NUST Institutions Library Catalogue Search for 'an:124803'

Development, Characterization and Testing of Nickel Titanium Based High Temperature Shape Memory Alloys /

By Saif ur Rahman,. . 216p. , TiNi-based shape memory alloys are well known for their excellent shape memory and superelastic properties. TiNiPd alloys are considered as the better high temperature shape memory alloys due to high transformation temperatures, small hysteresis, reasonable strain recovery and comparable workability. However, by further increasing the transformation temperatures i.e. by increasing the Pd content, thermal hysteresis also increases. This has an adverse effect on the actuation behavior of the alloy. At high temperature the critical stress for slip deformation of TiNiPd alloys also decreases, which increases the permanent deformation and reduces the strain recovery in the alloy. In order to prevent increase in thermal hysteresis, reduce permanent deformation and increase strain recovery in TiNiPd alloy, Ni has been replaced by 5 at%, 10 at% and 15 at% Cu. Four alloys; Ti50Ni25Pd25, Ti50Ni20Pd25Cu5, Ti50Ni15Pd25Cu10 and Ti50Ni10Pd25Cu15 (all in atomic %) have been developed and characterized for their microstructure, phase transformation temperatures, mechanical and shape memory properties in solution treated condition. By increasing the Cu content, the transformation temperature of the TiNiPdCu alloys significantly increased, whereas thermal hysteresis decreased. Similarly, the microhardness, yield and fracture strength also increased. Shape memory properties like strain recovery and work output also improved. Therefore, TiNiPdCu alloys showed improved transformation temperatures, strain recovery and critical stress for slip deformation through solid solution strengthening mechanism. The TiNiPdCu alloys were also aged at different aging temperatures i.e. 400°C, 500°C, 600°C and 700°C for 3 hours to investigate their transformation temperatures, mechanical and shape memory properties and compared with the solution treated samples. By aging the Ti50Ni25Pd25 and Ti50Ni20Pd25Cu5 alloys, the transformation temperatures, mechanical and shape memory properties slightly increased. After aging the Ti50Ni15Pd25Cu10 and Ti50Ni10Pd25Cu15 alloys, the transformation temperatures and shape memory properties significantly decreased, however the mechanical properties were improved. viii It can be concluded that aging of Ti50Ni25Pd25 and Ti50Ni20Pd25Cu5 alloys is beneficial to increase their transformation temperatures and shape memory properties. However it has an adverse effect in terms of transformation temperatures and strain recovery by aging the Ti50Ni15Pd25Cu10 and Ti50Ni10Pd25Cu15 alloys. 30cm.

Place Hold on Development, Characterization and Testing of Nickel Titanium Based High Temperature Shape Memory Alloys /

Tribological Analysis of Engine Valve Train Performance Considering Effects of Lubricant Formulation /

By Khurram, Muhammad . . 225p. , Fuel economy, reduction in power losses and control on emissions are the key drivers of the automotive industry. Development of new technologies and lubricant formulation is being pursued relentlessly to improve the engine performance. This in turn demands comprehensive experimental work based on reliable and accurate measurement system to analyze the effectiveness of these technologies. In roller follower valve train configuration, the power losses and deterioration of mating surfaces of cam and roller is largely governed by the sliding of rollers and lubrication conditions at cam/roller interface whereas the different operating conditions, lubricant rheology/chemistry can play an extremely important role in this context. In this research project, an advanced real production gasoline engine having end pivoted roller finger follower valve train has been instrumented, for the very first time, by employing the recently developed techniques based on advanced sensor technology to measure the rollers tribological behavior and oil film thickness at cam/roller interface under realistic environment. An elaborated experimental research work comprising of series of tests has been undertaken to investigate the effects of different operating conditions, oil rheology, lubricant chemistry and low viscosity oil on these important parameters. A new flexible test rig has been designed and developed whereas a high speed synchronized data acquisition system has been employed to hunt for the vital information. A numerical approach based on the lubrication and friction modeling is also the part of this research work to understand the tribological characteristics of the valve train and to predict various important tribological parameters. The experimental results showed that due to shear drag it was not necessary for roller rotational speed to increase with camshaft speed. The lubricant viscosity played a key role in the roller sliding at lower temperatures however at higher oil temperatures negative slip was also observed indicating that component inertia and internal friction have a role to play in roller slip. Relatively, higher magnitude of roller sliding was observed for mineral oil as compared to synthetic oil having almost same viscosity while operating under similar conditions. Good lubrication conditions were also observed at cam/roller interface due to dominance of rolling motion. Increase of roller sliding with corresponding rise in oil film thickness was recorded. A good agreement between the theoretical predictions and experimental evidences was also found. It is strongly believed that the obtained realistic data will provide greater flexibility in validating the predictive mathematical models on the valve iii trains and will be extremely beneficial for the engine designers and lubricant formulators in their ongoing efforts to improve the engine tribological efficiency. 30cm.

