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ردیف | عنوان | نوع |
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1 |
Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision
شناسایی و تعیین کمی ترکهای متراکم در کامپوزیت های سیمانی با عملکرد بالا با استفاده از دید رایانه ای مبتنی بر یادگیری عمیق-2021 High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R2 > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks. Keywords: Computer vision | Crack detection | Crack quantification | Deep learning | High-performance fiber reinforced | cementitious composites (HPFRCC) | Microcrack |
مقاله انگلیسی |
2 |
Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion
گرما و قدرت میکرو ترکیبی با استفاده از موتور سوخت دوگانه و بیوگاز از هضم بی هوازی ناپیوسته-2020 The modeling of the Micro-CHP unit operating in dual-fuel mode is performed based on experimental results
carried out at the laboratory scale. The engine tests were performed on an AVL engine, with a maximum power
of 3.5 kW, using conventional diesel as pilot fuel and synthetic biogas as primary fuel. The biogas flow rate is
evaluated using the experimental results from the literature, based on the anaerobic digestion in batch reactor of
a mixture of 26% of Oat Straw and 74% of Cow Manure, diluted to contain only 4% of volatile solid.
The engine operation was modeled using the Artificial Neuron Network (ANN) method. Experimental engine
tests were used as a database for training and validation phases of ANN models. Three different ANN models are
developed to model respectively the pilot fuel flow rate, the airflow rate and the exhaust gas temperature. Engine
power output, biogas flow rate and biogas methane content were used as the same input layer.
Given that the evolution of the biogas flow evolves along the entire digestion duration (50 days), the simulation
work is performed by varying the number of digesters to be used in parallel mode. It is obtained that
the optimal operation condition, minimizing the number of digesters and using less than 10% of the energy from
diesel fuel, is to use 5 digesters and run the engine under load of 70%. It is concluded that a micro-CHP unit of 1
kWe, requires a dual fuel generator with a nominal power of 1 kWe, five digesters and a daily availability of
effluents of 171 kg/day, consisting of 45 kg/day of oat straw and 126 kg/day of cow manure. It can also produce
up to 2.45 kW of thermal power from the exhaust. Keywords: Micro CHP | Anaerobic digestion | Dual fuel engine | Artificial Neural Network | Cogeneration |
مقاله انگلیسی |
3 |
Experimental evaluation of the performance of virtual storage units in hybrid micro grids
ارزیابی تجربی عملکرد واحدهای ذخیره سازی مجازی در شبکه های میکرو ترکیبی-2020 The work presented in this article proposes a new energy management algorithm for hybrid micro grids consisting
of higher penetration of DC non-critical (NC) loads and renewable energy sources. The methodology
suggested in this work is a rule based approach and tries to make the micro grids more autonomous. During
generation deficits in the micro grids, the suggested control strategy proposes to make the hybrid micro grids
self-dependent to a possible extent, without the incorporation of actual storage devices. Instead of using actual
storage elements like the batteries or super capacitors, the projected approach uses virtual storage devices, like
the DC electric springs for its functionality. The electric springs used in this work operate the DC NC loads in
accordance with the voltage produced by the renewable sources, which in turn reduces the power import from
the main grid during generation deficits in the micro grid. Further, the work presented in the due course only
studies about the efficiency of the proposed algorithm by operating the DC NC loads as per the requirement,
without intervening with the AC and DC critical loads operation. In order to test the robustness of the proposed
methodology a scaled down hybrid micro grid is developed in the laboratory using dSPACE 1104 real time
interface. Keywords: Hybrid micro grid | Energy management | Virtual storage units | DC electric spring |
مقاله انگلیسی |