Research Publications

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Start date: 2020End date: 2029

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    THERMOHYDRAULICS OF TURBULENT FLOW THROUGH SQUARE AND RECTANGULAR DUCTS WITH TRANSVERSE RIBS AND TWISTED TAPES WITH AND WITHOUT OBLIQUE TEETH
    (Journal of Enhanced Heat Transfer, 2022-06-22) Sujoy Saha
    Thermal and friction characteristics of turbulent flow through square and rectangular ducts with periodic transverse ribs and different types of twisted tapes with and without oblique teeth have been studied experimentally. Circular ducts have also been used. Correlations for predicting the friction factor and the Nusselt number have been developed and performance has been evaluated. Although both the friction factor and the Nusselt number are higher for all types of twisted tapes with oblique teeth in combination with transverse ribs, the performance evaluation has shown that the ducts with transverse ribs and regularly spaced twisted-tape elements with oblique teeth are better than those in the case without oblique teeth and this is recommended. Also, since the pressure drop in a heat exchanger is a small fraction of the total system pressure drop, the heat transfer being higher, full-length and short-length twisted tapes with oblique teeth in combination with transverse ribs can be recommended since the heat exchanging surface area requirement will be less.
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    The investigation on stability and physicochemical properties of multi ferrites nanoparticles dispersed Tamarindus indica biodiesel
    (International Journal of Thermofluids | Elsevier, 2025-02-28) Jaikumar Sagari
    This study aims to investigate the stability and physicochemical properties of nickel and manganese doped bismuth ferrite nanoparticles (BNiFMO) in a Tamarindus indica biodiesel. The BNiFMO nanoparticles were evaluated at concentrations of 50 mg/L and 75 mg/L. In addition, the BNiFMO nanoparticles were supplemented with dispersants (Tritonx and QPAN) at different ratios, namely 1:0.25, 1:0.5, 1:0.75 and 1:1, respectively. The stability study was carried out using the principle of photo spectroscopy at three different time intervals: Week 1, 2 and 3. The stability was evaluated by transmittance and absorbance. In addition, the physicochemical properties were evaluated according to ASTM standards. The transmittance of BNiFMO nanofuel spiked with Tritonx and QPAN80 was lower compared to the base nanofuel, while the absorbance increased, indicating better stability. At lower ratios of nanoparticles and Tritonx/QPAN, stability decreased, but better stability was achieved at a 1:1 ratio. The QPAN-based nanofuel was found to be more stable overall than the base nanofuel and the Tritonx-based nanofuel. The lower transmittance and higher absorbance were noticed with B20 +BNiFMO75 mg/L +QPAN 75 mg/L, while the stability decreased slightly with increasing duration. The minimum transmittance and higher absorbance values recorded were 87.75 % and 4.42 in week 1, 89.93 % and 4.23 in week 2, and 91.21 % and 4.18 in week 3. Finally, the addition of Tritonx and QPAN to BNiFMO nanofuel led to an increase in calorific value and cetane number. The highest calorific value and cetane number recorded were 41.456 MJ/kg and 64, respectively, for the B20 +BNiFMO 75 mg/L +QPAN 75 mg/L blend. However, the kinematic viscosity and density exhibited somewhat inconsistent trends.
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    Exploring the necessary upgrades in port infrastructure to accommodate and support the operation of the next‑generation green ships
    (Marine Systems & Ocean Technology | Springer, 2025-04-14) Abhijit Arvind Mohite, Emil Mathew
    The transition to green shipping is critical for addressing the maritime industry’s environmental challenges amid rising concerns about climate change and pollution. Green ships utilize alternative fuels and innovative technologies to reduce their carbon footprint, necessitating substantial upgrades to port infrastructure. This study employs a positivist approach, using a quantitative research methodology to analyze the financial, technological, and environmental requirements for accommodating these vessels. A descriptive research design, coupled with stratified random sampling, captures stakeholder perceptions from diverse groups, including industry experts and port authority officials, through a Likert-scale questionnaire. The findings reveal strong consensus on the urgent need for improvements in docking facilities, modern fuel supply systems, and waste management, indicating financial and technological challenges. Stakeholders largely believe that adequate financial resources exist for these upgrades, emphasizing the importance of government funding and private investment. There is a general agreement that upgrading infrastructure will decrease carbon emissions, while calls for stricter regulatory enforcement and legal incentives persist. Overall, this research underscores the readiness of ports to accommodate green ships and highlights the potential for aligning investments with global sustainability goals. The originality of this study lies in its comprehensive analysis of stakeholder perceptions and the integration of multiple dimensions—financial, legal, environmental, and technical—in evaluating port infrastructure upgrades for green shipping.
