bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Innovative Approach for the Validation of Computational Structural Outcomes of Octocopter’s Connection Arms Through Advanced Finite Element Methods
Authors: Muniswaran Sabareesh , Raja Vijayanandh , Rajapandi Rajkumar , Rajendran Parvathy , Mageswaran Ramesh , Madasamy Senthil Kumar , Stanislaus Arputharaj Beena 
Published: February 2024

Abstract
Reliability testing on computational structural outcomes is necessary because they are relays approximations. This study uses the connecting arm finite element method to validate computational structural results. The connecting arm’s displacement has been predicted using ANSYS Structural 17.2, a sophisticated analysis tool. This study uses the Multirotor Unmanned Aerial Vehicle (MUAV) connecting arm as a test platform. At various working situations like heavy gust loading, low thermal conditions, etc., ANSYS Fluent 17.2 computes the aerodynamic loads on the sophisticated Octocopter’s connection arms. These conditions compute connection arm weight-related structural outcomes. Through ANSYS Structural 17.2, structural displacements have been obtained due to aerodynamic loads and/or connection arm weight. The typical numerical approach validates the displacements analytically. This novel method has increased the computational results’ credibility. The connecting arms are then computed for equivalent elastic stress, strain energy, equivalent elastic strain, and stress intensity level. This Octocopter survives agricultural, foggy, and dust spraying. The modified convergent-divergent passage-based spraying tank has been mounted to an Octocopter to project fertilizers, insecticides, and chemicals correctly on working domains. Thus, the Octocopter with a convergent-divergent passage-based spraying tank may require additional push to finish the operation. These increased thrusts directly impact propeller performance and connecting arm longevity. Thus, computational structural results of the sophisticated cantilever construction (connecting arm) for lightweight materials are essential. This computational structural simulation imposes over 25 lightweight materials. After that, connection arms for different working situations were found. The Octocopter’s propeller diameter and connection arm design relationship are also unique. Thus, this confirmed computational inquiry will help future researchers build and deploy large payload-based drone connection arms.
Tuning of Natural Frequencies of Aeroengine Compressor Blades Using Spatial Distribution of Ceramic Reinforced Particles Composites Using Multi-Scale Modelling
Authors: Kari Sreedhar , C. P. Mahesha 
Published: February 2024

Abstract
The Aeroengine compressor blades design and weight play vital role in overall performance of the gas turbine engine efficiency. Often a particular blade profile will give more engine performance efficiency but may not have enough strength to withstand the vibration loads. Further if the natural frequencies of the blade coincide with high excitation frequency regions, then it will lead to high cycle fatigue (HCF) failures. In this scenario, it is required to increase the stiffness and weight of the blade by compromising on the aerodynamic performance.
In this paper, it is proposed to use the spatially distributed ceramic particle reinforced composites in local areas of the aeroengine compressor blades without changing the blade outer geometry to move the critical and fundamental frequencies away from the high excitation/vibration loads frequency regions. The typical aeroengine compressor blade geometry with titanium material (Ti-6AL-4V) and Silicon Carbide (SiC) particles as reinforcement have been considered for this case study. The multi-scaling modelling approach has been used using micro-macro scale modelling of randomly distributed particle reinforced composites. The effective material properties like stiffness and density of the composite have been calculated using a micro-scale Unit Cell approach for different volume fractions of the SiC particles. The effective material properties obtained from the micro-scale modelling have been used at local regions of the compressor blade (macro-scale) to simulate the effect of SiC particle reinforcement on the overall macro-scale compressor blade behavior. The natural frequencies of the baseline Titanium blade have been compared with the SiC reinforced composites with different volume fractions. The vibratory stresses and the tip displacements have been compared between the baseline blade and the ceramic particle reinforced blade at different critical frequencies. Using this approach, it is possible to tune the frequencies of the Aeroengine compressor blades design to achieve the improved performance and reduced vibratory stresses which will helps to improve the overall efficiency of the aeroengines with reduced Specific Fuel Consumption (SFC).
A Deep Learning Approach to Build Surrogate Model for Steady State and Transient Metal Temperature Prediction in Gas Turbine
Authors: Soni Raj Kumar , Ojha Avyakt , Verma Vishal , Niu Yi 
Published: February 2024

