International Journal of Engineering
Volume:33 Issue: 11, Nov 2020

In this study, the feasibility of using the developed design for the removal of organic pollutants from wastewater was examined. The design includes the integration of the work of both pulsation bubble column (PBC) and the inverse fluidization airlift loop reactor (IFALR). The experimental podium was fabricated and installed which that consists of a bubble column with a diameter of 5 cm and a height of 210 cm,  contains at the top a solenoid valve which is electrically turned via at least two timers, and its connection with the loop reactor by a one-way valve. The loop reactor consists of an outer rectangular tube with dimensions (29 cm long x 15.5 cm wide x 150 cm high) and an internal draft tube with 9 cm diameter and 120 cm long as granular activated carbon is put as an adsorbent in the annulus region between the inner and outer tube. Experiments were conducted using one of the organic pollutants namely chloroform, with a work scenario that includes changing both the airflow rate (2-20) liters/minute, the total survival time of the treatment (5-60) minutes, the molar ratio of the chloroform pollutant to the oxidizing agent of hydrogen peroxide (1/10 - 1/20). The results showed removal efficiency near to 89%, and it gives an indication of the success of the proposed design, with the possibility of recycling the treated water and releasing it to the environment due to the low risk of the organic pollutant in it.

In the most structural codes, deformation capacity of the unreinforced masonry shear walls is estimated based on their structural behavior(failure mode) and aspect ratio. In this paper, deformation capacity was determined for the Persian historic brick masonry walls by considering the effects of various parameters such as lateral constraints, aspect ratio and thickness. Also, to take into account the uncertainties in material and geometry of the walls in their deformation capacity, partial factor γdu was proposed, somehow, deformation capacity of shaer wall is determined by multiplying this factor in the computed deformation. Accordingly, the in-plane behavior of 48 different specimens of masonry walls with four lateral constraint configurations (contribution of transverse walls and also top slab), four distinct aspect ratios(height to length) of 0.5, 0.75, 1.0 and 1.5, three traditional wall thicknesses of 0.20, 0.35 and 0.50 m, under pre-compression load of 0.10 MPa were computed using nonlinear pushover analyses. Then, the obtained force-deformation curves were idealized by bilinear curves (linear elastic – perfectly plastic) to make them easier for comparison objectives as well as to be more adopted in practical purposes. The latter results indicated that deformation capacity of the shear walls decreases by stiffer lateral constraints, more thickening; and decrease in height-to-length aspect ratio. In addition, it was observed that the transverse walls (vertical constraints on two sides, and at two ends of the base shear walls) were more efficient in reducing deformation capacity than the top slab (horizontal constraint).  As a result, according to the numerical calculations, the ultimate drift value for the Persian historic brick masonry walls determined between 1.3% and 2.7%. Eventually, the partial factor of γdu to consider uncertainty in modulus of elasticity and thickness assessment in deformation capacity of the Persian historic masonry shear walls achieved in the range of 1.3 to 1.7.

Today, the use of additives to enhance the load-bearing capacity of the soil has been applied to some rural roads in a number of provinces in Viet Nam, proving the outstanding advantages. This paper presents the results of research work and techno-application to rural road construction with on-spot fine basalt soil combined with adhesives including cement PCB40 and additive DZ33. The experimental results show that additive DZ33 has made it possible to increase the hydration ability with cement when effectively reacting with soil particles to reduce construction costs and environmental harms, improve the intensity of reinforced soil mix such as Elastic modulus of reinforced soil (Es), splitting tensile strength (f’st), compressive strength (f’s) and California Bearing Ratio (CBR). Successful application of additive DZ33 combined with cement in soil reinforcement to build rural transport works not only brings about economic efficiency but also, makes use of in-situ fine basalt soil to create a new material for the construction industry, humbly contributing to the study of rural road development in Gia Lai mountainous province in particular and Vietnamese transport infrastructures in general.

