Search results

The mixing of fuel and air plays a pivotal role in enhancing combustion in supersonic regime. Proper mixing stabilizes the flame and prevents blow-off. Blow-off is due to the shorter residence time of fuel and air in the combustor, as the flow is in supersonic regime. The flame is initiated in the local subsonic region created using a flameholder within the supersonic combustor. This study aims to design an effective flameholder which increases the residence time of fuel in the combustor allowing proper combustion preventing blow-off and other instabilities.

The geometry of the strut-based flameholder is altered in the present study to induce a streamwise motion of the fluid downstream of the strut. The streamwise motion of the fluid is initiated by the ramps and grooves of the strut geometry. The numerical simulations were carried out using ANSYS Fluent and are validated against the available experimental and numerical results of cold flow with hydrogen injection using plain strut as the flameholder. In the present study, numerical investigations are performed to analyse the effect on hydrogen injection in strut-based flameholders with ramps and converging grooves using Reynolds-averaged Navier–Stokes equation coupled with Menter’s shear stress transport k-ω turbulence model. The analysis is done to determine the effect of geometrical parameters and flow parameter on the flow structures near the base of the strut where thorough mixing takes place. The geometrical parameters under consideration include the ramp length, groove convergence angle, depth of the groove, groove compression angle and the Mach number. Two different strut configurations, namely, symmetric and asymmetric struts were also studied.

Higher turbulence and complex flow structures are visible in asymmetric strut configuration which develops better mixing of hydrogen and air compared to symmetric strut configuration. The variation in the geometric parameters develop changes in the fluid motion downstream of the strut. The fluid passing through the converging grooves gets decelerated thereby reducing the Mach number by 20% near the base of the strut compared to the straight grooved strut. The shorter ramps are found to be more effective, as the pressure variation in lateral direction is carried along the strut walls downstream of the strut increasing the streamwise motion of the fluid. The decrease in the depth of the groove increases the recirculation zone downstream of the strut. Moreover, the increase in the groove compression angle also increases the turbulence near the base of the strut where the fuel is injected. Variation in the injection port location increases the mixing performance of the combustor by 25%. The turbulence of the fuel jet stream is considerably changed by the increase in the injection velocity. However, the change in the flow field properties within the flow domain is marginal. The increase in fuel mass flow rate brings about considerable change in the flow field inducing stronger shock structures.

The present study identifies the optimum geometry of the strut-based flameholder with ramps and converging grooves. The reaction flow modelling may be performed on the strut geometry incorporating the design features obtained in the present study.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

The purpose of this study is to advance turbulent thermal convection inside the constant heat-flux round tube inserted by multiple perforated twisted tapes.

The novel design of this study is accomplished by inserting several twisted tapes and drilling some circular perforations near the tape edge (C1, C3, C5: solid tapes; C2, C4, C6: perforated tapes). The turbulence flow appearances and thermal convective features are examined for various Reynolds numbers (8,000–14,000) using the renormalization group (RNG) κ−ε turbulent model and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm.

The simulated outcomes reveal that inserting more perforated-twisted tapes into the heated round tube promotes turbulent thermal convection effectively. A swirling flow caused by the twisted tapes to produce the secondary flow jets between two reverse-spin tapes can combine with the main flow passing through the perforations at the outer edge to enhance the vortex flow. The primary factors are the quantity of twisted tapes and with/without perforations, as the perforation ratio remains at 2.5 in this numerical work. Weighing friction along the tube, C6 (four reverse-spin perforated-twisted tapes) brings the uppermost thermal-hydraulic performance of 1.23 under Re = 8,000.

The constant thermo-hydraulic attributes of liquid water and the steady Newtonian fluid are research limitations for this simulated work.

The simulated outcomes will avail the inner-pipe design of a heat exchanger inserted by multiple perforated twisted tapes to enhance superior heat transfer.

These twisted tapes form tiny circular perforations along the tape edge to introduce the fluid flow through these bores and combine with the secondary flow induced between two reverse-spin tapes. This scheme enhances the swirling flow, turbulence intensity and fluid mixing to advance thermal convection since larger perforations cannot produce large jet velocity or the position of perforations is too far from the tape edge to generate a separated flow. Consequently, this work contributes a valuable cooling mechanism toward thermal engineering.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

Although it is a well-known notion that “a family firm does not survive beyond the third generation”, owing to the ineffective tacit knowledge transfer, studies investigating the relationship between generational evolution and knowledge innovation is scarce. Thus, this case study revolving Sin Kwang Plastic Resources Berhad (SKP) seeks to address this gap in literature.

To assess the development of family business, a longitudinal case study was performed by documenting the entire evolutionary process starting from its establishment until now. The historical profiles for SKP were obtained from the previous annual reports submitted to the government's Companies Commission (SSM). Secondary materials from the Federation of Malaysian Manufacturers (FMM) and the edge newspaper and articles on the company were also gathered. Throughout the in-depth interviews, the author can see how the next generation of this family firm innovates and implements tacit knowledge innovation in original equipment manufacturing (OEM) by adhering to the Japanese industrial standards.

Findings show that the second generation utilized the company's extensive knowledge in plastic contract manufacturing in SKP for tacit knowledge conversion, triggers the birth of STS Tecnic Berhad, a subsidiary company that manufactures plastic parts for the industrial packaging and automotive industry. To simplify the process of managing the complex business, SKP opted to “prune the family tree” by dividing the business, involving fewer managers and restricting the number of family shareholders.

This case study traces how Gan's family's tacit knowledge in plastic contract manufacturing have been acquired from the experience of contract manufacturing with the Japanese multinational corporations (MNCs) by further commercializing the tacit knowledge into different companies for different plants. SKP promotes tacit knowledge innovation in the learning organization, thus responding to the firm's sustainability.

