Current Approaches in Engineering Research and Technology Vol. 7

The concrete for the Building structure is required to have strong properties in holding the load or working forces. Problems that occur in concrete buildings in the coastal environment of concrete damage caused by the degradation of concrete that interacted with seawater. The durability deterioration of concrete is critical to the durability, safety, and sustainability of infrastructures, especially for offshore concrete structures in marine environments. Real damage caused by seawater, consists of 3 (three) parts: submerged concrete part, concrete part affected by tidal seawater and part of the concrete affected by seawater splashing. The concrete damage discussed in this research is the mechanical degradation of concrete due to the intrusion of seawater in the submerged seawater. Damage can occur in concrete due to the reaction between aggressive seawater that is intruded into the concrete and compounds in the concrete that cause the concrete to lose some of its mass, strength, and stiffness and also accelerate the weathering process. This research used 2 media ponds, where one filled with seawater and the other filled with fresh water. The amount of concrete soaked in seawater as much as 20 specimens, and soaked freshwater (PDAM) as much as 20 specimens. The amount of concrete soaked in seawater as much as 20 specimens, and soaked freshwater as much as 20 specimens. The results of this study indicate that the reduction of compressive strength of concrete due to the intrusion of seawater tends to show a logarithmic graph. Concrete compressive strength will be lost by 50% when the concrete is soaked in seawater for 19,031 days. Normal quality concrete f'c = 25 MPa undergoes mechanical degradation due to seawater intrusion, in this case, the compressive strength of concrete is 12,063% when soaked for 28 days and 16,809% when soaked for 90 days.

The present study addresses the implementation challenges in Feedforward neural network (FFNN) architecture design using the most efficient resources. Power quality (PQ) issues are a major concern in the present day. FPGAs (Field programmable gate arrays) are essential in PQ analysis, particularly in smart meters for data processing, storage, and transmission. Artificial neural networks are selected for PQ event classification as they are found to be more robust once they are trained with a large number of data sets. FPGA's reconfigurability, which makes use of abundant hardware resources to create intricate and time-sensitive data processing units, is one of its greatest features. Due to data loss in the data channel unit caused by the FPGA architecture's support for fixed point arithmetic, the PQ event detection module and classification model must be realized with greater accuracy than software implementation approaches. Most of the work reported, with FFNN (Feedforward neural network) structure occupying a large number of multipliers and adders for classification, most of the work reported has not addressed to minimize the data path resources for FFNN instead have addressed in improving classification accuracy. Based on these issues, this paper addresses the implementation challenges in FFNN architecture design by proposing improved and fast architectures. The proposed FFNN architecture design uses optimum resources. The FFNN-based classifier is designed to perform PQ event detection and classification with 99.5% accuracy. The FFNN processor operates at the maximum frequency of 238 MHz. The present paper addresses the hardware implementation of FFNN cores on the FPGA platform. Interfacing FFNN with all other glue logic modules will require FIFO architecture and a data synchronization network.  The proposed designs for FFNN can be used as IP cores for any signal and control applications.

The purpose of this review paper is to overview of the past investigations of the design criteria of vortex tubes, to draw together the mass of literature, and to provide detailed information on the design of vortex tubes. The vortex tube, also known as the Ranque-Hilsch vortex tube (RHVT), is a mechanical device that separates compressed air into hot and cold streams flowing in vortex motion/tangentially into the vortex chamber through inlet nozzles. This instrument has no moving components, does not break or wear, and hence requires minimum maintenance. The vortex tube is ideal for a variety of applications since it is simple, lightweight, quiet, and small, and it does not need Freon or other refrigerants (HFCs or CFCs). Though the vortex tube is simple in structure, the internal mechanism of airflow and energy transfer is very complex. A series of theories have been presented by researchers regarding the energy flow inside the vortex tube. This paper after a brief introduction and literature survey, focuses on the design criteria, applications of RHVT and the results obtained up to now. One of the objectives of this paper is to highlight the application of this device and its usage in the industry, rough estimates of temperature that are achieved and which be used in particle applications. The review will conclude with comments on future directions that this device can be used and can be applied for further study and investigation. By reviewing most of the theories, predicting the vortex tube performance and showing the common features and promising add-ons, this review paper could set the basis for the development of the first commonly accepted design model of counter-flow vortex tubes working with perfect gas for spot cooling application.