Place Hold on Tribological Analysis of Engine Valve Train Performance Considering Effects of Lubricant Formulation /

Experimental Study of Energy Harvesting Flag under the Influence of Wakes /

By Latif, Usman. . 167p. , In this dissertation, an energy harvesting system based on wake flow energy is proposed for microelectromechanical devices that require few watts of energy for their operation. A potential source of small-scale renewable energy that draws a lot of attention is to harvest energy linking up with the piezo-flag’s vibration which is caused by fluid force on it. The stability of the structure is depending upon this applied force and at a critical velocity, it starts to oscillate. To be benefited from this phenomenon a piezoelectric flag is used just like a cantilever beam and placed in the wake of a bluff body. The piezoelectric flag is excited by the shedded vortices in the wake region where a continuous energy transfer process, from fluid to structure, occurs. This oscillation of the piezo flag produces strain energy in the piezo-material that can be transformed into electrical energy and stored in a battery or directly supply power to small sensors using an electrical circuit. The flag in the wake of the cylinder was strongly influenced by the vortices shed from the upstream cylinder under the vortex-vortex and vortexbody interactions. Geometric and flow parameters are optimized for a flexible flag subjected to flapping. For investigating the effect of critical parameters and interactions between flexible bodies and vortices, the current study examined the flexible flags in the viscous flow. Extensive experimentation is performed to see the impact of key parameters along with the bluff body’s shape change on the energy harvesting from piezoelectric flags. Several crosssections of the bluff body covering hollow, solid, and range of cut angles (α= 0° – 180°) are studied. Other parameters including the geometry of the bluff body, L/D (ratio of flag’s length to cylinder diameter), streamwise gap (Gx=S/D, the ratio of the distance between cylinder and flag to the diameter), flow velocity U and bending rigidity γ are optimized and it is found that harvested energy is sensitive to these parameters. Particle image velocimetry (PIV) and videography techniques are used for the bluff body wake and dynamical behavior of piezoflag. However, PIV is done for selective cases but videography is performed for every case. PIV results show that the variation in the time-mean wake for different bluff bodies has a significant impact on the energy harvesting system’s performance. Two different flag configurations are studied to improve the energy harvesting: a single flag behind different bluff bodies, and two tandem formations behind a hollow inverted Cshape cylinder. Variation in synchronization of wake flow with the flag is found with the variation in stated parameters which describes the poorly coupled to an optimally coupled motion state, where the piezo-flag oscillates at the undisturbed wake’s natural frequency. x Furthermore, unidirectional and bidirectional bending is found which explained the variations in the harvested energy. The aim of the wind and hydrodynamic testing is to maximize the strain/mechanical energy by coupling the unsteady wake flow with the vibration of the piezoflag. 30cm.