This study demonstrates that knowledge transformation plays vital roles in product development and gaining competitive advantage. The success of this business is founded by the building of shared values, norms and technical understanding in plastic contract manufacturing among the Japanese MNCs in Malaysia.

Nature and occurrence of food-borne pathogens in raw and processed food products evolved greatly in the past few years due to new modes of transmission and resistance build-up against sundry micro-/macro-environmental conditions. Assurance of food health and safety thus gained immense importance, for which bio-sensing technology proved very promising in the detection and quantification of food-borne pathogens. Considering the importance, different studies have been performed, and different biosensors have been developed. This study aims to summarize the different biosensors used for the deduction of food-borne pathogens.

The present review highlights different biosensors developed apropos to food matrices, factors governing their selection, their potential and applicability. The paper discusses some related key challenges and constraints and also focuses on the needs and future research prospects in this field.

The shift in consumers’ and industries’ perceptions directed the further approach to achieve portable, user and environmental friendly biosensing techniques. Despite of these developments, it was still observed that the comparison among the different biosensors and their categories proved tedious on a single platform; since the food matrices tested, pathogen detected or diagnosed, time of detection, etc., varied greatly and very few products have been commercially launched. Conclusively, a challenge lies in front of food scientists and researchers to maintain pace and develop techniques for efficiently catering to the needs of the food industry.

Biosensors deduction limit varied with the food matrix, type of organism, material of biosensors’ surface, etc. The food matrix itself consists of complex substances, and various types of food are available in nature. Considering the diversity of food there is a need to develop a universal biosensor that can be used for all the food matrices for a pathogen. Further research is needed to develop a pathogen-specific biosensor that can be used for all the food products that may have accuracy to eliminate the traditional method of deduction.

The present paper summarized and categorized the different types of biosensors developed for food-borne pathogens.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

This study aims to investigate the morphing concepts able to manipulate the dynamics of the downstream unsteadiness in the separated shear layers and, in the wake, be able to modify the upstream shock–boundary layer interaction (SBLI) around an A320 morphing prototype to control these instabilities, with emphasis to the attenuation or even suppression of the transonic buffet. The modification of the aerodynamic performances according to a large parametric study carried out at Reynolds number of 4.5 × 10⁶, Mach number of 0.78 and various angles of attack in the range of (0, 2.4)° according to two morphing concepts (travelling waves and trailing edge vibration) are discussed, and the final benefits in aerodynamic performance increase are evaluated.

This article examines through high fidelity (Hi-Fi) numerical simulation the effects of the trailing edge (TE) actuation and of travelling waves along a specific area of the suction side starting from practically the most downstream position of the shock wave motion according to the buffet and extending up to nearly the TE. The present paper studies through spectral analysis the coherent structures development in the near wake and the comparison of the aerodynamic forces to the non-actuated case. Thus, the physical mechanisms of the morphing leading to the increase of the lift-to-drag ratio and the drag and noise sources reduction are identified.

This study investigates the influence of shear-layer and near-wake vortices on the SBLI around an A320 aerofoil and attenuation of the related instabilities thanks to novel morphing: travelling waves generated along the suction side and trailing-edge vibration. A drag reduction of 14% and a lift-to-drag increase in the order of 8% are obtained. The morphing has shown a lift increase in the range of (1.8, 2.5)% for angle of attack of 1.8° and 2.4°, where a significant lift increase of 7.7% is obtained for the angle of incidence of 0° with a drag reduction of 3.66% yielding an aerodynamic efficiency of 11.8%.

This paper presents results of morphing A320 aerofoil, with a chord of 70cm and subjected to two actuation kinds, original in the state of the art at M = 0.78 and Re = 4.5 million. These Hi-Fi simulations are rather rare; a majority of existing ones concern smaller dimensions. This study showed for the first time a modified buffet mode, displaying periodic high-lift “plateaus” interspersed by shorter lift-decrease intervals. Through trailing-edge vibration, this pattern is modified towards a sinusoidal-like buffet, with a considerable amplitude decrease. Lock-in of buffet frequency to the actuation is obtained, leading to this amplitude reduction and a drastic aerodynamic performance increase.

In this paper the aim is that Aramid/alginate blended nonwoven fabrics were prepared, and the flame retardancy of the blended nonwoven fabrics was studied by thermogravimetric analysis, vertical flame test, limiting oxygen index (LOI) and cone calorimeter test.

The advantages of different fibers can be combined by blending, and the defects may be remedied. The study investigates whether incorporating alginate fibers into aramid fibers can enhance the flame retardancy and reduce the smoke production of prepared aramid/alginate blended nonwoven fabrics.

Thermogravimetric analysis indicated that alginate fibers could effectively inhibit the combustion performance of aramid fibers at a higher temperature zone, leaving more residual chars for heat isolation. And vertical flame test, LOI and cone calorimeter test testified that the incorporation of alginate fibers improved the flame retardancy and fire behaviors. When the ratio of alginate fibers for aramid/alginate blended nonwoven fabrics reached 80%, the incorporation of alginate fibers could notably decreased peak-heat release rate (54%), total heat release (THR) (29%), peak-smoke production rate (93%) and total smoke production (86%). What is more, the lower smoke production rate and lower THR of the blends vastly reduced the risk of secondary injury in fires.

This study proposes to inhibit the flue gas release of aramid fiber and enhance the flame retardant by mixing with alginate fiber, and proposes that alginate fiber can be used as a biological smoke inhibitor, as well as a flame retardant for aramid fiber.