Abstract
This research paper explores the use of machine learning (ML) to create a surrogate model for steady state and transient metal temperature prediction in industrial gas turbines. This work leverage measured performance parameters from a development test engine to build a surrogate model using ML-based approach. The resulting surrogate model significantly reduces the effort required to derive empirical correlations to predict the tie-bolt temperature of the industrial gas turbine which operates in various environmental conditions and power requirements.
The development engine test involves approximately 10,000 measured signals which includes major performance parameters like speed, fuel flow rate, power, IGV angle, station temperature and pressure along with others testing requirements from different disciplines. Hence various statistical techniques such as heatmap, correlation map, auto-lag, and principal component analysis were employed to identify the key influencing performance parameters to train and test the surrogate model.
Several time-series models were studied for this research, starting from simple regression models such as ARIMA, ARIMAX, and VAR to more complex deep learning models like LSTM. The benefits and drawbacks of different machine learning models were assessed, and their relative performance was compared in terms of temperature prediction accuracy.
The LSTM model was found to be the most accurate and better fit for this kind of sequential problem (time series), with temperature prediction accuracy within 2% for steady-state and 4% for transient conditions. This surrogate model forecasting approach can potentially estimate the real-time remaining useful life of critical parts in the fleet engine.
Furthermore, the paper proposes that the machine learning framework developed for predicting metal temperature in industrial gas turbines can be extended to forecast temperatures for other regions using fleet measured data. This can lead to more accurate critical part lifing calculations based on real-time fleet measured data, which can be used to optimize part replacement, inventory management, servicing, and overhaul scheduling. In essence, this research presents a comprehensive analysis of the benefits and drawbacks of different machine learning models and demonstrates the effectiveness of using an LSTM model for metal temperature prediction in industrial gas turbines.
Overall, this research presents a significant step towards improving the reliability of industrial gas turbines through the use of machine learning approach. The proposed framework has the potential to revolutionize the way in which industrial gas turbines are maintained and serviced, leading to significant cost savings and provide valuable insights for the engineers and researchers working in this field.
Development of Machine Learning Model for Maximum Creep Strain Prediction in Gas Turbine Blades
Authors: Shrivastava Rishabh , Mehrotra Vidit , Singh Amit , Yadav Amit , Das Debdulal 
Published: February 2024

Abstract
Gas turbines blades are subjected to significantly high temperature, pressure, and centrifugal load for prolong period. To withstand this severe operating condition, turbines blades are made up of nickel base superalloy. However, still we observe that creep is one of the dominant failure modes for turbine blade and could cause significant damage to the turbine section. Creep is the progressive time-dependent in elastic deformation resulting from high stress and temperature. Because of above reasons, creep evaluation becomes an imperative part of the turbine design. Creep analysis involves multiple discipline such as aerodynamic, heat-transfer and mechanical integrity. Additionally, conventional creep analysis using finite element model requires defeaturing of models, meshing of geometry, boundary condition application, material definition, solving the model and post processing. The process involves a numerical solution of differential equations that governed these physical phenomena. The above steps need significant time, computational resource, and manpower. The mentioned limitation could be overcome by leveraging data driven techniques for calculating maximum creep strain using system level parameters. In this work, machine learning techniques are explored to predict maximum creep strain in gas turbine blades. Machine learning models will use FEA results as input to predict the maximum creep strain values. Since creep strain is a continuous variable, hence regression-based ML model will be studied. The input parameter used for building the machine learning models are coolant temperature, coolant mass flow rate, hot side mass flow rate, turbine inlet temperature along with blade material. Among all the data, 75% data were used for training purpose and 25% data were used for evaluating the machine learning model performance. With the developed model, time required for creep prediction is reduced significantly. This provides greater flexibility to the designer in terms of evaluating multiple boundary conditions, materials option, coating, and cooling optimization. A comparative study is presented between linear & polynomial regression, random forest, and support vector regressor models. To reduce the prediction error, hyperparameter optimization is carried out for all the studied models. Additionally, a feature importance study is performed to assess the impact of each parameter on the creep strain prediction. Findings from the study show that linear regression model generates the least root mean squared error. Study also shows that Turbine inlet temperature has the maximum impact on creep strain, which also aligns with our physics-based understanding of creep phenomenon.
Investigation of High Thermal Intensity Porous Media Combustion in a Reverse Flow Combustor
Authors: Karad Vinay , Arghode Vaibhav 
Published: February 2024