For civil engineers, the determination of ring footing bearing capacity subject to the combination of inclined and eccentric loading is a great topic of interest. In this paper, a novel approach is proposed to predict the behavior of ring footing subject to combinations of inclined and eccentric loading using the failure envelope approach, which can explain shallow footings behavior. Load eccentricity, inclination angle, and diameter ratio for ring footings are the most effective parameters on the failure envelope. In this regard, a series of experiments were conducted to investigate the behavior of ring footing subject to a possible form of eccentrically inclined loading. Three values of diameter ratio were considered, including n = 0.2, 0.4, and 0.6, along with a circular footing (n = 0). According to the test results, the conical 3D failure envelope and its equation were developed for each type of footing model. With constant vertical load the failure envelopes show that when the eccentricity is increased, the possible inclination angle is decreased. Also, by increasing the vertical loading, the possible eccentricity and inclination angle is decreased. Based on the observations, and obtained failure envelopes for different diameter ratios, when a ring footing is subject to combined eccentric inclined loading, n=0.4 is optimum. In the following, by using the failure envelope, the concepts of critical eccentricity and critical inclination angle were defined in a way that is suitable for studying the stability of ring footings in the V-H-M/B space.

Limited resources and budget are the most important problem facing the road management sector; therefore, apportionment of maintenance and rehabilitation (M&R) requirements and priorities at the right time and logical are the most significant factors.  Roadway will request continuous (M&R) works to avoid deterioration result from repetitive vehicle weight as well as other factors such as environmental factors. Whether, with the allocation budget that was allocated for roadway maintenance work; there is a necessity to efficiently used the obtainable funding. To execute this, a systematic approach for planning M-and-R process to reach optimum the benefits from roadway segment and minimize necessary funding and costs to repeat the pavement into first state. This process defined as the pavement maintenance management system (PMMS); thus, approach would enable agency to allotted funds, labors, equipment and other resources, most efficiently. This paper demonstrates the applying process of the maintenance program according to the genetic algorithm optimization. The aim of it was to obtain the optimal maintenance strategy alternative percent to reach best values for service life extended as well as increasing the pavement condition index (PCI) along with a specific budget that is not sufficient to restore the whole pavement to its previous state. After applying this program, it was found that it gives the road an additional service life (1.2 years), and at the same time it gives an increase in PCI value (3.8%), taking into consideration the limited resources allocated for maintenance.

Building envelopes and regional conditions can significantly contribute to the cost and energy performance of the buildings. Structured methods that take into account the impacts of both the envelope materials and the regional conditions to find the most feasible envelope materials within a region, however, are still missing. This study responds to this need by proposing a novel method using the capabilities of Building Information Modeling (BIM). The proposed method is used for identifying cost- and energy-efficient building envelope materials within a region over the life cycle. First, commonly used envelope materials in a region are identified. Then, BIM is employed for simulating the energy performance and evaluating the life cycle cost of the materials. The method was implemented in Tehran, Iran. It was successfully utilized for improving the cost and energy performance of a nine-story residential building case. The achieved results indicated a potential energy performance enhancement of 31%, and the life cycle cost improvement of 28% by replacing conventionally used envelope materials with the available high-performance building materials. The proposed method can benefit various stakeholders in the building construction industry, including municipalities, owners, contractors, and consumers, by enhancing the cost and energy performance of the buildings.

Today’s semi-autonomous vehicles are gradually moving towards full autonomy. This transition requires developing effective control algorithms for handing complex autonomous tasks. Driving as a group of vehicles, referred to as a convoy, on automated highways is a highly important and challenging task that autonomous driving systems must deal with. This paper considers the control problem of a vehicle convoy modeled with linear dynamics. The convoy formation requirement is presented in terms of a quadratic performance index to minimize. The convoy formation control is formulated as a receding horizon linear-quadratic (LQ) optimal control problem. The receding horizon control law is innovatively defined via the solution to the algebraic Riccati equation. The solution matrix and therefore the receding horizon control law are obtained in the closed-form. A control architecture consisting of four algorithms is proposed to handle formation size/shape switching. The closed-form control law is at the core of these algorithms. Simulation results are provided to justify the models, solutions, and proposed algorithms.

