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Farzad A. Shirazi

Researcher at University of Tehran

Publications -  55
Citations -  575

Farzad A. Shirazi is an academic researcher from University of Tehran. The author has contributed to research in topics: Turbine & Compressed air energy storage. The author has an hindex of 11, co-authored 51 publications receiving 431 citations. Previous affiliations of Farzad A. Shirazi include University of Minnesota & University of Houston.

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Experimental study of heat transfer enhancement in a liquid piston compressor/expander using porous media inserts

TL;DR: In this paper, an experimental investigation on heat transfer with porous media inserts during compression and expansion was conducted for a pressure ratio of 10 and 6, respectively, and the results showed that the surface area increase was the predominant cause for the improvement in performance.
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Modeling and control of an open accumulator Compressed Air Energy Storage (CAES) system for wind turbines

TL;DR: In this article, a compressed air energy storage (CAES) system for wind turbines is presented, which captures excess power prior to electricity generation so that electrical components can be downsized for demand instead of supply.
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Wind turbine integrated structural and LPV control design for improved closed-loop performance

TL;DR: The FAST closed-loop simulations for two selected designs with the smallest values of the performance index demonstrate the improved performance of the overall system through the integrated structure/control redesign in both minimising the effect of the wind disturbance on the generator output power, and reducing the structural loads on the wind turbine.
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Identification and Control of an MR Damper With Stiction Effect and its Application in Structural Vibration Mitigation

TL;DR: The parameter identification and control of a magnetorheological (MR) damper with stiction effect and its application to seismic protection of a model two-story structure is presented and the improved performance of the LPV controller design is shown in terms of the maximum acceleration and the RMS values of the structure response.
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Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network:

TL;DR: A two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines and verified that vibration signals acquired from intake manifold have more potential in fault detection.