Place Hold on Experimental Study of Energy Harvesting Flag under the Influence of Wakes /

Modeling and Monitoring of Performance Limiting Factors for Ball-screw Linear Motion Systems /

By Riaz, Naveed . . 143p. , Reliability of high precision linear motion systems is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective Ball Screw (BS) linear drives. A fault in these members severely affects positioning accuracy and safe working of overall system. BS linear drives perform flight / application critical job and are responsible to provide precise linear motion while carrying thrust loading. BS drives are specifically designed on the basis of desired operational parameters like power rating, drive torque, slew rate, efficiency, friction, and mechanical backlash etc. These operating parameters significantly affect the functional performance of linear electro-mechanical systems. At present, few techniques are available to monitor BS drives for aerospace and industrial systems. This research works to improve reliability of ball screw drive linear systems by modeling and monitoring the performance factors through analytical redundancy and intelligent deep learning. In the past, some traditional techniques have been employed to address these problems; however these techniques show limitations like insufficient data acquisition, requirement of dedicated model developer and poor domain adaptation. Recently, deep learning techniques have been introduced and are becoming more popular to detect and characterize various fault signal analysis problems due to their robustness and accuracy. The aim of this research is to provide solution of mechanical faults identification and classification problems for BS linear drives. A fault diagnostic algorithm is designed based on dynamic mathematical model and a remnant filter is implemented to detect signal errors. The remnant filter generates residual signal proportional to the error induced. Fault detection thresholds are set and decision logic is established based on position measurement corrections to compare residual signal with the lower and upper pre-defined threshold constants. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). Current and Position signal data was collected under different load domains. This novel hybrid combination proved very effective in accurate faults identification and classification. The performance of proposed intelligent technique was successfully tested and validated on different datasets including IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) publically published bearing dataset, Paderborn published multi-stage bearing dataset, Current signal dataset for multiple fault modes of BS drive and Position measurement data for multi faults cases for BS linear drive. The actual Signal and Model Fit Simulated Data for BSD system was compared. The testing results proved the effectiveness and superiority of proposed model against different state of the art techniques. The proposed novel framework was also tested for system's stability under different load domains. The results reveal highly competitive values greater than 95% in terms of accuracy and precision for different faults cases 30cm..

Place Hold on Modeling and Monitoring of Performance Limiting Factors for Ball-screw Linear Motion Systems /

On the Predicted Effectiveness of Climate Change Adaptation Measures for Outdoor Thermal Comfort using CFD /

By Zeeshan, Muhammad . . 145p. , The urban heat island (UHI) phenomenon has become a major concern for urban sustainability in the wake of global warming and rapid urbanization. This has resulted in increased heat stress and worsened outdoor thermal comfort in urban microclimates. Vegetation, water bodies, and cool materials are one of the most effective strategies to alleviate the adverse effects of rising outdoor temperatures. Computational fluid dynamics (CFD) has established itself as a valuable tool to model various urban physics phenomena and develop climate change and UHI mitigation and adaptation strategies. However, there exist certain numerical modeling aspects which require further insight. In this work, CFD simulations have been performed to analyze the effect of more realistic vegetation modeling parameters. The vegetation modeling parameters include the actual form drag coefficient and the variable tree transpiration rate. In addition to that, thermal comfort effectiveness of different tree species with its various morphological characteristics, cool materials’ albedo, and water bodies have also been studied in individual and in combination for a real urban area. The morphological characteristics/parameters include trunk height (HT), crown diameter (CW), crown height (CH), and leaf area density (LAD). The wind flow and heat transfer phenomena are simulated using the unsteady Reynolds-averaged Navier– Stokes (URANS) approach. The simulations were performed with proposed adaptation measures for a real urban area having hot-humid climatic conditions under heat wave conditions. It has been found that for the studied climatic conditions, the consideration of more realistic values of these parameters can yield significant variation in the determination of cooling potential and flow characteristics of applied vegetation. Of all the morphological characteristics, LAD, crown height, and trunk height are found to be most influential in providing thermal comfort. Water bodies promotes improved thermal conditions and urban ventilation in spatial direction. Water and vegetation interventions promote the cooling effect by resulting in low ambient air and surface temperature i.e. 0.9 °C and 3.5 °C; 0.3 °C and 3 °C respectively when compared with reference case. Cool materials, when applied simultaneously on both buildings and ground, generate a more pronounced cooling effect than when applied separately on ground or the buildings as it results in a large reduction of air and surface temperature i.e., of 2 °C and 6 °C respectively. Furthermore, the impact becomes more significant for collective application of these adaptation measures. Cool materials when combined with vegetation and water results in large reduction i.e. 2.2 °C and 1.9 °C in air temperature; and 5.9 °C and 9 °C in surface temperature was observed respectively compared to the reference case. For air flow velocity, it is highest for combined cool materials with water with peak effect at the time of highest solar irradiance. The analysis shows that the proposed interventions can effectively decrease surrounding temperature and promote airflow; thereby promoting thermal comfort conditions. 30cm..

