Prediction of thermal buffer zone effectiveness in real‐size buildings—An experimental and analytical study

Global warming is caused by greenhouse gas (GHG) emissions produced from the use of fossil fuel–based energy sources. Buildings consume about 30% to 35% of the global energy use, which makes buildings a major contributor to the global warming problem. A long‐term plan has been established at the Thermal Processing Laboratory (TPL) at McMaster University to investigate the use of various renewable energy–based technologies to achieve net‐zero energy buildings (NZEB) in Canada. This paper presents results of an investigation of the effectiveness of using a thermal buffer zone (TBZ) in real‐size buildings. A TBZ is a closed passage built around the building that allows air to passively redistribute heat energy from solar radiation received on the south side throughout the building. A TBZ offers an effective solution of the overheating problem usually experienced on the south side of the building, and at the same time, it helps in reducing the heating load of the north side of the building. An experimental setup employing TBZ in a lab‐scale model of a typical building floor has been built. An analytical model of the TBZ has been developed. The experimental data has been used to validate the developed analytical model, which then was used to predict the performance of the TBZ implemented in a real‐size building floor, considering four cases. Results of the first three case studies considering the use of TBZ in cold and hot climates, with and without thermal insulation, show that the predicted effectiveness of TBZ could reach 117% and 72.5% in the winter and summer, respectively. Results of the fourth case study considering the effect of integrating a fan with the TBZ show that a fan is beneficial up to a certain fan power, beyond which the use of the fan would not be feasible. Results presented herein confirm that the TBZ is an effective means of integrating solar energy into buildings, thereby reducing buildings' fossil fuel–based energy consumption.     

Synchronization of Fractional Hyperchaotic Rabinovich Systems via Linear and Nonlinear Control with an Application to Secure Communications

International Journal of Control, Automation and Systems - This paper proposes two approaches for the synchronization of a fractional hyperchaotic Rabinovich master-slave pair. The first approach...     

IR Thermographic Analysis of 3D Printed CFRP Reference Samples with Back-Drilled and Embedded Defects

Carbon-fiber composite structures may demonstrate a defective behavior due to manufacturing induced anomalies (delamination, dis-bonds) or service related defectives (impact damage, water ingress). Thus, there is a need for a relatively fast and low cost non-intrusive testing schemes such as infrared thermography (IRT). Still, thermography testing requires calibrated samples and coupons to yield best results. The presented research demonstrates the novel use of 3D printing technology to generate IRT calibration samples. In this text, two carbon fiber reinforced polymer samples are 3D printed; the first mimics a “back-drilled holes” type coupons, while the other is designed to embed air pockets similar to Teflon inserts. The generated samples are then tested using two IRT modalities; namely pulse thermography and lock-in thermography. Furthermore, the resulted thermograms are processed using a principle component analysis, to help highlight the variance of defectives in a consistent manner among the samples. This research findings offer insights on the variation of detectability between embedded and back-printed samples, which might be due to the inserts thickness.     

FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks

The Internet of Things (IoT) has achieved exponential growth worldwide. Although the IoT is used by millions of users, these networks are handicapped by attacks such as denial of service, man in the middle, and spoofing. These attacks threaten the entire IoT ecosystem and affect the integrity and security of the user. Hence, the prediction and identification of novel network attacks in an IoT network remains a challenge for researchers. Recently, machine learning and deep learning have played a pivotal role in predicting and classifying different attacks in an IoT network. However, these algorithms suffer from computational complexity as the number of attacks increases. Hence, a novel hybrid optimized long short-term memory (LSTM) approach is proposed. Whereas a convolutional neural network is used to extract the temporal and spatial correlated features of the IoT network, the optimized LSTM is used to predict the different attacks in the network. Furthermore, firefly swarm optimization is integrated with LSTM to reduce the computational overhead, which in turn increases the prediction accuracy. Nearly 19,00,503 real-time normal and attack data were collected from the experimental simulation setup based on the OMNET++–Python–IoT framework. Extensive experimentation was carried out to evaluate the proposed algorithm, and various metrics, such as accuracy, sensitivity, specificity, and F1-score, were calculated and compared with state-of-the-art learning-based network intrusion detection systems. Furthermore, other benchmarks, such as the CIDCC-15, UNSW-NB15, and NSL-KDD datasets, were used to evaluate the performance of the different deep-learning-based intrusion detection systems. The results demonstrate that the proposed deep-learning method outperforms other classical learning models with low complexity and high prediction performance.

     

Optimization and sensitivity study of biodiesel synthesis from Jojoba oil using mixed-integer programming

Jojoba oil-based biodiesel is promising alternative fuel due to its versatile properties. Renewable transportation fuels are considered as promising alternatives to conventional fuels. The physical and chemical properties of these fuels enabled them to be used in modern internal combustion engines; this makes them attractive for use as direct replacements or as additives of fossil fuels. Jojoba oil is extracted from Jojoba seeds, and it is an excellent feedstock for biodiesel after the transesterification process. The plant is highly adaptable to harsh weather including salty water, desert, and hot temperatures; thus, it can be grown in Saudi Arabia. This research work comprises a detailed optimization study of biodiesel production from Jojoba oil using mixed-integer programming. golden section search method was used for the optimization and sensitivity study was conducted for reaction time and temperature. The result shows that 54.1 minutes and 47.5 °C are the optimized reaction time and temperature to produce biodiesel which is considerably low as compared to previous studies.     

