21 research outputs found
Self-assembled Nano-BiFeO3 Chemi-resistive VOC Sensor: A Non-conventional MOS Sensor Highly Selective toward Acetone
No full text
Automatic Tuning of Wind Tubrine controller
No full textThe energy demand in current times has increased greatly in last few years. This increasing demand calls for a sustainable and clean energy resource that would reduce the load on non-renewable resources. Wind energy is a renewable resource which is harnessed by mankind from an ancient era. So as to meet this increasing energy demands, innovation in the field of wind energy is required.A wind turbine generates electricity but achieving optimal power is a difficult task. In this case, the optimal power is defined as maximum power produced but at the constraints that the fatigue loading of the wind turbine structure should be as minimum as possible. Also, the wind turbine parameters such as rotor speed and pitch activity should be in a safe operational region. The problem in controlling wind turbines is that they work in a highly uncertain environment where managing so many factors at the same time are difficult. Optimal control of wind turbine has helped in achieving maximum power with-in safe working limits. Due to high uncertainty in the operating conditions of a wind turbine, it is quite a daunting task to find optimal gains for a wind turbine controller.This thesis focuses on achieving optimal gain parameters for wind turbine controller by using an algorithm from machine learning community. In this thesis the problem is formulated as a supervised learning problem where input-output mapping has to be predicted. For this purpose, GPRT is used. The reason behind using GPRT is it takes fewer number of measurements to give good prediction compared to others. The property of GPRT where it deals with uncertain and non linear data with ease, making it a good choice for predicting wind turbine controller gains.The second part of this thesis contains optimisation of the surrogate model achieved by performing regression. The optimisation is done by Monte Carlo Maximum distribution and improved results were generated by applying sequential sampling to this algorithm. This helps us to get a likelihood of optimal gains where the wind turbine gives out rated power with minimal fatigue loads, pitch activity and least deviation of rotor speed from rated.The results obtained from the likelihood was tested for different operational wind speed and also tested for Extreme Operating Gusts as part of disturbance rejection and compared to current parameter used.The comparison shows considerable improvement in the fatigue loads and pitch activity with having improvement in power production. In second case study, more parameters were predicted and optimised using the same algorithm so that the potential of this algorithm can be estimated. This was also performed successfully which proves that this technique can successfully be used to solve higher dimensional problems of wind turbine control.Mechanical Engineering | Systems and Contro
Effect of mechanical milling on the structural and dielectric properties of BaTiO3 powders
No full textBarium titanate (BaTiO3) is a well-known ferroelectric material and widely used in electronic industries for the multi-layer ceramic capacitor. In this reported work, commercially available tetragonal BaTiO3 (BT) powders were taken to study the size effect on the structural and dielectric properties of the BT ceramics during high-energy ball milling (0-110 h). The same perovskite when kept under a normal atmospheric condition after milling shows gradual increase of additional crystalline phase that occurred because of the absorption of atmospheric CO2 gas, which is characterised as orthorhombic BaCO3. The milled BT samples were characterised by X-ray diffraction and small-angle X-ray scattering and a dielectric analyser. The purpose of this work was to study how the dielectric property of nanoBT ceramics varies with reduction of particle size, structural changes and the absorption of carbon by these nanopowders. It was observed that the dielectric constant of the BT powders increases with particle size reduction during milling. The dielectric behaviour of the BT ceramics significantly changes with polymorphic phase transformation in nanocrystalline BT at different stages of milling. The capacitance of nanoBT powders is significantly changed with the absorption of carbon by the nanoBT powders in a humid atmosphere
Material dependent and temperature driven adsorption switching (p- to n- type) using CNT/ZnO composite-based chemiresistive methanol gas sensor
No full textThe present study correlates two simultaneous as well as significant observations coming out from a single sensing prototype concerning the detection of volatile organic compounds (VOCs) by a carbonaceous material based sensor. We have developed a composite based chemiresistive sensor utilizing two different components (carbon nanotube (CNT) and zinc oxide (ZnO)). This is reflected in a unique adsorption switching phenomena followed by a ‘p- to n-’ type transition characteristics above a certain operating temperature (150 °C) in the VOC detection process. Noticeably, by the virtue of adsorption switching, the CNT/ZnO composite is able to operate as a dual mode sensor, in which CNT dominates in low temperature region (≤ 150 °C) and ZnO at high temperature region (>150 °C) with a realistic detection ability. The highly reproducible sensors (29 prototypes) are selective towards methanol (Response, R ∼ 73 ± 3 %) and shows 8-fold enhancement in response value compared to neighbouring VOC i.e., ethanol at an operating temperature of 150 °C with a very low bias voltage of 10 mV. Finally, the adsorption switching phenomena (physisorption to chemisorption) has been explained by Fourier Transform Infrared Spectroscopy (FTIR) study and activation energy values along with ‘p- to n-’ type transition is compared qualitatively with a typical full wave rectification process
Misspelled Author Name in the Article by Neogi et al (Arthritis Rheumatol, October 2015)
No full textRoom temperature multiferroicity in orthorhombic LuFeO3
No full textFrom the measurement of dielectric, ferroelectric, and magnetic properties, we observe simultaneous ferroelectric and magnetic transitions around similar to 600K in orthorhombic LuFeO3. We also observe suppression of the remanent polarization by similar to 95% under a magnetic field of similar to 15 kOe at room temperature. The extent of suppression of the polarization under magnetic field increases monotonically with the field. These results show that even the orthorhombic LuFeO3 is a room temperature multiferroic of type-II variety exhibiting quite a strong coupling between magnetization and polarization. (C) 2014 AIP Publishing LLC
Ultrafast, Highly Sensitive, and Selective Detection of p-Xylene at Room Temperature by Peptide-Hydrogel-Based Composite Material
No full textA peptide/carbon dot (CD) composite xerogel is used as a selective p-xylene VOC (volatile organic compound) sensor. The fiber formation by the peptide allows us to attain a semiconducting property, whereas the presence of the CD amplifies the sensitivity. The selective detection of p-xylene is achieved at a very low concentration (response ≈ 96% for 50 ppm) with an ultrafast response (630 ms) and recovery (540 ms). The sensor is also able to detect p-xylene within crude oil, proving its industrial application. In comparison with the available VOC sensors, this work stands out as a low-cost, sensitive, and selective room-temperature p-xylene sensor with ultrafast sensing ability
Incorrect Disclosure Information Added for Author Neogi in the Article by Choi et al (Arthritis Rheumatol, November 2018)
No full textUltrafast, Highly Sensitive, and Selective Detection of <i>p</i>‑Xylene at Room Temperature by Peptide-Hydrogel-Based Composite Material
No full textA peptide/carbon
dot (CD) composite xerogel is used as a selective p-xylene VOC (volatile organic compound) sensor. The fiber
formation by the peptide allows us to attain a semiconducting property,
whereas the presence of the CD amplifies the sensitivity. The selective
detection of p-xylene is achieved at a very low concentration
(response ≈ 96% for 50 ppm) with an ultrafast response (630
ms) and recovery (540 ms). The sensor is also able to detect p-xylene within crude oil, proving its industrial application.
In comparison with the available VOC sensors, this work stands out
as a low-cost, sensitive, and selective room-temperature p-xylene sensor with ultrafast sensing ability
