Cooling Performance of a Novel Circulatory Flow Concentric Multi-Channel Heat Sink with Nanofluids

Heat rejection from electronic devices such as processors necessitates a high heat removal rate. The present study focuses on liquid-cooled novel heat sink geometry made from four channels (width 4 mm and depth 3.5 mm) configured in a concentric shape with alternate flow passages (slot of 3 mm gap). In this study, the cooling performance of the heat sink was tested under simulated controlled conditions.The lower bottom surface of the heat sink was heated at a constant heat flux condition based on dissipated power of 50 W and 70 W. The computations were carried out for different volume fractions of nanoparticles, namely 0.5% to 5%, and water as base fluid at a flow rate of 30 to 180 mL/min. The results showed a higher rate of heat rejection from the nanofluid cooled heat sink compared with water. The enhancement in performance was analyzed with the help of a temperature difference of nanofluid outlet temperature and water outlet temperature under similar operating conditions. The enhancement was ~2% for 0.5% volume fraction nanofluids and ~17% for a 5% volume fraction

Studying the C–H crystals and mechanical properties of sustainable concrete containing recycled coarse aggregate with used nano-silica

The present study aims to replace 30%, 40%, and 50% of the natural coarse aggregate (NCA) of concrete with recycled coarse aggregate containing used nano-silica (RCA-UNS) to produce a new sustainable concrete. Three groups of concrete are made and their mechanical properties and microstructure are studied. In the first group, which was the control group, normal concrete was used. In the second group, 30%, 40%, and 50% of the NCA were replaced with coarse aggregate obtained from crushed concrete of the control samples and with 0.5% nano-silica as filler. In the third group, 30%, 40%, and 50% of the concrete samples’ NCA were replaced with aggregates obtained from 90-day crushed samples of the second group. Water absorption, fresh concrete slump, and compressive strength of the three groups were investigated and compared through scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) tests. The results show that the third group’s compressive strengths increased by 12.8%, 10.9%, and 10% with replacing 30%, 40%, and 50% of NAC with RCA-NS at 28 days compared to the control samples, respectively. This could be due to the secondary production of calcium silicate hydrate due to the presence of new cement paste. The third group’s microstructure was also improved due to the change in the C–H and the production of extra C–S–H. Therefore, the hydration of cement with water produces C–H crystals while reactions are induced by recycled aggregate containing used nano-silica

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries

Toward designing a secure authentication protocol for IoT environments

Authentication protocol is a critical part of any application to manage the access control in many applications. A former research recently proposed a lightweight authentication scheme to transmit data in an IoT subsystem securely. Although the designers presented the first security analysis of the proposed protocol, that protocol has not been independently analyzed by third-party researchers, to the best of our knowledge. On the other hand, it is generally agreed that no cryptosystem should be used in a practical application unless its security has been verified through security analysis by third parties extensively, which is addressed in this paper. Although it is an efficient protocol by design compared to other related schemes, our security analysis identifies the non-ideal properties of this protocol. More specifically, we show that this protocol does not provide perfect forward secrecy. In addition, we show that it is vulnerable to an insider attacker, and an active insider adversary can successfully recover the shared keys between the protocol’s entities. In addition, such an adversary can impersonate the remote server to the user and vice versa. Next, the adversary can trace the target user using the extracted information. Finally, we redesign the protocol such that the enhanced protocol can withstand all the aforementioned attacks. The overhead of the proposed protocol compared to its predecessor is only 15.5% in terms of computational cost

Cryptanalysis of two recent ultra-lightweight authentication protocols

Radio Frequency Identification (RFID) technology is a critical part of many Internet of Things (IoT) systems, including Medical IoT (MIoT) for instance. On the other hand, the IoT devices’ numerous limitations (such as memory space, computing capability, and battery capacity) make it difficult to implement cost- and energy-efficient security solutions. As a result, several researchers attempted to address this problem, and several RFID-based security mechanisms for the MIoT and other constrained environments were proposed. In this vein, Wang et al. and Shariq et al. recently proposed CRUSAP and ESRAS ultra-lightweight authentication schemes. They demonstrated, both formally and informally, that their schemes meet the required security properties for RFID systems. In their proposed protocols, they have used a very lightweight operation called Cro(·) and Rank(·), respectively. However, in this paper, we show that those functions are not secure enough to provide the desired security. We show that Cro(·) is linear and reversible, and it is easy to obtain the secret values used in its calculation. Then, by exploiting the vulnerability of the Cro(·) function, we demonstrated that CRUSAP is vulnerable to secret disclosure attacks. The proposed attack has a success probability of "1" and is as simple as a CRUSAP protocol run. Other security attacks are obviously possible by obtaining the secret values of the tag and reader. In addition, we present a de-synchronization attack on the CRUSAP protocol. Furthermore, we provide a thorough examination of ESRAS and its Rank(·) function. We first present a de-synchronization attack that works for any desired Rank(·) function, including Shariq et al.’s proposed Rank(·) function. We also show that Rank(·) does not provide the desired confusion and diffusion that is claimed by the designers. Finally, we conduct a secret disclosure attack against ESRAS

