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Mechanical Engineering

Energy Conversion Systems: An Overview

Energy storage systems: A comprehensive review

In this paper, an outline of mean-field homogenization and fracture toughness of boron nitride nanotube-reinforced aluminium and epoxy composites have been presented. The meso-scale material modelling has been achieved using Mori-Tanaka and double inclusion homogenization schemes. To examine the effectiveness of Mori-Tanaka and double inclusion schemes, a comparison has been drawn with the rule of mixture. Meso-scale numerical results of boron nitride nanotube-reinforced aluminium and epoxy composites are presented for various design parameters such as aspect ratio and volume fraction of boron nitride nanotubes. The validation studies of the developed transversely isotropic composites model have been documented with the experiments performed and existing literature. Finally, macro-scale fracture toughness simulations incorporating the transversely isotropic properties have been performed using the domain integral method. It is observed that aluminium and epoxy with elastoplastic and viscoelastic nature, respectively, have shown a significant effect on the fracture properties of the composites. © IMechE 2020.

Fulltext

Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications

Meso–macro-scale computational analysis of boron nitride nanotube-reinforced aluminium and epoxy nanocomposites: A case study on crack propagation

Multichromophoric triads 1 and 2 based on aza-BODIPY as the central chromophore and bay-substituted (tetrachloro- and tetraphenoxy-)perylenediimides (PDI) as peripheral chromophores have been designed and synthesized that are panchromatic absorbers and near infrared (NIR) emitters. Both triads 1 and 2 exhibited ∼99% Förster resonance energy transfer (FRET) from the peripheral PDIs to central aza-BODIPY. The excitation energy transfer from PDI to aza-BODIPY was studied via steady state emission, fluorescence quantum yield, time resolved fluorescence emission and theoretical calculations. These studies revealed quantitative singlet excitation energy transfer efficiencies for 1 and 2. Electrochemical studies revealed the strong electron deficient character of these triads and thus electron mobilities of these triads were measured using space charge limited current (SCLC) method. Triads 1 and 2 exhibited appreciable electron mobilities of 2.44 ± 1.70 × 10-3 cm2 V-1 s-1 and 4.00 ± 1.50 × 10-3 cm2 V-1 s-1 respectively, an order of magnitude higher mobility than aza-BODIPY based small molecules reported in the literature. Leveraging upon the dual emission behaviour of these triads, ratiometric FRET sensing as well as ratiometric temperature sensing behaviour were investigated via steady state absorption and fluorescence measurements. Triads 1 and 2 showed remarkable ratiometric FRET-off sensing where the addition of metals such as Co2+ and Fe3+ led to near-quantitative FRET off for both the triads. Triads 1 and 2 also serve as efficient ratiometric temperature sensors with positive temperature coefficients and small temperature sensitivities of ∼0.29% °C-1 and ∼0.14% °C-1 respectively that suggest the possibility of precise physiological temperature measurements using these triads. © 2021 The Royal Society of Chemistry.

Electrical Engineering

Journal of Materials Chemistry C

Efficient electron transporting and panchromatic absorbing FRET cassettes based on aza-BODIPY and perylenediimide towards multiple metal FRET-Off sensing and ratiometric temperature sensing

Due to the rise in automotive accidents across the globe, a cheap and reliable system is needed that can be retrofitted in any type of vehicle and can monitor road collision events. This research is aimed to develop an IoT system that uses contemporary smartphone’s intrinsic sensors to accurately report vehicle collision accidents on the road. Absolute linear acceleration (ALA) and the speed of the vehicle have been used to train and test our Support Vector Machine (SVM) based collision detection model. During the testing, the accuracy of the model was found to be very high with a MAPE (mean absolute percentage error) of 0.6\%. © 2021, Springer Nature Singapore Pte Ltd.

Computer Science and Engineering

Advances in Intelligent Systems and Computing

Modeling IoT Based Automotive Collision Detection System Using Support Vector Machine

In this work, food waste was treated using a novel method of in situ gasification after pyrolysis (IGP) at the same temperature of 700 °C. This was done with the expectation that IGP would result in a higher hydrogen fraction in syngas as opposed to hydrogen fraction from a more conventional approach of overlapping pyrolysis and gasification (OGP). Firstly, both IGP and OGP were carried out at the same steam flow rate (SFR) of 0.625 mL/min and then only IGP was carried out by changing the flow rate from 0.625 to 0.375 mL/min. Comparative data obtained from OGP and IGP was presented for syngas characteristics (syngas yield, syngas composition, hydrogen yield, high heating value (HHV)). The syngas yield from OGP and IGP at the same flow rate of 0.625 mL/min was found to be 1.31 Nm3/kg and 1.48 Nm3/kg, respectively. The hydrogen fraction in syngas from OGP was found to be 54.5%, which increased significantly to 66.7% in syngas from the gasification part of IGP. Consecutively, HHV increased with an increase in hydrogen fraction in syngas from 12.1 to 17.4 MJ/kg. Additionally, the syngas yield obtained from IGP did not change much on changing the SFR to 0.375 mL/min; however, the hydrogen fraction changed to a lower value of 61.04%. Furthermore, performance parameters such as carbon conversion efficiency (CCE) and cold gas efficiency (CGE) are also presented to evaluate the performance of proposed method. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Chemical Engineering