Place Hold on On the Predicted Effectiveness of Climate Change Adaptation Measures for Outdoor Thermal Comfort using CFD /

INVESTIGATING THE EFFECT OF CARBONACEOUS NANOFILLERS ON STRENGTH PROPERTIES OF ADHESIVE LAP SHEAR JOINTS /

By Ejaz, Hassan . . 252p. , Adhesive joining presents a compelling substitute to traditional joining techniques, like welding and mechanical fastening. Adhesive bonding offers several advantages, such as the capability to construct lightweight and stiff structures, the ability to join various types of materials, offer improved fatigue performance, and a decrease in heat effects zones commonly associated with welding. However, lack of structural redundancy and moderate strength offered by adhesive joints still makes it an area of exploration for researchers as joint strength is significantly influenced by geometric, surface, manufacturing, and environmental parameters. In recent times, modification to the properties of host resin by the addition of nanofillers is a non-geometric parametric technique proven to be effective in improving the mechanical performance of adhesive joints. In literature, the effects of various fillers (metallic, nonmetallic) have been studied with varying rates of success. The conducted research aims to fill the gap in the non-metallic category by performing a systematic study of the effect of graphene nanoplatelets (GNPs), multiwalled carbon nanotubes (MWCNTs) and reduced graphene oxide (RGO) addition on a high viscous, high strength structural adhesive at various weight fractions of the nanofiller addition. The nanofillers including the functional components of GNPs and MWCNTs (COOH and NH2 functionalized) were added in weight fractions of 0.25, 0.5, 0.75 and 1 wt% in the adhesive. A comprehensive mixing method based on solution mixing technique was developed for uniform mixing of nanofillers in the host resin. The effects of filler addition on the dispersion characteristics, mechanical response of nanofiller/adhesive composite and strength characteristics of two different lap joint configurations were then investigated. The joints were fabricated using aluminum 5083 alloy where adherends were electrochemically treated prior to joining. Lap shear tests were conducted on Universal Testing Machine (UTM). Fourier-Transform Infrared Spectroscopy (FTIR) was utilized for the analysis of functional groups and chemical interaction of nanofillers with the adhesive. Variation in the cure kinetics was investigated using Differential Scanning Calorimetry (DSC). Ultraviolet-Visible Spectroscopy (UV-VIS-Nir) was carried out to quantitatively quantify the dispersion characteristics of nanofillers. ANOVA study was performed for the evaluation of data variation and interaction. Optical and Scanning Electron Microscopy (SEM) was utilized for the analysis of fracture surfaces, and the correlation between nano-reinforcement and strengthening mechanisms was critically discussed. A comprehensive comparison of the x mechanical behavior of bulk adhesive specimens and strength characteristics of lap joints reinforced with GNPs, MWCNTs and RGO was established. The novelty of the research is that, it introduces a pioneering exploration into the combined effects of functional and non-functional components of Carbon Nanotubes (CNTs), Graphene Nanoplatelets (GNPs), and Multi-Walled Carbon Nanotubes (MWCNTs) within high viscous structural adhesive. Unlike previous independent studies, our approach considers filler concentration, dispersion behavior, and diverse lap joint configurations, providing a holistic understanding of their impact on mechanical properties. The developed solution mixing technique ensures uniform nanofiller dispersion, and advanced characterization techniques offer unprecedented insights. This research not only addresses critical literature gaps but also provides a roadmap for tailoring adhesive properties, with wide-ranging implications for automotive, aerospace, marine, and construction industries. The result of the study depicted that the role of non-functionalized GNPs, MWCNTs in improving failure parameters in lap joints was superior to that of nonfunctionalized ones in general. This is a consequence of their superior dispersion properties and higher cross-linking density with the adhesive. However, in comparison between fillers, the strength improvement of lap joints reinforced with MWCNTs was superior to both GNPs and RGO. This is due the lateral length of the MWCNTs particles being greater than GNPs and MWCNTs which provided maximum shearing resistance out of the three nanofillers. The findings of this research can be applied to the aerospace and automotive sectors, construction and infrastructure and general adhesive industry where adhesive joints play a critical role in structural integrity. By incorporating carbonaceous nanofillers into epoxy adhesives, it is possible to enhance the strength and durability of adhesive bonds, resulting in improved performance and safety. 30cm..