In this paper, for energy harvesting applications a design of a wideband planar monopole antenna is proposed using fractal geometry and inserting a thin slot on radiation patch. The proposed antenna is optimized at various stages during its design. Moreover, a parametric study is done for understanding sensitivity of important design parameters in the design process of the antenna. The designed antenna operates at 10-dB impedance bandwidth from 0.92 to 2.58 GHz (fractional bandwidth of 95%) with low profile structure and compact size of 65 × 65 × 0.8 mm3, acceptable return losses, stable radiation characterizes and reasonable gains. A potential application of the antenna as a receiving antenna for harvesting systems has been carried out inside the laboratory as the harvested power from ambient radio-frequency (RF) energy. This wideband antenna has a good potential to harvest RF energy of available signals in the GSM-900, GSM-1800, and Wi-Fi bands, and can be used as a part in DC power supply modules of low power sensor networks.

IRNSS is a regional system designed to procure, an accurate user position in all circumstances with 24/7 coverage. This system is used in a wide range of applications with accuracy better than 20 meters in the primary service area. The IRNSS provided position, velocity, and timing services are useful for the Indian users and also the users 1500km from the Indian frontier. The accurate positioning in the phase measurement technique depends on the resolution of ambiguities. In this paper, the main focus is on the effective resolution of ambiguities and thereby position estimation. This paper proposes a Carrier Phase (CP) differencing based Wide Lane (WL) measurement. To resolve the ambiguities, estimate the position of the WL classified methods, Single Frequency Single Difference (SFSD), Single Frequency Double Difference (SFDD), Dual Frequency Single Difference (DFSD), and Dual Frequency Double Difference (DFDD) are used. These four types are processed through the Reference Base and Reference Satellite (RBARS) algorithm to estimate the position of the user/rover. In this paper, direct amalgamate of three estimations are utilized: WL, Narrow Lane (NL), and Ionosphere Free (IF) carrier phase estimations. Using this combination, the estimations of ambiguities are determined for individual satellites by utilizing WL and NL techniques. Thereby the user/rover position is computed, by assessing these real number ambiguities. In this work, every single condition is utilized and together with the least-squares modifications, the positional errors are computed in 3D plane. The computed root mean square errors are compared for all classified methods.

Neuron emulator circuits can be used for teaching and proving concepts.  Such emulators should be made with cheap and off-the-shelf components.  There are bipolar and MOSFET transistor-based neuron emulator circuits heavily used in literature. Opamp-based neuron emulators are also commonly used. Such circuits provide simple and cheap solution instead of using microcontroller-based neuron emulators if many neurons are to be used in the studies such as showing circadian resonance. Unipolar junction transistor (UJT) is commonly used in industrial electronics applications. It provides a cheap timing circuit. Although there are a few UJT-based artificial neuron patents, we were unable to find research articles on UJT-based artificial neurons. In this study, we examined a simple network of UJT-based artificial neurons and show their spiking and bursting behavior with synaptic connections between neurons. It is shown that the firing rate of a UJT-neuron can be increased by utilizing spikes generated by another one with simulations. This behavior represents excitatory connectivity between two neurons.

This paper introduces a meter of the root-mean-square value of deterministic and stochastic signals of an arbitrary shape that are generated over the set time interval. Such a meter involves only the minimum number of simple arithmetic operations to obtain results, and it ensures a high degree of measurement accuracy. For this purpose, the direct calculation of the signal root-mean-square value is applied while the measurement of the half-period average straightened signal value is carried out by means of the traditional measurement devices. Implementation of this meter requires neither the knowledge of what the signal period is, nor the synchronization with the processed sampling. Simulation is then carried out demonstrating the high efficiency of the proposed measurement algorithm. We analyze the characteristics of the meter operating within a wide frequency range of the measurable signals. The recommendations concerning the hardware implementation of such a meter by means of the field programmable gate arrays are considered. The meter can be used when designing digital high-frequency AC voltmeters and ammeters and it can provide the readings that do not depend upon the signal waveform.