Numerical study of a novel ventilation system added to the structure of a catamaran for different slamming conditions using OpenFOAM

Large-size catamarans' structural behavior is sensitive and critical during the slamming phenomenon. “Ventilation pipes” within the center bow structure are proposed to discharge these cumulative pressure and related loads. The validation case is comprising two different simulation schemes, static and dynamic wedge. First, the appropriate method is chosen based on the accuracy and needed computational running time criteria. The numerical solution approach solves the RANS equation using the Open Field Operation and Manipulation (OpenFOAM) library called “InterFoam and OverInterDyMFoam for static and dynamic mesh respectively. Totally three different impact conditions with four different impact velocities (12 case studies) were considered for the case with added ventilation pipes (amended hull) and the standard model (parent-hull). Apart from the limitation of the proposed plan which is discussed, the results indicate that the recorded pressure and total force decreases by about (15%–50%), and (5%–25%) respectively

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones

Crack propagation modeling of strengthening reinforced concrete deep beams with CFRP plates

Fracture analysis of reinforced concrete deep beam strengthened with carbon fiber-reinforced polymer (CFRP) plates was carried out. The present research aimed to discover whether crack propagation in a strengthened deep beam follows linear elastic fracture mechanics (LEFM) theory or nonlinear fracture mechanics theory. To do so, a new energy release rate based on nonlinear fracture mechanics theory was formulated on the finite element method and the discrete cohesive zone model (DCZM) was developed in deep beams. To validate and compare with numerical models, three deep beams with rectangular cross-sections were tested. The code results based on nonlinear fracture mechanics models were compared with the experimental results and the ABAQUS results carried out based on LEFM. The predicted values of initial stiffness, yielding point and failure load, energy absorption, and compressive strain in the concrete obtained by the proposed model were very close to the experimental results. However, the ABAQUS software results displayed greater differences from the experimental results. For instance, the predicted failure load for the shear-strengthened deep beam using the proposed model only had a 6.3% difference from the experimental result. However, the predicted failure load using ABAQUS software based on LEFM indicated greater differences (25.1%) compared to the experimental result

Mechanical and Fracture Parameters of Ultra-High Performance Fiber Reinforcement Concrete Cured via Steam and Water: Optimization of Binder Content

An investigational study is conducted to examine the effects of different amounts of binders and curing methods on the mechanical behavior and ductility of Ultra-High Performance Fiber Reinforced Concretes (UHPFRCs) that contain 2% of Micro Steel Fiber (MSF). The aim is to find an optimum binder content for the UHPFRC mixes. The same water-to-binder ratio (w/b) of 0.12 was used for both water curing (WC) and steam curing (SC). Based on the curing methods, two series of eight mixes of UHPFRCs containing different binder contents ranging from 850 to 1200 kg/m3 with an increment of 50 kg/m3 were produced. Mechanical properties such as compressive strength, splitting tensile strength, static elastic module, flexural tensile strength and the ductility behavior were investigated. This study revealed that the mixture of 1150 kg/m3 binder content exhibited the highest values of the experimental results such as a compressive strength greater than 190 MPa, a splitting tensile strength greater than 12.5 MPa, and a modulus of elasticity higher than 45 GPa. The results also show that all of the improvements began to slightly decrease at 1200 kg/m3 of the binder content. On the other hand, it was concluded that SC resulted in higher mechanical performance and ductility behavior than WC

The Effect of Incorporating Silica Stone Waste on the Mechanical Properties of Sustainable Concretes

Incorporating various industrial waste materials into concrete has recently gained attention for sustainable construction. This paper, for the first time, studies the effects of silica stone waste (SSW) powder on concrete. The cement of concrete was replaced with 5, 10, 15, and 20% of the SSW powder. The mechanical properties of concrete, such as compressive and tensile strength, were studied. Furthermore, the microstructure of concrete was studied by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy analysis (EDX), Fourier transformed infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) tests. Compressive and tensile strength of samples with 5% SSW powder was improved up to 18.8% and 10.46%, respectively. As can be observed in the SEM images, a reduced number of pores and higher density in the matrix can explain the better compressive strength of samples with 5% SSW powder