Place Hold on INVESTIGATING THE EFFECT OF CARBONACEOUS NANOFILLERS ON STRENGTH PROPERTIES OF ADHESIVE LAP SHEAR JOINTS /

Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems /

By Javaid, Ali . . 205p. , Increasing global energy demand and environmental concerns drive the shift to sustainable alternatives. Solar and wind energy, with their eco-friendly attributes and abundant availability, emerge as key contenders. However, effectively harnessing renewable energy faces challenges due to variability, intermittency, and the need to adapt energy systems to diverse environments. Engineers and planners face the unpredictability inherent in renewable resources influenced by weather conditions, seasonal changes, and geographical variations. As renewable energy grows in the energy mix, integrating fluctuating sources into the grid becomes complex, necessitating energy storage and grid management solutions. Pakistan aims for 16% solar and wind energy by 2040. Technological advancements, including artificial Intelligence (AI), Machine Learning (ML), and improved weather forecasting, enhance renewable energy predictions. Accurate forecasting is crucial for a stable power supply, requiring sophisticated models. Supply side forecasting, fundamental for energy planning, faces challenges due to the unpredictability of solar and wind. Various forecasting techniques, including Transformer, Long Short Term Memory (LSTM), Support Vector Regression (SVR), and Linear Regression (LR), have been explored to improve the accuracy of renewable energy forecasts. The thesis conducts two case studies of energy forecast in Islamabad. The first study focuses on wind speed prediction using LR, SVR, and LSTM models. LSTM emerges as the most effective, achieving 78% accuracy for a 2-day wind speed forecast. Mean absolute error (MAE) serves as the performance metric. Combining techniques optimize prediction accuracy, facilitating the integration of more renewable energy into the grid. Addressing intermittency, storing excess energy as hydrogen is proposed, 6.76 kg estimated hydrogen production per day using wind energy and Proton Exchange Membrane (PEM). Similarly in the second study, hybrid solar and wind energy systems exhibit similar trends, inspiring the exploration of alternative hybrid solutions. The Transformer model predicts energy production, achieving 90.7% accuracy for solar irradiance and 90.45% for wind speed. Additionally, the analysis of model behavior unveiled that the R2 score exhibited a direct correlation with the look-back period and epochs, while demonstrating an inverse relationship with training data, horizon, and learning rate. xiii In conclusion, the global shift to sustainable energy, driven by rising demand and environmental concerns, face challenges in efficiently harnessing renewable energy due to its variability. As countries like Pakistan aim to integrate more renewables into their energy mix, advancements in technology, particularly artificial intelligence and machine learning, play a critical role in improving the accuracy of energy predictions. Accurate supply side forecasting is essential for effective energy planning. In the context of hybrid systems such as solar and wind, the Transformer model stands out for its significant accuracy in predicting energy production. These advancements represent significant advances toward achieving resilience and sustainability in the energy sector. Furthermore, to relieve the challenges posed by intermittency, storing surplus renewable energy in the form of hydrogen is proposed as a promising and viable solution. 30cm.