In this work, a comparison of six neutron beams was carried out using the MCNPX Monte Carlo code for thermal neutron radiography purposes. The necessary neutrons produced via the 7Li(p,n) reaction for 1 mA proton beam with energies 2.3, 2.5, 3, 4, 4.5, and 5 MeV. The design of the facility was governed from the purpose to achieve the maximum thermal neutron flux in the position of the investigated object. An extensive number of simulations were realized for every source under different conditions. The higher energy of proton beam provides higher intensity for the neutron source but at the same time, the produced spectrum shifted to the fast neutron area. Protons with energies from 2.3 to 3 MeV are more suitable when the thermal neutron content is the main issue of the facility design. Neutrons produced by proton beam in the energy range of 4–5 MeV provide higher thermal neutron fluxes at the cost of the thermal neutron content. The final choice is a compromise, between the thermal neutron content that can be tolerated, in combination with a workable thermal neutron flux.

In this research article, an Ultra-low-power 1-bit SRAM cell is introduced using Tunneling Field Effect Transistor (TFET). This paper investigates feasible 6T SRAM configurations on improved N-type and P-type TFETs integrated on both InAs (Homojunction) and GaSb-InAs (Heterojunction) platforms. The voltage transfer characteristics and basic parameters of both Homo and Heterojunctions are examined and compared. The proposed TFET based SRAM enhances the stability in the hold, read, and write operations. This work evaluates the potential of TFET which can replace MOSFET due to the improved performance with low-power consumption, high speed, low sub-threshold slope, and supply voltage (VDD = 0.2 V). The results are correlated with CMOS 32nm technology. The proposed SRAM TFET cell is implemented using 30nm technology and simulated using an H-SPICE simulator with the help of Verilog-A models. The proposed SRAM TFET cell architecture achieves low power dissipation and attains high performance as compared to the CMOS and FINFET.

This paper proposes a new variable frequency zero voltage switching (ZVS) control method for boost converter operating in boundary conduction mode (BCM). The intended method keeps the converter in BCM despide of the  load and input voltage variations. This is done by changing switching frequency in a certain specified range. The proposed method can guarantee circuit performance in BCM via zero-crossing detection of the inductor current and changing the switching frequency. In addition, with a slight modification in control structure, it is possible to achieve a fully ZVS in all cases. This converter control is carried out in analog form without using microprocessors which, compared with the digital one, has less noise, cost and processing challenges in high frequency applications. Simulation results obtained from applying the proposed method on a GaN-based synchronous boost converter in two different switching frequency ranges (100KHz and 1MHz) are indicative of the proposed strategy advantages.

Nowadays, the relative importance of logistics and sustainable supply chain cannot be denied and third-party logistics as one of the logistics management strategies can play an important role for many industry owners to consider their sustainability goals. The goal of this paper is to choose the best third-party logistics provider to achieve a sustainable logistics system, because third-party logistics service is mainly dependent on both transportation and workforces, managing them is one of the important issues of sustainability. Thus, third-party logistics providers need to be concerned about not only the economic criteria but also issues related to environmental and social sustainability in addition to two other dimensions namely technical and reputation. In this paper, a comprehensive classification of related criteria, sub-criteria, and sub-sub-criteria is proposed according to selecting the best third-party logistics provider. To evaluate and rank the proposed criteria, a D Number-Analytic Hierarchy Process method, as one of the proper and popular multi-criteria decision-making (MCDM) approaches, is utilized. Besides, a case study in dairy industry has been accomplished in the real-world to show the effectiveness and a better understanding of the proposed conceptual model. Finally, the best third-party logistics provider was identified among the alternatives for the proposed case study. The results showed that the proposed method could be a good alternative to conduct evaluations and the related sensitivity analysis, considering sustainability.

Press automotive components manufacturer in Indonesia with the Overall Equipment Effectiveness (OEE) achievement was 60.7%, it was below than world company level as mention in Japanese Institute of Plant Maintenance (JIPM)- Total Productive Maintenance (TPM) standard, the biggest OEE components problem was availability value that achieved 63.3%, then the biggest availability problem was dies preparation time. Using TPM method and Plan-Do-Check-Act (PDCA) cycle with Pareto chart and Fishbone diagram, we succeeded to improve dies setting time within one months. We could increase the availability value from 63.3% became 67.8% and finally OEE value increased from 60.7% became 65.3%.