Abstract
A small-scale (6.25 kW) reverse flow combustor with different fillings of porous media is explored to examine the effect on combustion behaviour, lean operational limit, and NOx, CO emissions. Premixed mixture of reactants is supplied to the combustor. Silicon carbide foam is used as the porous media. The investigation parameters include pore density of the porous media, reactant mixture injection diameter, and the volume occupied by porous media in the combustor. Combustor without porous media is also included in the study for comparison. The combustor is operated at high thermal intensity of 39 MW/m3-atm. Combustion is stabilized in the porous media as observed from its glow. Similar NOx and lower CO emission levels were obtained for the case with porous media. The results highlight the effectiveness of ceramic porous media in achieving stable combustion and low pollutant emissions at high thermal intensity applicable to gas turbine engines.
Spray Combustion Study of Biofuel-Blended Aviation-Grade Fuel in an Annular Air-Swirl Burner
Authors: Balasubramaniyan Manikandan , Pandurangan Nandhakumar , Sahu Srikrishna 
Published: February 2024

Abstract
Growing concern over global warming due to carbon pollutant emissions such as CO2 and CO has led the aviation industry to take numerous steps to reduce carbon emissions. One viable option is to use biofuels instead of conventional hydrocarbon fuels. The net carbon emission produced while using biofuel is still neutral, and therefore, the usage of biofuels has significantly increased over the past decade. However, the physical and chemical properties of biofuels are drastically different from those of hydrocarbon fuels, which can significantly affect the spray characteristics. For example, biofuels generally have a higher viscosity, which can affect the atomization and vaporization process, resulting in lower combustion efficiency and higher pollutant emissions compared to hydrocarbon fuels. Biofuels also have higher cetane numbers and lower calorific values, which can increase the ignition delay time and release lower net heat energy. Despite these parameters being crucial to spray characteristics in a reactive environment, past studies have underrated their importance. The present study aims to investigate the spray characteristics of a commercially available biofuel, particularly under reactive swirl flow conditions.
In the current study, a simplex swirl atomizer housed in a spray burner rig is used to generate the fuel spray. The rig has provision to incorporate an axial swirler in the annular air pathway such that the swirling airflow interacts with the fuel spray at the burner exit. To understand the effect of air swirl on the spray characteristics, experiments are conducted for three different conditions in the presence and absence of air co-low and air swirl. The air and the liquid mass flow are maintained the same. Different optical diagnostics are employed to characterize the spray combustion process. The primary breakup length of the liquid sheet (near the nozzle exit) and the spray cone angle are measured by the shadow imaging technique. The spray flame photographs are captured using a SLR camera. The size of the spray droplets is measured using Interferometric Laser Imaging for Droplet Sizing (ILIDS). In this technique, the spray field is illuminated by a high-power laser sheet. Each droplet is imaged as a rectangular fringe pattern that is generated on a defocused plane due to the interference of the reflected and first-order refracted light scattered from the droplet. The number of fringes formed in an interference pattern is proportional to the droplet size. The ILIDS measurements are obtained at different axial and radial measurement stations. The measured droplet and flame characteristics for different air swirl strengths provide insight into the practical spray combustion behavior of the biofuel, which has not gained much attention in the past.
The Effect of H2 Addition on CNG/H2-Air Flame Shape Topology in a Lean-Premixed Swirl Stabilized Gas Turbine Combustor
Authors: Huda Noorul , Soni Surendra Kumar , De Santanu 
Published: February 2024