Place Hold on Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems /

Investigation of Bio-Hybrid Fiber Reinforced Composites Under Impact Loading /

By Masud, Manzar . . 224p. , The integration of natural and synthetic fibers in bio-hybrid fiber-reinforced polymer (HFRP) composites is gaining prominence in high-performance industries such as aerospace and automotive, driven by the demand for materials that balance mechanical performance, sustainability, and cost-effectiveness. This research adopts a dual approach, combining experimental testing with machine learning (ML) to investigate and optimize the mechanical performance of five composite laminates, including a pure carbon laminate and four carbon–flax HFRP configurations with symmetric and asymmetric stacking sequences. All laminates were evaluated through uniaxial tensile, compressive, lowvelocity impact (LVI) at energies from 30 to 75 J, and compression-after-impact (CAI) testing. The symmetric BH3 layup, with evenly distributed flax layers, demonstrated superior performance with only a 9% reduction in tensile strength compared to the carbon baseline while showing a 37.71% increase in failure strain, indicating enhanced energy absorption. Under compression, BH3 retained 86% of the carbon laminate’s strength and 81% of its modulus. In impact resistance, BH3 withstood energies up to 75 J, surpassing the carbon configuration. To evaluate performance and economic trade-offs, two indices were introduced i.e., the Impact Performance Index (IPI) and the Cost-Effectiveness Index (CEI). BH3 achieved the highest impact performance and a CEI comparable to that of the carbon laminate. Complementing the experimental work, an ML framework was employed using stacking sequence and impact energy as inputs, and peak impact force, damage area, and damage extension as outputs. Six algorithms were assessed, including decision tree (DT), random forest (RF), deep neural networks (DNN) with Adam and stochastic gradient descent (SGD) optimizers, and recurrent neural networks (RNN) with the same optimizers. The DT model with depth 8 and 28 leaf nodes performed best for peak force prediction, while the model with depth 6 and 23 leaf nodes was most accurate for damage area. An RNN with SGD and four hidden layers containing 70 neurons achieved the highest accuracy for damage extension. This integrated methodology demonstrates the potential of HFRP laminates to deliver high mechanical performance, improved damage tolerance, and enhanced sustainability for structural and impact-critical applications across automotive, aerospace, sporting, and construction sectors. 30cm.

Place Hold on Investigation of Bio-Hybrid Fiber Reinforced Composites Under Impact Loading /

A Novel Algorithm for Evaluating the Accuracy of In-Cylinder Convective Heat Transfer Coefficient Estimation Models in Port Water-Injected Diesel Engine /

