA2 mote. While the hardware simulation elements comprise of radio and external flash memory, the software blocks include an environment noise model, packet delivery model and an energy estimator block for the complete system. The hardware radio block uses the software environment noise model to sample the noise floor.The packet delivery model is built by establishing the SNR-PRR curve for the MICA2 system. The energy estimator block models energy consumption by Micro Controller Unit(MCU), Radio,LEDs, and external flash memory. Using the manufacturer’s data sheets we provide an estimate of the energy consumed by the hardware during transmission, reception and also track several of the MCUs states with the associated energy consumption. To study the effectiveness of this work, we take a case study of a paper presented in [1]. We obtain three sets of results for energy consumption through mathematical analysis, simulation using the blocks built into PowerTossim-T2 and finally laboratory measurements. Since there is a significant match between these result sets, we propose our blocks for T2 community to effectively test their application energy requirements and node life times.

Necessity for quatum simulation for future technology nodes

In this paper we present and compare the results obtained from semi-classical and quantum mechanical simulation for a double gate MOSFET structure to analyze the electrostatics and carrier dynamics of this device. The geometries like gate length, body thickness of this device have been chosen according to the ITRS specification for the different technology nodes. We have shown the extent of deviation between the semi- classical and quantum mechanical results and hence the need of quantum simulations for the promising nanoscale devices in the future technology nodes predicted in ITRS.

A Simulation Based Study and Analysis of Double Gate Tunnel FET Performance for Low Stand-By Power Applications

The conventional metal oxide semiconductor field effect transistor (MOSFET)may not be suitable for future low standby power (LSTP) applications due to its high off-state current as the sub-threshold swing is theoretically limited to 60mV/decade. Tunnel field effect transistor (TFET) based on gate controlled band to band tunneling has attracted attention for such applications due to its extremely small sub-threshold swing (much less than 60mV/decade). This paper takes a simulation approach to gain some insight into its electrostatics and the carrier transport mechanism. Using 2D device simulations, a thorough study and analysis of the electrical parameters of the planar double gate TFET is performed. Due to excellent sub-threshold characteristics and a reverse biased structure, it offers orders of magnitude less leakage current compared to the conventional MOSFET. In this work, it is shown that the device can be scaled down to channel lengths as small as 30 nm without affecting its performance. Also, it is observed that the bulk region of the device plays a major role in determining the sub-threshold characteristics of the device and considerable improvement in performance (in terms of ION/IOFF ratio) can be achieved if the thickness of the device is reduced. An ION/IOFF ratio of 2x1012 and a minimum point sub-threshold swing of 22mV/decade is obtained.

Filtered density function for large eddy simulation of turbulent reacting flows

A methodology termed the “filtered density function” (FDF) is developed and implemented for large eddy simulation (LES) of chemically reacting turbulent flows. In this methodology, the effects of the unresolved scalar fluctuations are taken into account by considering the probability density function (PDF) of subgrid scale (SGS) scalar quantities. A transport equation is derived for the FDF in which the effect of chemical reactions appears in a closed form. The influences of scalar mixing and convection within the subgrid are modeled. The FDF transport equation is solved numerically via a Lagrangian Monte Carlo scheme in which the solutions of the equivalent stochastic differential equations (SDEs) are obtained. These solutions preserve the Itô-Gikhman nature of the SDEs. The consistency of the FDF approach, the convergence of its Monte Carlo solution and the performance of the closures employed in the FDF transport equation are assessed by comparisons with results obtained by direct numerical simulation (DNS) and by conventional LES procedures in which the first two SGS scalar moments are obtained by a finite difference method (LES-FD). These comparative assessments are conducted by implementations of all three schemes (FDF, DNS and LES-FD) in a temporally developing mixing layer and a spatially developing planar jet under both non-reacting and reacting conditions. In non-reacting flows, the Monte Carlo solution of the FDF yields results similar to those via LES-FD. The advantage of the FDF is demonstrated by its use in reacting flows. In the absence of a closure for the SGS scalar fluctuations, the LES-FD results are significantly different from those based on DNS. The FDF results show a much closer agreement with filtered DNS results. © 1998 American Institute of Physics.

Command Tracking in High Performance Aircrafts: A New Dynamic Inversion Design

This paper proposes a new straight forward technique based on dynamic inversion, which is applied for tracking the pilot commands in high performance aircrafts.Pilot commands assumed in longitudinal mode are normal acceleration and total velocity(while roll angle and lateral acceleration are maintained at zero). In lateral mode, roll rate and total velocity are used as pilot commands (while climb rate and lateral acceleration are maintained at zero). Ensuring zero lateral acceleration leads to a better turn co-ordination. A six degree-of-freedom model of F-16 aircraft is used for both control design as well as simulation studies. Promising results are obtained which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response and reduced control magnitude. Another advantage of this approach is that it leads to a significant reduction of tuning parameters in the control design process.