Super HIPS: improved high impact polystyrene with two sources of rubber particles

Significant improvements in impact strength were achieved in polystyrene blends that combined conventional HIPS particles in combination with particles produced by compositional quenching. A commercial HIPS was solvated and blended with additional polystyrene, rubber and diblock copolymer, and the mixture was flash devolatilized to give the end-product. Impact strengths of injection and compression molded samples and tensile properties are reported. It is known that the best impact modified polystyrene obtained by compositional quenching, here called aHIPS, has smaller and lower modulus rubber particles than conventional HIPS, and has more than twice the impact strength of conventional HIPS. The novel blends of HIPS and aHIPS reported here exhibit synergism, the impact strength of the blend being higher than expected as a linear average of the component properties. The rubber phase volume including occlusion was held at 23%. An interior optimum in rubber efficiency (i.e. Izod impact per unit weight of rubber) was observed when 75% of the phase volume was derived from HIPS while an interior minimum was observed when 25% of the phase volume was derived from HIPS. The elongation at break and tensile impact strength exhibited a form of negative synergism, indicating that conventional HIPS is superior in low speed tests and aHIPS is better in high speed tests such as Izod.     

A Taguchi Design of Experiment Approach to Pulse and Lock in Thermography,Applied to CFRP Composites

The current text presents a parametric study of two active thermography routines namely, Pulse and Lock-in as applied to carbon fiber reinforced plastics (CFRP) composites; using a Taguchi design of experiment approach. A set of controllable factors are highlighted and selected for each technique at different levels. Three factors have been identified for the pulse thermography (specifically; defect aspect ratio, pulse period, and experimental duration), and two factors for the Lock-in mode (that is lock in frequency and period); each factor can be manipulated at three different levels. The analysis reveals the effectiveness of the Taguchi design of experiment in consolidating the number of factorial experiments, and in quantifying the results and the associated sensitivity for each factor (its dominance), using a signal to noise ratio criterion. The analysis of variance and analysis of means show that the aspect ratio is not a controlling parameter for the pulse thermography, with the pulse time being the most dominant. Moreover, it decides on the optimal settings for each testing mode. These settings are further validated using additional CFRP artificial sample with eight and six layers of laminates.     

Dynamic output feedback robust MPC for LPV systems subject to input saturation and bounded disturbance

For linear parameter varying (LPV) systems with unknown scheduling parameters and bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with input saturation is investigated. By pre-specifying partial controller parameters, a main optimization problem is solved by convex optimization to reduce the on-line computational burden. The main optimization problem guarantees that the estimated state and estimation error converge within the corresponding invariant sets such that recursive feasibility and robust stability are guaranteed. The consideration of input saturation in the main optimization problem improves the control performance. Two numerical examples are given to illustrate the effectiveness of the approach.     

Supra‐monolayer coverages on small metal clusters and their effects on H2 chemisorption particle size estimates

H₂ chemisorption measurements are used to estimate the size of supported metal particles, often using a hydrogen‐to‐surface‐metal stoichiometry of unity. This technique is most useful for small particles whose sizes are difficult to estimate through electron microscopy or X‐ray diffraction. Undercoordinated metal atoms at the edges and corners of particles, however, make up large fractions of small metal clusters, and can accommodate multiple hydrogen atoms leading to coverages which exceed 1 ML (supra‐monolayer). Density functional theory was used to calculate hydrogen adsorption energies on Pt and Ir particles (38–586 atoms, 0.8–2.4 nm) at high coverages (≤3.63 ML). Calculated differential binding energies confirm that Pt and Ir (111) single‐crystal surfaces saturate at 1 ML; however, Pt and Ir clusters saturate at supra‐monolayer coverages as large as 2.9 ML. Correlations between particle size and saturation coverage are provided that improve particle size estimates from H₂ chemisorption for Pt‐group metals. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3109–3120, 2018     

Microcystins in groundwater wells and their accumulation in vegetable plants irrigated with contaminated waters in Saudi Arabia

The present study describes the presence of toxic cyanobacteria and microcystin (MCYST) concentrations in groundwater wells and tissues of vegetable plants irrigated with well waters in Asir region, southwest of Saudi Arabia. The results revealed the presence of cyanobacteria in all groundwater wells with a dominance of Oscillatoria limentica. This species was found to produce MCYSTs at a concentration of 336 μg g⁻¹ as determined by enzyme-linked immunosorbent assay (ELISA). HPLC chromatogram for the methanolic extract of this species showed one main peak corresponding to MCYST-YR. MCYSTs were also detected in well waters at concentrations (0.3–1.8 μg L⁻¹), exceeding the WHO guideline level (1 μg L⁻¹) in some wells. All vegetable plants collected during the present study were found to accumulate MCYSTs in their leaves and roots at concentrations ranged from 0.07 to 1.2 μg g⁻¹ fresh weight. The study suggests that ground waters and vegetable plants should be continuously monitored for the presence of MCYSTs to protect the public against the exposure to such potent hepatotoxins.