3 resultados para irrigation intensity

em CaltechTHESIS


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One of the critical problems currently being faced by agriculture industry in developing nations is the alarming rate of groundwater depletion. Irrigation accounts for over 70% of the total groundwater withdrawn everyday. Compounding this issue is the use of polluting diesel generators to pump groundwater for irrigation. This has made irrigation not only the biggest consumer of groundwater but also one of the major contributors to green house gases. The aim of this thesis is to present a solution to the energy-water nexus. To make agriculture less dependent on fossil fuels, the use of a solar-powered Stirling engine as the power generator for on-farm energy needs is discussed. The Stirling cycle is revisited and practical and ideal Stirling cycles are compared. Based on agricultural needs and financial constraints faced by farmers in developing countries, the use of a Fresnel lens as a solar-concentrator and a Beta-type Stirling engine unit is suggested for sustainable power generation on the farms. To reduce the groundwater consumption and to make irrigation more sustainable, the conceptual idea of using a Stirling engine in drip irrigation is presented. To tackle the shortage of over 37 million tonnes of cold-storage in India, the idea of cost-effective solar-powered on-farm cold storage unit is discussed.

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In this thesis, we develop an efficient collapse prediction model, the PFA (Peak Filtered Acceleration) model, for buildings subjected to different types of ground motions.

For the structural system, the PFA model covers modern steel and reinforced concrete moment-resisting frame buildings (potentially reinforced concrete shear wall buildings). For ground motions, the PFA model covers ramp-pulse-like ground motions, long-period ground motions, and short-period ground motions.

To predict whether a building will collapse in response to a given ground motion, we first extract long-period components from the ground motion using a Butterworth low-pass filter with suggested order and cutoff frequency. The order depends on the type of ground motion, and the cutoff frequency depends on the building’s natural frequency and ductility. We then compare the filtered acceleration time history with the capacity of the building. The capacity of the building is a constant for 2-dimentional buildings and a limit domain for 3-dimentional buildings. If the filtered acceleration exceeds the building’s capacity, the building is predicted to collapse. Otherwise, it is expected to survive the ground motion.

The parameters used in PFA model, which include fundamental period, global ductility and lateral capacity, can be obtained either from numerical analysis or interpolation based on the reference building system proposed in this thesis.

The PFA collapse prediction model greatly reduces computational complexity while archiving good accuracy. It is verified by FEM simulations of 13 frame building models and 150 ground motion records.

Based on the developed collapse prediction model, we propose to use PFA (Peak Filtered Acceleration) as a new ground motion intensity measure for collapse prediction. We compare PFA with traditional intensity measures PGA, PGV, PGD, and Sa in collapse prediction and find that PFA has the best performance among all the intensity measures.

We also provide a close form in term of a vector intensity measure (PGV, PGD) of the PFA collapse prediction model for practical collapse risk assessment.

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Part I

The spectrum of dissolved mercury atoms in simple liquids has been shown to be capable of revealing information concerning local structures in these liquids.

Part II

Infrared intensity perturbations in simple solutions have been shown to involve more detailed interaction than just dielectric polarization. No correlation has been found between frequency shifts and intensity enhancements.

Part III

Evidence for perturbed rotation of HCl in rare gas matrices has been found. The magnitude of the barrier to rotation is concluded to be of order of 30 cm^(-1).