In recent years, regarding the issues such as lack of natural resources, government laws, environmental concerns and social responsibility reverse and closed-loop supply chains has been in the center of attention of researchers and decision-makers. Then, in this paper, a multi-objective multi-product multi-period mathematical model is presented in the sustainable closed-loop supply chain to locate distribution, collection, recycling, and disposal centers, considering the risk criterion. Conditional value at risk is used as the criterion of risk evaluation. The objectives of this research are to minimize the costs of the chain, reducing the adverse environmental effects and social responsibility in order to maximize job opportunities. Uncertainty in demand and demand-dependent parameters are modeled and determined by the fuzzy inference system. The proposed model has been solved using multi objective particle swarm optimization algorithm (MOPSO) approach and the results have been compared with Epsilon constraint method. Sensitivity analysis was performed on the problem parameters and the efficiency of the studied methods was investigated.

The researches on environmental and sustainability are an active topic, especially in the waste management. As such, the hazardous waste optimization is an active research topic in developing countries which may be integrated with carbon emissions and green subjects. This grand challenge motivates the current research to contribute a new multi-objective optimization model to address the green hazardous waste location-routing problem. The proposed multi-objective optimization model establishes four objectives simultaneously for the first time. In addition to the total cost and the greenhouse gas emissions of the transportation systems as the two main objectives, another objective function aims to minimize the risk of transportation of the hazardous waste alongside the waste residue associated with the people’s exposure around transportation paths. Furthermore, the total risk linked with the population in a certain radius around the treatment and disposal centers is minimized. As the proposed model is complex with conflicting objectives, several multi-objective decision making (MODM) tools are employed and compared with each other based on different test problems associated with an industrial example. Based on the solution quality and the computational time, the technique for the order of preference by similarity to the ideal solution (TOPSIS) is selected as the strongest technique to assess the performances of all five MODM methodologies.

In this paper, we studied a new integrated production scheduling, vehicle routing, inventory and outsourcing problem. The production phase considers parallel machine scheduling including setup times with outsourcing allowed and the distribution phase considered batch delivery by a fleet of homogenous vehicles with respect to holding cost of completed jobs. The objective of the Mixed Integer Linear Programming (MILP) formulated model is to minimize the total costs including production, outsourcing, holding, tardiness and distribution fixed and variable costs. Due to the nondeterministic polynomial time (Np)-hardness of the problem, we derive a number of dominance properties for the optimal solution and combine them with a Genetic Algorithm (GA) to solve the problem. To assess the efficiency and effectiveness of the proposed hybrid algorithm, we conduct the computational study on randomly generated instances. Sensitivity analyses showed the impacts of the parameters on the objective function were incorporated. In order to evaluate the significance of the differences among the results obtained by GA and GADP one-tailed paired t tests were performed and interval plots were depicted.

In a powerful industry, supplier selection is one of the complex processes that can increase productivity and competitive advantages. Supplier selection includes different quantitative, qualitative, and also interactive criteria. In addition, the selection process has always faced with inadequate and incomplete data. Multi-criteria decision making (MCDM) is a useful approach that can be applied, for addressing the opting problems of a supplierconsidering mentioned issues. In this approach, the interaction between criteria can be considered with several methods, such as Choquet integral, which is a practical method for decision ranking. Also, incomplete data can be covered with incomplete analytic hierarchy process (AHP) method. Therefore, in this study, an application of Choquet integral along with incomplete AHP method is provided for supplier selection problem at the petroleum industry. After achieving the ranking rate of suppliers, requested orders are assigned to preferred suppliers by using multi-objective linear programming(MOLP) model and ɛ-constraint method to generate the Pareto optimal points. As a result, supplier 3 with weight 0.8274 was the most preferred supplier in which 50% of total orders was assigned to this supplier as the best selection.

