The Application of Response Surface Methodology in the Investigation of the Tribological Behavior of Palm Cooking Oil Blended in Engine Oil

Sakinah, M. H.; Amirruddin, A. K.; Kadirgama, K.; Ramasamy, D.; Rahman, M. M.; Noor, M. M.

doi:10.1155/2016/6545904

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…The simulating models (Figure 2 (a)) were created on the base of real propellers (DF-1050CR from Draganfly Innovations, airfoil type similar to NACA6409) with diameter of 254 mm and pitch of 127 mm as well as a propeller with diameter of 203.2 mm and pitch of 101.6 mm, scanned with the help of a 3D scanner. The scanned models were imported in SolidWorks Flow Simulation as point cloud data [36]. Then, working models were created and lifting force of propellers was determined at different rotation speeds.…”

Section: Cfd Modellingmentioning

confidence: 99%

Analysis and study of the influence of the geometrical parameters of mini unmanned quad-rotor helicopters to optimise energy saving

Penkov¹,

Александров²

2017

Int. J. Automot. Mech. Eng.

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The article discusses lift force generated by mini UAVs (Unmanned Aerial Vehicles). CFD (Computational Fluid Dynamics) simulations were made on the base of a 3D scanned propeller model. Influence of some geometrical parameters of propeller (like velocity or pitch) and quadcopter (like gap) on lifting force was considered. Different propeller pitches were used and pitch influence on propeller lift force was analysed. Normally, lifting force will increase with the increasing of propeller pitch but for different rotation velocities, this increasing is different and in all cases, it can be approximated by a linear relationship. To obtain dependency functions, an equation for calculation of lift force given took into account the correction coefficients. This equation gave reliable results at pitch values equal to 0.3 -0.7 of the propeller diameter and at rotation velocities of 2000 min -1 -8000 min -1 .Lift force dependency from distance between rotors was also considered. Simulations and experiments showed that the lifting force of a quadcopter increased about 15% on gap distances from 5 mm to 35 mm. From a distance of 70 mm, the lifting force will decrease about 2% and then will stabilise. At increasing of distance between propellers from 5 mm until 25 mm, the power consumption decreased 8% -10% and after the gap distance equal to 40 mm, it will be stable and minimal. It can be asserted that quadcopters have different optimal distances between the propellers at different rotation speeds to generate the same force. Equations for calculation of optimal gap distances for different multicopters were derived and calculation results are presented in graphs and tables.

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Section: Cfd Modellingmentioning

confidence: 99%

Analysis and study of the influence of the geometrical parameters of mini unmanned quad-rotor helicopters to optimise energy saving

Penkov¹,

Александров²

2017

Int. J. Automot. Mech. Eng.

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“…The bending strength shown in Figure 5 is almost the inverse of the density changes (Figure 3) for the sample sintered at 750°C, where the highest strength was obtained by the sample sintered at 900°C for 60 mins, which yielded a strength of 34.55 MPa, and which also shows a lower density change ( Figure 4) and volumetric expansion (Figure 2). Referring to the SEM image (Figure 6), it can be seen that the grains are closely packed together with fewer voids [31,32]. Close-packed grains might produce larger grain boundaries, which would further enhance the strength of the material.…”

Section: Effects Of Sintering Schedule On the Characteristics Of Fe-bmentioning

confidence: 99%

Effects of sintering schedule on the characteristics of Fe-based powder compacts formed through warm compaction route

Rahman¹,

Rosli

Namasivayam

2016

Int. J. Automot. Mech. Eng.

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This paper presents the sintering characteristics of Fe-based powder compacts formed through the warm compaction method. Iron powder ASC100.29 (73 wt%), chromium powder (22 wt%), and aluminium powder (5 wt%) were mixed mechanically for 60 mins in preparing the feedstock. The powder mixture was subsequently compacted at 200ºC through simultaneous loading of 325 MPa from upper and lower punches. The defectfree green compacts were then sintered in an argon gas fired furnace at a rate of 10ºC/min by varying the sintering time (30, 60, and 90 mins) and temperature (600, 750, and 900ºC), respectively. The sintered samples were characterized through physical and mechanical properties and their microstructures were analysed through scanning electron microscopy. The results revealed that sintering at 900ºC for 90 mins contributed to the highest volumetric expansion and relative density change, which are not expected in the powder metallurgy industries. A moderate sintering temperature of 750ºC for a holding time of 60 mins was found to produce a sintered sample with higher bending strength. In these sintering conditions, better dispersion of the alloyed elements is also observed.

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“…The training time is determined by various factors where they are influenced by the complexity of the problem, the number of data, the network and the transfer function parameters used. From the training process, the generalization results from the process where it produces the models that can overfit the data, this shows that the optimum process from the selected parameters and statistical result to select the best model [7][8][9][10][11]. This study applies the Backpropagation Artificial Neural Network (BPANN).…”

Section: Introductionmentioning

confidence: 99%

Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain

Fradinata

Suthummanon

Suntiamorntut

et al. 2019

JMES

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The objective of this study was to compare the Bullwhip Effect (BWE) in the supply chain through two methods and to determine the inventory policy for the uncertainty demand. It would be useful to determine the best forecasting method to predict the certain condition. The two methods are Artificial Neural Network (ANN) and Support Vector Regression (SVR), which would be applied in this study. The data was obtained from the instant noodle dataset where it was in random normal distribution. The forecasting demands signal have Mean Squared Error (MSE) where it is used to measure the bullwhip effect in the supply chain member. The magnification of order among the member of the supply chain would influence the inventory. It is quite important to understand forecasting techniques and the bullwhip effect for the warehouse manager to manage the inventory in the warehouse, especially in probabilistic demand of the customer. This process determines the appropriate inventory policy for the retailer. The result from this study shows that ANN and SVR have the variance of 0.00491 and 0.07703, the MSE was 1.55e-6 and 1.53e-2, and the total BWE was 95.61 and 1237.19 respectively. It concluded that the ANN has a smaller variance than SVR, therefore, the ANN has a better performance than SVR, and the ANN has smaller BWE than SVR. At last, the inventory policy was determined with the continuous review policy for the uncertainty demand in the supply chain member.

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The Application of Response Surface Methodology in the Investigation of the Tribological Behavior of Palm Cooking Oil Blended in Engine Oil

Cited by 16 publications

References 17 publications

Analysis and study of the influence of the geometrical parameters of mini unmanned quad-rotor helicopters to optimise energy saving

Analysis and study of the influence of the geometrical parameters of mini unmanned quad-rotor helicopters to optimise energy saving

Effects of sintering schedule on the characteristics of Fe-based powder compacts formed through warm compaction route

Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain

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