By Janjua, Asad Asghar . . 118p. , This research study focuses on evaluating the accuracy of three classical empirical models (Eichelberg, Woschni, and Hohenberg Models) commonly used to estimate the in-cylinder gas-to-wall spatially-averaged-instantaneous-convective heat transfer coefficient (HTC) in a four-stroke high-speed diesel (HSD) engine. The investigation specifically examines effects of port water injection on HTC at low, part, and high load conditions using a retrofitted port water injection system capable of generating variable water injection rates. The absence of a reliable accuracy determination experimental methodology for HTC models in port water injected-diesel engines has prompted the development of an algorithm. This approach utilizes a novel model-based, sequential flow approach using experimental data to develop an algorithm for determination the accuracy of HTC models. The primary data source consists of 36000 experimentally measured in-cylinder pressure values, obtained at 0.36-degree intervals. Separate pressure measurements were conducted for each of the three loading conditions, with six variations in water injection rates within each condition. The algorithm follows a twostep process. First, thermodynamic models are employed to derive essential parameters such as in-cylinder volume, temperature, internal energy, thermodynamic work, and exhaust heat. In the second step, the algorithm calculates the in-cylinder convective heat transfer coefficients using the Eichelberg, Woschni, and Hohenberg models. Notably, these models yield different HTC values for identical engine operating conditions and water injection rates. Using the derived HTC values, the algorithm calculates the heat loss to the cylinder walls, enabling the determination of the engine's cumulative heat release based on thermodynamic relations involving internal energy, work, exhaust heat, and heat loss. By comparing the fuel cumulative heat release with the engine cumulative heat release, the algorithm calculates the engine's combustible efficiency. To identify the heat transfer coefficient model that closely matches a reference value of 98% for engine combustible efficiency, the algorithm compares the calculated values from all testing conditions. The model that generates the highest number of occurrences closest to the referenced 98% is considered the most accurate heat transfer coefficient estimation model xix for high-speed diesel engines utilizing port water injection. The algorithm was implemented on the data obtained from three operating conditions. Each operating condition was tested without water injection and five water injections rates using successive increase in water mass. Total of 36000 in-cylinder pressure values were used for each operating condition and at each water injection rate to obtain all thermodynamic values used in the research. This led to 54 engine combustible values. These engine combustible values then provided 18 most accurate values corresponding to 18 water injection rates for three engine operating conditions. The accuracy of each HTC model was estimated by algorithm at each operating condition and for entire engine operation as well. According to reference value of 98% combustible efficiency; the algorithm calculated that at low and high loading conditions, Hohenberg model estimated most accurate HTC values with 66.66% and 100% accuracy. At medium loading condition, Woschni model estimated most accurate HTC values with 66.66% accuracy. According to algorithm, Hohenberg model estimated HTC values with 67% accuracy for overall engine operation. This research addresses a critical gap in accurately estimating HTC in water-injected diesel engines and provides valuable insights for optimizing engine performance. 30cm.

Place Hold on A Novel Algorithm for Evaluating the Accuracy of In-Cylinder Convective Heat Transfer Coefficient Estimation Models in Port Water-Injected Diesel Engine /

Experimental Tribological Study of Surface Textured Valve Train Components /

By Siddiqui, Muhammad Rizwan. . 208p. , Reducing wear and friction in mechanical components is critical for enhancing energy efficiency and extending the operational lifespan of engine systems. Recent approaches to friction reduction have focused on lubricant chemistry, surface coatings, and surface modifications. This study explores how micro-texturing can enhance the tribological performance of Thermo-Elastohydrodynamically Lubricated (TEHL) cam-tappet contacts in direct-acting valvetrains. This research uses a two-phase experiment to assess how surface texture area density affects friction under realistic engine conditions. Fiber laser texturing was utilized to create micro-dimples on five tappet shim samples with controlled texture densities of 2%, 5%, 8%, 10%, and 12%. In the first phase, a modified reciprocating tribometer was used to compare untextured and textured shims, leading to the selection of three optimal texture densities (5%, 8%, and 10%). In the second phase, a production engine valvetrain was used to test the selected textured shims under realistic conditions. Consistent tappet components were used to prevent bore friction variations. Friction was measured at various camshaft speeds (300, 500, 700, and 900 RPM) and temperatures (30°C, 60°C, and 90°C), with both the tappet and shim rotating under real conditions. Such a scenario has not been previously explored in existing studies. The analysis focused on the instantaneous frictional torque difference between untextured and textured shims. Results demonstrated a significant reduction in friction, with textured shims achieving up to 18.33% lower friction at 90°C. Among the tested textures, the 8% textured shim exhibited the most consistent friction reduction performance across all engine speeds. The results demonstrate the potential of micro-surface texturing to reduce friction in camtappet contacts within valvetrain systems of internal combustion engines. The promising results offer a pathway toward realizing a more efficient engine. This research offers valuable experimental data and highlights the need to further explore and optimize surface texturing for wider automotive applications. 30cm.

Place Hold on Experimental Tribological Study of Surface Textured Valve Train Components /