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    PHOTOVOLTAIC-FED MOTOR DRIVE SYSTEM FOR NEXT-GENERATION ELECTRIC VEHICLES
    (INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 2025-06-30) S Thangalakshmi
    The growing demand for zero-emission, environmentally friendly Electric Vehicles (EVs) is driven by both current and anticipated energy crises, supported by government policies and evolving market trends. Addressing this need, the present study proposes a novel control strategy for an induction motor drive system in EVs, primarily powered by solar PhotoVoltaic (PV) panels. The system employs Field-Oriented Control (FOC) technology to ensure precise motor control, enhanced performance, and reduced energy losses. To enable real-time monitoring and control, an Internet of Things (IoT) based system is integrated, providing valuable insights into the motor drive’s operation and energy consumption. The incorporation of solar PV energy offers a sustainable, long-term alternative to conventional grid-powered sources. The FOC technique further ensures efficient and reliable motor drive operation under varying load conditions. Overall, the synergy of solar energy, advanced motor control, and IoT monitoring presents a highly efficient and eco-friendly solution for future EV applications.
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    Effect of nutrient-based alloying elements on biodegradable magnesium alloys: Evolution, challenges, and strategies for orthopaedic applications
    (Biomedical Engineering Advances | Elsevier, 2025-03-28) A Saikiran
    In recent years, magnesium (Mg) alloys have become increasingly popular in orthopaedic applications as biomaterials. Unlike traditional implants such as cobalt-chrome, stainless steel, and titanium alloys, Mg alloys offer notable advantages, including outstanding biodegradability and biocompatibility. This characteristic eliminates the need for a second surgery after the bone healing process, a distinct advantage for patients. Additionally, Mg alloys address the issue of stress shielding, a common problem with other materials. Despite facilitating the osteoconductive process, their rapid degradation in physiological conditions poses a challenge, compromising mechanical strength and hindering bone tissue recovery. This degradation leads to tissue alkalization and the formation of hydrogen bubbles, hindering the recovery rate of bone tissues and limiting the applications of Mg alloys. And the rapid degradation of magnesium alloys in physiological conditions accelerates corrosion and compromises mechanical integrity, affecting their load-bearing capacity. Enhancing structural integrity is essential to ensure sufficient strength during bone healing, aligning the degradation rate with the physiological process. To reduce the fast degradation rate, extensive research has been conducted in mechanical and corrosion-based studies, focusing on altering the biomedical performance of Mg alloys through alloying elements, processing routes, and other strategies. One approach involves mixing pure magnesium with nutrient materials and reinforcing it with hydroxyapatite. These modifications aim to match the corrosion rate with the healing rate of bone tissue. This paper explores the significance of biodegradable Mg alloys, providing a comprehensive review of their evolution and development. It emphasises enhancing the mechanical and corrosion properties of Mg alloys by adjusting the percentage of alloying elements, employing specific processing strategies, and incorporating reinforcements. The discussion particularly emphasizes the impact of nutrient elements, binary and ternary alloys, as well as hydroxyapatite composites of magnesium-based alloys in physiological conditions. Furthermore, the review highlights emerging technologies like Laser Powder Bed Fusion (LPBF), offering a general perspective on improving the mechanical and corrosion properties of Mg alloys for orthopaedic use.