The Al-Mg-Si alloy is recognized as an engineering material currently gaining attention in the aerospace industry due to its high strength-to-weight ratio. The welding of these materials has been recently improved using a technique known as thixo-joining, also referred to as semi-solid welding. To investigate the susceptibility of the welded Al-Mg-Si to corrosion in acidic and marine environments, various welding temperatures (34°C, 100°C, 200°C, and 300°C) were utilized before exposure to these harsh conditions. The preparation of the coupons adhered to accepted standards and they were subsequently subjected to a weight loss gravimetric and linear polarization technique for corrosion investigation. Observations indicated improved corrosion resistance when the gravimetric approach (weight loss plot) was used. The presence of globules, in comparison with the parent metal (PM), served as crack arresters to the structures, thus reducing the susceptibility to corrosion in both marine and acidic environments.

The novel solitary electromagnetic wave (SEMW) theory and the design methodologies of the switching mode power-supply circuit (SMPC) are presented. The novel SEMW theory which was developed by fusing semiconductor physics, nonlinear undulation physics, and electromagnetic wave (EMW) physics is the basic theory of the circuit design of all kinds of switching mode circuits (SMC) including SMPC. When the SEMW theory is used, the electromagnetic analysis of SMPC becomes possible by using only parameters that are in accordance with the physics without using the equivalent circuit which is formed by the arbitrary idea. The low impedance lossy line (LILL) and the matched impedance lossy line (MILL) are also presented. They are the typical technologies that were developed based on the SEMW theory. LILL is effective for suppressing the electromagnetic noise and the spike on the unregulated DC line (UDCL) and the regulated DC line (RDCL) on SMPC, and an ideal DC source is provided to the switching device by it at a short distance. The MILL is effective for suppressing the electromagnetic noise and the spike on the switching DC line (SDCL) on SMPC. SMPC can be reconfigured to the quasi-stationary state closed circuit (QSCC) by applying LILL and MILL. LILL and MILL will provide the boundary condition for the electromagnetic analysis of SMC. The buck converter which is one of the most popular DC-DC converters is presented as an example of the method for being reconfigured to QSCC. Normally, the electromagnetic effect and the inductive element of the wire can be ignored on QSCC. When the accurate analysis of the transmission delay of the wire is necessary, the easy method using the element of the capacitance and the resistance can be applied like a design method of on-chip interconnect. As a result, it can say that the conventional AC circuit theory and the circuit simulator such as SPICE were the effective methods of being utilizable on QSCC because the switching time of the device was quite long at this time when these theories and the tools were developed. The state space averaging method for the analysis of the stability control of SMPS will become more reliable when SMPS is reconfigured to QSCC.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are aircraft operated without an onboard human pilot. They have a wide range of applications, including military operations, emergency response such as firefighting, and various civilian uses, making them a significant focus of ongoing research. This study aims to develop a cost-effective and efficient communication system between transmitters and receivers for UAVs, enhancing their operational capabilities over long distances to reduce response times in critical scenarios such as military operations, firefighting, and medical emergencies. We propose using 2.4 GHz RF modules for the transmitter and receiver systems, providing 256 channels for reliable and economical long-range data transmission. Integrating multiple transmitters with a single receiver further enhances the system's functionality. The nRF modules consume current between 12.63 mA and 15.94 mA, demonstrating reliable operation with an average communication range of 750 to 950 meters between transmitter and receiver. This performance was evaluated using a Quadcopter Drone. Additionally, upgrading the nRF front-end module from 2.4 GHz to 5.7 GHz is explored to extend the transmission range and improve overall system performance.

Objectives: This paper proposed to enhance the performance of Direct Torque Control (DTC) CSI fed Interior Permanent Magnet Synchronous Motor (IPMSM) drive with online stator resistances estimation using a PI controller.

Background: In CSI drives, the DC link reactor limits the rate of rise of current under short circuit conditions, so the drives can be easily protected under short circuits and thus result in improved reliability of the drive.