Smart manufacturing systems are triggerring the next industrial revolution.They are intended to be collaborative manufacturing systems that respond in real time to meet the system’s changing demands and conditions. Different types of dependencies among system components are introduced to enable this and to improve system performance, including structural, stochastic, technical and economic dependences. Supporting systems are also introduced to this aim, through specified interfaces. In this paper, the role of maintenance policy, spare part inventory and buffer size as supporting systems of smart systems is considered. Load-sharing dependence, adaptive control with feedback and economic dependence are specifically considered, and their effect is studied via Monte Carlo simulation. Results show that smart systems with properly designed supporting systems have undoubtedly increased system complexity and dependencies, but can indeed increase availability and production volume, and system efficiency overall, with total cost reduced.

In the rubber industry, the process of designing rubber compound is of great importance due to the impact on product specifications. The good performance of this process is a competitive advantage for manufacturers in this industry. The process of designing a rubber compound includes a set of activities related to selecting the best amount of raw materials to prepare a composition with the desired physical and mechanical properties. Currently, the most common method for designing a rubber compound is the experimental method based on trial and errors. This method is time consuming and expensive. In addition, the obtained combination is not necessarily the best combination. To improve the performance of the rubber compound we need to design the desired process, this research presented using a combination of artificial neural network and genetic algorithm, with an approach to reduce time and cost, while increasing accuracy. In this method, the behavior of the rubber compound was modeled with artificial neural network. Then, using Genetic Algorithm as a quick search technique.The optimal values of the four raw materials such as carbon, sulfur, oil and accelerator; in order to determine the specified value of the two characteristics .abrasion and rubber modulus at 300% elasticity at the lowest price. To evaluate the method, several samples of rubber compound designed with two method. The results showed that the artificial neural network model has the ability to predict the two characteristics of abrasion and modulus based on the four mentioned raw materials in the trained range with high accuracy. In addition, average results for genetic algorithm, is a price of 17% less and a design accuracy of 84.5% more than experimental method. The design speed with this method is 454 times higher than the experimental design speed. Based on the results, by designing the rubber compound with the integration of artificial intelligence and genetic algorithms has a better performance than the experimental method.

This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 ᵒC for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by R2 = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

The effect of water absorption and swelling thickness on the tensile properties of flax/sisal/carbon/glass fabrics reinforced by unsaturated polyester-based hybrid composites was evaluated. Hybrid composites reinforced with different natural and synthetic fiber configurations have been processed using the Hand lay-up (HLU) technique. Water absorption test was carried out by immersing specimens in distilled water for different lengths of time, up to 1344 hours, and showed slight enhancement of water absorption for fibers configuration [C/C/C/C/C/C] up to 1.45%, but great enhancement of water absorption for fiber configuration [S/S/S/S/S/S] up to 14%. The tensile strength of samples before and after immersion in distilled water was evaluated. This result showed that the tensile strength of fibers configuration [C/C/F/F/C/C] is approaching fibers configuration [C/C/C/C/C/C] at dry condition. Fractured cross-section morphology of composites was investigated using scanning electron microscopy (SEM) to evaluate the fibers/matrix interface before and after hydro aging. The reduction in tensile strength due to water penetrating is slight for hybrid composite with synthetic fibers at the outer layer, but huge for hybrid composite with natural fibers at outer layer.

Hippotherapy as a treatment modality relies on patient-equine dynamic interaction to enhance physical abilities in a range of neuromuscular diseases. The modality takes advantage of external stimulations in the form of kinetic and kinematic inputs to patient upper body. Current practices and procedures could be greatly enhanced by an objective approach to session planning based on a predictive neuromuscular model. Individualization of the treatment program is both subject-specific and equine-specific.To this effect, kinesiological aspects of the three main upper body flexor-extensor muscles which are directly affected by this treatment modality are presented in a biomechanical model. Events and phases of this dynamic interaction are identified and described using a phase plane analysis. Physical interpretations of coefficients in the movement differential equation illustrates that the proposed approach and mathematical modeling have the potential to be tailored for various musculoskeletal or neuromuscular disorders. Validation results show that the model has the ability to simulate kinematic response and muscle forces of the patient upper body during a hippotherapy session. This predictive ability could provide the therapist with a tool to estimate the effects prior to therapy sessions and choose the most suitable combination of horse and exercises.