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    Sustainable antifouling coating technologies for the maritime industry: An evolutionary overview
    (Progress in Organic Coatings | Elsevier, 2025-12-08) Anirban Chakraborty, C. Pradeep Raja, Sachin Kumar Badhan, Pankaj Sharma
    Biofouling of marine submerged structures due to the colonization of marine micro and macro-organisms continues to pose severe operational and ecological challenges. Traditional antifouling paints containing Tributyl Tin and Cybutryne were banned by the International Maritime Organization in 2008 and 2023 respectively due to their toxicity to marine ecosystem. Currently available antifouling paints fall into two broad categories: ablative or sloughing paints used for smaller vessels, and hard coat paints, such as vinyl and epoxy coatings, used for larger ships. Functionally, these coatings can be grouped into Foul release coatings, Protein resistant coatings, and the more recent Bioinspired coatings. This paper presents a consolidative review of modern antifouling coating technologies and their transition from conventional chemical formulations to advanced, biologically inspired and data-driven approaches. Eight major fabrication and surface engineering techniques, including deposition, templating, etching, electrostatic deposition, nanocomposite synthesis, additive manufacturing, micromachining, and self-assembly, have been discussed with reference to their antifouling mechanisms, benefits, and limitations. Special focus is given to laser-based micromachining methods, which enables precise modification of micro and nanoscale surface topographies. The review also explores the development of hybrid organic and inorganic coating systems, multifunctional and environmentally responsive materials, and the application of computational and machine learning tools for predictive design and accelerated testing of antifouling coatings. By combining these experimental and computational strategies, the study outlines a coherent direction for the creation of next generation coating systems that exhibit structural innovation, self-repairing capability, and intelligent performance. The paper concludes that collaborative research between laboratory scientists and the maritime industry will be essential for developing durable, effective, and environmentally sustainable antifouling solutions for future marine applications.
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    Protecting the Traditional Knowledge of Coastal Communities for Climate Resilience
    (Rivers Unbound | Routledge, 2025-06-02) Gabriela Michael
    The increased changes in climatic conditions are not a new phenomenon, yet the repercussions can be seen in almost every sphere of human existence in recent times. Taking place at an unprecedented pace, the coasts and coastal communities are the most affected due to these changes causing severe damage to the nations. The world is on the verge of climate warfare, wherein every country is battling the multifold crisis posed by climatic conditions, adapting to climate change has become a task for everyone worldwide; thus, finding a sustainable solution has become quintessential. Coastal communities are not only the eyes and ears for the nation’s maritime security, but they also possess traditional knowledge developed over generations, offering valuable insights into climate resilience and adaptation strategies. However, this precious cultural heritage is vulnerable to erosion due to climate change, urbanization, and cultural homogenization. This research aims to record, preserve, and promote coastal communities’ traditional knowledge, emphasising their distinctive practices, beliefs, and climate adaptation solutions. The study aims to recognize and document traditional knowledge about climate adaptation, such as indigenous methods and ecosystem-based management techniques in India and other countries, as well as to examine the role of coastal communities in improving resilience to climate change and informing sustainable economic growth. Finally, the study intends to examine the policy and laws recognizing and protecting traditional knowledge in climate governance, emphasizing the necessity for a comprehensive international legal framework for protecting coastal communities’ traditional knowledge for climate resilience that benefits all countries through information sharing.
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    A comparative experimental study on the prediction of renewable oils properties using RGB and HSV image processing techniques
    (Petroleum Research | Science Direct, 2025-03-04) Aditya Kolakoti
    In this study, renewable oil properties of Flash Point (0C), Fire Point (0C), Density (kg/m3), Cloud Point (0C), Pour Point (0C), and Viscosity (cST) are predicted using image processing techniques of Red Green Blue (RGB) and Hue Saturation Value (HSV). Eleven types of renewable oils are chosen for experimentation, and their surface images are captured with a high-resolution digital camera. For better accuracy, around 150 surface images are captured for each oil sample, and their average pixel data is extracted using RGB and HSV techniques. The digital pixel information (metadata) of all the oil samples is mapped to their experimental oil properties, and the accuracy of the developed metadata is validated with Fiji software due to its better image analysis and also complex data quantifying capabilities. The minimum, maximum, mean, mode and standard deviation results of RGB and HSV agree with Fiji. In addition, the developed dataset has been validated with Neural Network classification and TreeBagger algorithms. The results of TreeBagger reveal that the trained dataset is highly accurate (91.9% for RGB and 95.3% for HSV). Similarly, 95.6% (RGB) and 97.3% (HSV) accuracy is achieved for Neural Network classification. Finally, two new oil surface images are trained using the developed dataset. Both RGB and HSV accurately predict the oil properties. Therefore, it is evident that predicting the significant oil properties helps optimize the production process by reducing experimental costs and time.