Methods/Analysis: Direct Torque Control (DTC) CSI fed IPMSM drives are widely used in the industry because of their high dynamic performance and the need for position sensors. This method needs fewer parameters to estimate the motor torque and stator flux linkages. The estimation is dependent only on the machine parameter such as stator resistance.

Results: At low-speed operation, the stator resistance varies due to temperature and frequency, which degrades the system performance by introducing errors in flux linkage and motor torque. Hence the stator resistance compensation is essential at low speed. The error between the estimated stator flux and its reference value is used to estimate the stator resistance by using the PI controller. For the first time, it is demonstrated in this paper for the DTC - CSI fed IPMSM drive system. Due to the change in stator resistance, the parameter stator flux and electric torque mismatch between the set value and estimated value results in the drive system becoming unstable. Therefore, the overall performance of the DTC system is degrading. To overcome this stator resistance compensation is essential.

Conclusion: The performance of the stator resistance estimators and torque and flux responses of the drive are investigated with the help of MATLAB/SIMULINK software and hardware. The results show that the proposed method ensures reliability and performs well during low and high operations. The design of a PI-controlled stator resistance estimator is easier compared to other controllers and it is implemented in DTC-CSI-fed IPMSM drives very first time. The proposed control system was to deliver high performance at low-speed operations.

Power distribution systems (PDS) have gained less attention in the past than in comparison with transmission and distribution. The rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs pose growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. According to customer failure statistics from major utilities, the maximum unavailability of electrical power to consumers is due to distribution system outages. Power suppliers are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metric for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in a distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. Layer-by-layer learning allows a deep belief network (DBN) to absorb feature specifics from huge amounts of data. This study introduces a deep belief neural network (DBNN) optimized with particle swarm optimization (PSO) for distribution network reliability analysis. A 16-bus system is taken for reliability analysis. The input data and output data are obtained from the reliability analysis code. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate. The reliability of power distribution networks may also be investigated using an optimized DBN model, which can then be used on a variety of grid configurations in distribution networks.

The narrative of work has changed globally since the emergence of the COVID-19 pandemic in the year 2020 till date. The Work from Home (WFH) model which took various businesses time to effectively adapt became popular following the lockdown of over 9 months globally. The Architecture, Engineering, and Construction (AEC) Sector was not left out. Organizations that could adapt however recorded significant operational cost savings with the WFH model and continued with a hybrid work model after the relaxation of the lockdown. With either the WFH or the hybrid work model, the combination of Building Information Modelling (BIM) and Cloud Computing technologies became unavoidable by AEC firms for effective collaboration and seamless productivity. This study examines the role of BIM and Cloud Computing in successfully implementing a hybrid work model for AEC firms. It also examines the readiness of firms to operate the hybrid work model and the possible barriers to its successful implementation. Quantitative data was gathered from registered architectural firms using a close-ended structured questionnaire. Using a simple random sampling method, a total of 140 survey responses were analyzed using descriptive statistics and the Relative Importance Index (RII). Findings revealed that the utilization level of the cloud-based file storage and sharing system within the BIM work process is still very low among firms with less than 20% of the respondents actively operating on a cloud-based server. The study also revealed high BIM setup and maintenance cost as the greatest barrier for firms in transitioning to a cloud-based system with a mean score of 4.74 and an RI value of 0.95. The study recommends greater awareness of the cloud server system amongst firms’ top-level management for increased financial investment for daily operational cost savings that the hybrid work model offers.

Aquaplaning is a phenomenon that occurs when a layer of water builds up between the tyres of a vehicle and the road surface, leading to a loss of traction and preventing the vehicle from responding to control inputs such as steering, braking, or accelerating. This can lead to serious road accidents, making it crucial to study tyre-pavement interactions to devise optimal methods for preventing hydroplaning. This paper presents a study of the expulsion process of viscous fluid from under a solid plane using the general differential equation of pressure in the viscous fluid layer. The analysis focuses on the aquaplaning process of an automobile's wheel rolling on a wet path. The study also considers a tyre with a tread composed of insulated profile blocks of circular shape. Key findings include the identification of critical speeds for aquaplaning and the effects of tyre tread design and water depth. The results have significant implications for improving tyre designs and enhancing vehicle safety on wet roads.