The ambient conditions have a significant effect on the generated power and efficiency of gas turbines [1]. These variations considerably affect power generation, fuel consumption, power plant emissions, and plant incomes. However, cooling the compressor inlet air has been widely used to reduce this deficiency [2]. In this paper, by simulating a specific gas unit in Thermoflow software, the effect of the FOG system on it was studied. Considering the error in determining the capacity of cooling systems based on the average values of dry and wet bulb temperatures, or even considering the worst possible temperature and humidity conditions, it is advisable to use ECDH or Evaporation cooling Degree Hours. Accordingly, by calculating ECDH under at ambient temperatures above 15 °C and changing the conditions of the model, the total production increase of the unit was estimated to be 4.7×106 kWh. In addition, the effect of relative humidity on payback time was examined, which illustrated the critical relative humidity for a gas unit would depend on the price of fuel, the purchase price of electricity, the design parameters of the unit and the expected payback time. For this gas unit, critical relative humidity was monitored based on expected payback time and electricity purchase price. Results showed that for a certain electricity price, at the shorter PBT, the critical RH is lower; therefore, the temperature drop and power enhancement will be greater. In addition, at a certain PBT, as the electricity price increases, the critical RH for the same PBT will be higher.

Solid fuel from the briquetting of ulin wood and gelam wood residue was investigated in this work. The effect of compaction pressure (10, 12, and 15 MPa), and briquette formulation were investigated. The ulin wood and gelam wood were blended in the mixing ratios of 100:0, 70:30, 50:50, 30:70, and 0:100, respectively. The size of the particle was fixed of 50 µm. The ulin wood and gelam wood were carbonized under fixed temperature (500oC), and time (120 min). The gelatinized binder (cassava starch) was 20% of the total briquettes weight. The densification was carried out using the briquetting machine (piston-press type) laboratory scale. The compaction pressure briquette had a significant effect on some characteristics of briquette (ash content, moisture content, volatile matter, bulk density, and combustion rate). An increasing in compaction pressure briquettes resulted in low ash content, moisture content, and volatile matter but the reverse is the case for bulk density. However, the mixing ratio slightly affected. High combustion rate (3.18 g/min) achieved at low compaction pressure (10 MPa).

Lyapunov's direct method is a primary tool for designing Model Reference Adaptive Control (MRAC) and robust MRAC schemes. In general, Lyapunov function candidates contain two categories of quadratic terms. The first category includes the system tracking error quadratic terms or, in some cases, consist of the system state quadratic terms. The second consists of the parameter estimation error quadratic terms. To design MRAC and Robust MRAC systems, researchers have used a limited variety for choosing quadratic terms. In this study, we consider a general form for the tracking error quadratic terms. We consider a strictly increasing function that belongs to the class of c1, which is a function of state tracking error quadratic terms. It yields a general structure for stable adaptive laws for updating controller parameters. For the MRAC scheme, the global asymptotic stability of the closed-loop system and stability and uniform bounded tracking of robust MRAC schemes are guaranteed. To evaluate the performance of the designed controllers, we consider the single DOF wing rock dynamics.

Resistance spot welding is the primary welding process used in automotive body panel assembly. However, plug welding is widely used in automotive body repair due to its technical simplicity and cost benefits. In this paper, spot welding and plug welding using Tungsten Inert Gas (TIG) welding of an automotive body panel are compared. TIG welding is selected for plug welding because it offers the greatest flexibility to weld the widest range of materials, thicknesses, and types. The base material used in this study is JIS G3141 SPCC. Full factorial experimental design coupled with statistical and graphical analysis of the results using analysis of variance was applied to determine the significance of process parameters. Parameter interactions were investigated using regression analysis, model adequacy checks, and determination of optimum conditions. A genetic algorithm is used to predict the optimum combination of the process parameters to realize the highest strength level. For tensile-shear strength, the experimental results demonstrate that plug welding has a higher maximum load than spot welding. The optimum plug welding joints were obtained at a hole diameter of 9 mm and a welding current of 136 kA, with a maximum load of 8.2 kN. The maximum load of the spot weld joint, 7.4 kN, was found at a welding current of 70 kA, an electrode force of 0.25 MPa, and 10 cycles of welding time.