Biomass Conversion and Biorefinery

Food waste treatment using in situ gasification after pyrolysis to produce hydrogen-rich syngas

Delamination type of failure is extremely common in laminated structures and is a primary reason for failure in many use cases especially in aircrafts. In this project, Machine Learning Techniques were employed to detect the crack size and location in a four-layered laminated structure. The dataset for this problem statement was not available so it had to be generated and to do so ANSYS 18.0 was used. One structural model without any cracks was modeled and a training set with 2000 samples was generated to output the natural frequencies with different crack locations and sizes. Two regressor machine learning architectures with three algorithms (Linear regressor, Random Forest regressor and XGB Regressor) were developed for the prediction task, one to predict the area of the delamination and the other was a multioutput regressor model, which had to predict the X and Y coordinates of the center of the crack. The Random Forest Regressor gave the best generalizability in predicting the area of the delamination although linear regressor was not far behind as it performed quite remarkably given its simplicity. While predicting the locations, linear regressor gave the best test performance although hyperparameter tuning of the random forest and XGB regressor achieved similar results as compared with the linear regressor. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Mechanical Engineering

Delamination Detection and Localization in Laminated Structures Using Machine Learning Techniques

In this paper, a new approach to design an antenna for low radar cross section (RCS) application using microstrip technology is developed. For this purpose, a rectangular patch antenna with rotated C-shaped slot for impedance matching, circular shaped slot in the center of the patch along with Complementary Split Ring Resonator (CSRR) is designed. To achieve a better reduction in the RCS at the desired central frequency, the radiating edges of the antenna are loaded with stepped impedance resonator, as levelled edges of the radiating surfaces contribute to an increased RCS. The antenna radiates at 2.3GHz, has an impedance bandwidth of 44.2MHz and a gain of 4.47dBi. The monostatic RCS is -24.77dBsm at the radiating frequency. Results show that loading the radiating edges with stepped impedance resonator and the addition of the planar metamaterials aid in the reduction of the RCS of the antenna. © 2021 IEEE.

2021 6th International Conference for Convergence in Technology, I2CT 2021

Low RCS Antenna Loaded with Stepped Impedance Resonator and Metamaterials

Reinforcement learning, a sub-field of Machine Learning has been garnering lot of research attention lately. It helps create intelligent agents that can incrementally learn optimal strategies for challenging environments by interacting with it. Such agents are best suited for solving problems like traffic congestion, which demand solutions that cater to dynamic changes in the traffic throughput. Intelligent transportation systems which use deep reinforcement learning can adapt to varying traffic demands and learn to maintain reduced congestion. In this paper, we propose a solution approach to use Double Deep Q Networks for traffic signal control of varied traffic flows in an isolated intersection. To improve the stability of our proposed method we have used target networks, delayed updates and experience replay mechanisms. We evaluate the performance of our method on different time-varying traffic flows and find that our method learns a robust and optimal strategy which reduces vehicle waiting time and queue length significantly. Our method achieved superior performance compared to traditional traffic signal control strategies. The method has been trained and evaluated through simulations of road networks created on Simulation of Urban Mobility (SUMO). © 2021 IEEE

Proceedings of the 8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

Intelligent Traffic Control using Double Deep Q Networks for time-varying Traffic Flows

Placement of items on the shelf space of retail stores signifcantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes. © 2021, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

An Urgency-Aware and Revenue-Based Itemset Placement Framework for Retail Stores

Metamaterials, an artificial periodic two- or three-dimensional configuration, can change propagation characteristics of electromagnetic waves (i.e., reflection, transmission, absorption). The current challenges in the field of metamaterial coatings are their manufacturing in a large-scale and large-length scale. There is a clear need to enhance process technologies and scalability of these. Thermal spraying is a method used to deposit small- to large-scale coatings where the sprayed layer is typically formed by the successive impact of fully or partially molten particles of a material exposed to various process conditions. This work aims to investigate the feasibility to manufacture large scale metamaterial coatings using the thermal spray technique and examine their response to solar radiation. Two types of coatings namely, Cr2O3 and TiO2, were deposited onto various substrates (e.g., steel, aluminium, glass, indium tin oxide (ITO)–coated glass) with a fine wire mesh (143 µm and 1 mm aperture sizes) as the masking sheet to manipulate the surface pattern using suspension high-velocity oxy-fuel thermal spraying (S-HVOF) and atmospheric plasma-sprayed (APS) methods, respectively. Post deposition, their responses subjected to electromagnetic wave (between 250 and 2500 nm or ultraviolet (UV)-visible (Vis)-infrared (IR) region) were characterised. The additional microstructural characterisation was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), three-dimensional profilometry, and optical spectroscopy. It is demonstrated that through novel application of thermal spray techniques, large-scale manufacturing of metamaterial coating is possible, and such material can affect electromagnetic wave propagation. Comparison between Cr2O3 and TiO2 coatings on aluminium substrates showed reduced three orders of reduced reflectance for Cr2O3 coatings (for 1-mm aperture size) throughout the spectrum. It was concluded that for a similar bandgap, Cr2O3 coatings on aluminium substrate will yield improved optical performance than TiO2 coating, and hence more useful to fabricate opto-electronic devices. Graphical abstract: [Figure not available: see fulltext.] © 2021, The Author(s).