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    Predicting Common Rail Direct Injection (CRDI) engine metrics using nanoparticle-enhanced pongamia pinnata biodiesel with machine learning
    (Emergent Materials| Springer, 2025-07-23) Rakesh Kumar Tota
    The effective use of fossil fuels in compression ignition (CI) engines results in hazardous pollution into the atmosphere. Many scholars explored using biodiesel and its blends to replace fossil fuels and reducing hazardous emissions. The current study extends preparation of biodiesel through transesterification process from the karanja oil feed stock as an alternative energy for twin cylinder, Common Rail Direct Injection (CRDI), CI engines. Moreover, various types of metal and non-metal-based nanoparticles (NPs) are dispersed in karanja oil based B20 (20% of biodiesel is blended in 80% of diesel) sample in the presence of QPAN80 surfactant. To prepare stable and homogeneous nano assisted fuel blends the optimum ratio of 1:4 (NPs to surfactant) is used. The Diesel R-F Simulation software is used to find the calibration of CI engine using diesel fuel. The cylinder pressure (CP), brake thermal efficiency (BTE) are found to be 5% variation, while the brake specific fuel consumption (BSFC) is with in the 10% range. The performance parameters are found to be enhanced BTE increased by 9.8%, and BSFC is decreased by 18.7% for B20CNT50 mix at maximum brake power (BP) as compared to B20 blend. Further, the emission characteristics including carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and smoke are found to decrease by 7.71, 11.2, 10.2, and 5.71% for B20CNT50 mix at higher BP as correlated to D100 sample. Nevertheless, the nitrogen oxides (NOx) emission is lowered by 10.9% for B20CNT50 blend than the B20 mix at maximum BP. These findings disclosed that the B20CNT50 sample offers higher performance and lower emission characteristics and potential to use in CRDI, CI engines without engine modifications. Additionally, machine learning algorithms such as support vector regression (SVR), and random forest (RF) are implemented in accurately predicting engine performance and emission characteristics by analyzing relationships between input and output parameters. Their ability to model these interactions enables optimized engine design and reduced experimental cost.
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    Application of Neural Network for Reducing Emission and Optimizing Performance of Hydrogen with Biofuel CI Engine
    (Machine Learning for Social Transformation | Springer, 2025-01-03) Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra, Manashi Chakraborty
    Fuel selection influences internal combustion engine (ICE) performance and toxic emissions. However, predictive monitoring plays an imperative function in the validation and support of the machine. It optimizes engine running performance, reduces emissions, and increases efficiency. This study investigates the emission reduction and optimization of brake thermal efficiency (BTE) using a blended fuel—hydrogen, biofuel, and water on a one-cylinder compression ignition (CI) engine. Simulink simulations are used to collect data, which is then preprocessed and analyzed using advanced feature extraction to increase prediction accuracy. In this paper, a hybrid deep reinforcement learning, and artificial neural network (DRL-ANN) is initiated and designed to predict CI engine emission attributes. To optimize the prediction model, this method combines DRL and neural networks. As a result, the model achieved superior predictive accuracy compared with earlier approaches regarding accuracy (BTE 0.96851, CO 0.95124, HC 0.96624), mean-squared errors (BTE 0.00018, CO 0.00058, HC 0.00055) and R2 (BTE 0.95478, CO 0.94694, HC 0.97015). This study demonstrated the prediction model's efficacy in optimizing CI engine running characteristics and fuel types.