Two-Point Incremental Forming (TPIF) method is a novel technique for producing free form shell parts. The main purpose of this study is to analyze the TPIF process, and, by approximate calculation, to find the force applied to the tool. One of the limitations of an incremental forming process is that during this process force applied to the tool is born by the machine. In this research, an equation for approximate prediction of the force applied to the tool is presented using the values of the yield stress of the sheet, friction coefficient, tool radius and thickness of the sheet; hence, the applied force can be calculated. By increasing the forming angle, the amount of the created local strain increases and the change in thickness and the force applied to the tool is enhanced. However, by increasing the angle of punch wall, less compressive stress is applied to the metal sheet due to the reduction in contact between the surface of the tool and punch wall. Analytical equations presented are validated by the results from experimental tests.

Using scaffold microstructure for bone tissue graft has been widely considered. Among the several properties of a scaffold, permeability plays a prominent role in the transport of nutrients, oxygen, and minerals. It is a key parameter which comprises various geometrical features such as pore shape, pore size and interconnectivity, porosity, and specific surface area. The main aim of this research is to characterize the permeability of the scaffold microstructure in terms of different pore sizes and porosity. To this end, cylindrical geometries for pores were modeled and the permeability coefficient was calculated using velocity and pressure drop and employing Darcy’s law. The validation process of the numerical results was done by comparing with experimental data. In this regard, a simple experiment setup was presented based on the constant head method. Additionally, the scaffolds were built using Solid Freeform Fabrication (SFF) techniques. The results showed that increasing porosity leads to an increase in permeability. Moreover, the permeability increases as the pore size increases. Eventually, the reducing pore diameters have a significant effect on the flow and hence permeability (e.g., a 20% decrease in diameter yields a 76% decrease in permeability).

This study proposed a novel method for system failure reasoning based on Bayesian networks to solve emergency airflow control system reliability problems. A system fault tree model was established to identify the logical relationship between the units, which was then transformed into a Bayesian network fault analysis model to determine network node states and the conditional probability table, as well as to carry out diagnostic reasoning on the system node branches. The reliability analysis of the model based on Netica Bayesian tool shows that the probability of system failure caused by substation communication node is the highest under normal conditions, and data monitoring and central station communication nodes have a greater impact on intelligent control. By predicting and diagnosing system faults, the optimization of system design is realized on the framework of Bayesian network to improve the reliability, and there by establishing a theoretical foundation for future disaster prevention research.

The single polycrystalline diamond compact (PDC) cutter’s performance is affected by temperature during rock cutting process. The study towards understanding the factors and its interaction affecting the cutter’s temperature is essential prior to cutting process optimization. Thus, this study aims to investigate the effect of various cutting parameters and its interaction on the temperature of a single PDC cutter. A series of test was conducted in a lathe machine which utilized facing operation to cut the rock samples at 0.5 to 1.5 mm depth of cut and back rake angle of 5° to 15°. Two types of rock being tested in this study were Indiana limestone and Carthage marble. The analysis of variance (ANOVA) output indicated that cutting parameters and rock properties and its interaction have a significant effect on cutter’s temperature except for interaction between back rake angle and rock properties.  Increasing the depth of cut and decreasing back rake angle has resulted in increasing temperature. The temperature of the single PDC cutter is higher when cutting Carthage marble than Indiana limestone. Combination of low back rake angle and high depth of cut producing maximum temperature.  It is also validated that the data developed from the mathematical model having a difference of 5% as compared to the experimental data obtained using similar parameters which indicates that the results are reliable and can be forwarded in the future study.