Srinivasa Pai P.,N.M.A.M.I.T. |
Shrinivasa Rao B.R.,N.M.A.M.I.T.
Applied Energy | Year: 2011
Due to the increasing demand for fossil fuels and environmental threat due to pollution a number renewable sources of energy have been studied worldwide. In the present investigation influence of injection timing on the performance and emissions of a single cylinder, four stroke stationary, variable compression ratio, diesel engine was studied using waste cooking oil (WCO) as the biodiesel blended with diesel. The tests were performed at three different injection timings (24°, 27°, 30° CA BTDC) by changing the thickness of the advance shim. The experimental results showed that brake thermal efficiency for the advanced as well as the retarded injection timing was lesser than that for the normal injection timing (27° BTDC) for all sets of compression ratios. Smoke, un-burnt hydrocarbon (UBHC) emissions were reduced for advanced injection timings where as NOx emissions increased. Artificial Neural Networks (ANN) was used to predict the engine performance and emission characteristics of the engine. Separate models were developed for performance parameters as well as emission characteristics. To train the network, compression ratio, injection timing, blend percentage, percentage load, were used as the input parameters where as engine performance parameters like brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature (Texh) were used as the output parameters for the performance model and engine exhaust emissions such as NOx, smoke and (UBHC) values were used as the output parameters for the emission model. ANN results showed that there is a good correlation between the ANN predicted values and the experimental values for various engine performance parameters and exhaust emission characteristics and the relative mean error values (MRE) were within 8%, which is acceptable. © 2010 Elsevier Ltd.
Communications in Mathematical Physics | Year: 2010
We describe the behaviour of Fukaya categories under "suspension", which means passing from the fibre of a Lefschetz fibration to the double cover of the total space branched along that fibre. As an application, we consider the mirrors of canonical bundles of toric Fano surfaces. © 2009 Springer-Verlag.
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase II | Award Amount: 528.55K | Year: 2015
The goal of Phase II of this STTR project is to optimize the GaN transistor obtained in Phase I to deliver a 100-W power amplifier with 90% power added efficiency (PAE) at 1 GHz. Emphasis will be put on optimizing output capacitances, improving gate control and further improving epitaxial wafers. We will finalize our design of power amplifier circuit module based on the improved device and our proprietary device models. Device reliability and scalability in output power will be extensively studied.Upon the completion of Phase II base period, we expect to deliver a working prototype of this ultra-high-efficiency PA assembled on a test board. The technology prototype is expected to open up the possibility to replace vacuum-tube-based power amplifiers with solid-state power amplifiers. Such a case will impact a broad range of applications including phase-array radar systems, communication systems, energy transfer modules, and imaging modules in medical equipment. At the end of this project, we will be well positioned to initiate a unique product line that leads the application of solid-state device in ultra-high efficiency power amplifiers for both military and commercial markets.
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 80.00K | Year: 2014
Naval vessels which regularly encounter sub-freezing environments experience superstructure ice accumulation which has negative effects on seaworthiness, deck safety, and ship system performance. In order to mitigate risks to mission success, this ice is currently removed with tools and de-icing solvents through a hazardous manual process with risk of damaging ship components. Ideally, these de-icing challenges would be overcome with a passive ice protection technology that does not require personnel on deck and maintains high performance of ship systems at sub-zero conditions. Technologies based on superhydrophobic surfaces delay drop freezing allowing them to roll off of surfaces before freezing, however, they are readily defeated by frost, snow, and high winds driving drops into the surface, making an additional ice-phobic capability necessary. Researchers at Luna Innovations, in collaboration with engineering professors Cohen and McKinley at Massachusetts Institute of Technology (MIT), have identified a microstructured surface treatment compatible with ship coatings that can provide an unparalleled barrier to ice adhesion. The proposed spray-ready coating formulation is robust, practical and has tunable properties to include transparency and easy-cleaning capabilities.
Agency: Department of Defense | Branch: Army | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2014
Autonomous or teleoperated navigation of unmanned ground vehicles (UGVs) is difficult even in benign environments due to challenges associated with perception, decision making, and human-machine interaction, among others. In environments with rough, sloped, slippery, and/or deformable terrain, the difficulty of the navigation problem increases dramatically. In this effort, Quantum Signal, LLC, University of Michigan, and Massachusetts Institute of Technology propose to collaboratively research methods for robust terrain-adaptive planning and control to enable a future generation of UGVs with assured mobility in highly challenging terrain. The approach will exploit physics-based terrain modeling with data-driven variance estimation, stochastic vehicle motion planning through feasible corridor, and terrain-adaptive predictive vehicle control integrated into a threat-based control arbitration architecture. This architecture will enable operation at (and seamless transition between) any point on the autonomy spectrum, ranging from manual teleoperation to full autonomy. In Phase 1 the team will develop, test, and characterize algorithm performance with Quantum Signal"s high fidelity ANVEL robotic vehicle simulator and determine feasibility. Should the methods prove feasible, Phase 2 will involve the further development, integration, and testing of the methodology on experimental vehicle hardware.