Abstract
The present study investigates the flame shape topology in a lean premixed swirl stabilized combustor for various hydrogen thermal powers (Pth.H2) and equivalence ratios (ϕ) using CNG/H2-air mixtures at atmospheric conditions. High-speed OH*-chemiluminescence (OH*-CL) imaging is employed to study flame dynamics. It is found that at a fixed value of hydrogen mole fraction (XH2), an intense and short flame is found for a higher value of ϕ. Also, at a fixed value of ϕ, flame length shortens, and an enhanced heat release is observed near the burner exit with an increase in XH2. A V-shaped flame with the flame tip pointing toward the exhaust port is witnessed at low values of ϕ and XH2. Moreover, the V-shaped flame transitioned into an M-shaped flame with a flickering flame tip near the wall with an increase in ϕ and XH2. Further, the heat release zone gets extended and distributed over the entire length of the combustion chamber for small values of ϕ and XH2. Finally, the stable flame topology is correlated to the non-dimensional parameters Damköhler number (Da), and the wall temperatures (Tw).
Packed Bed Storage Systems and its Modelling Approaches: A Review of Navier- Stokes Equation and Turbulence Equations in Porous Media
Authors: Tiamiyu Nurayn , Hoffmann Jaap 
Published: February 2024

Abstract
Pressure drop prediction in packed bed storage systems is important for the overall cost and performance of solarized gas turbines. There is a need to accurately estimate the pressure drop across the packed beds to size the fans and estimate the system storage efficiency and the associated costs (capital and operating costs) accurately. This study reviews different empirical correlations and turbulence modelling approaches for pressure drop prediction. Flow through packed beds relies on properties such as flow velocity, orientation, particle packing density, and particle shape. Most pressure drop correlations were developed based on some assumptions such as isotropic packaging arrangement, laminar flow, and spherical particle shapes. This does not reflect the transport phenomena of packed beds. This paper provides fundamental insights into the modelling approaches necessary to accurately determine the pressure drop across the packed bed. Also, the limitations of the turbulence modelling approaches for porous media are highlighted in this review.
System Identification Approach to Ocular Tactile Tonometry
Authors: Zhang Qiuchen , Enikov Eniko T. 
Published: February 2024

Abstract
Frequent intraocular pressure (IOP) measurements are necessary for the diagnosis and management of glaucoma. Most current tonometers utilize some form of corneal deformation in order to estimate the IOP. Trans-corneal tonometers require sterile parts, or are complex and expensive (air-puff tonometer). Therefore, their use at home is not practical. Trans-scleral and trans-palpebral tonometry on the other hand, could be utilized at home but is less sensitive and prone to measurement errors. One such method is based on tactile palpation of the eye (palpation tonometry). During tactile palpation, the IOP increases significantly. The goal of tactile tonometry is to estimate the initial (starting) IOP, based on contact forces and displacements obtained during the palpation.
This paper describes the development of two phenomenological models relating the contact forces, displacement, and intraocular pressure during instrumented mechanical palpation of porcine eyes. The first model predicts the changes of intraocular pressure (IOP) and corresponding forces as a function of the applied displacements and initial IOP. The model was build in two parts. First, a pressure vs force and displacement was developed and subsequently a force vs displacement and pressure was added. It is then used to derive a second model that predicts the initial IOP from the applied forces and displacements. Least-squares techniques have been used to extract parameter values of the two models. The first model (14 parameters) produced a maximum error of 1.65 mmHg, while the second model (17 parameters) produced a maximum error of 0.85 mmHg over the test range of 10 to 35 mmHg. The second model leads to an explicit solution for the initial IOP as a function of the applied forces and displacements. When calibrated, this model can be used to interpret palpation data and estimate the starting IOP.
Most commercial tonometers are certified to be accurate within 2–3 mmHg against Goldmann applanation tonometry. They also suffer from larger errors for low (IOP &lt; 11 mmHg) and high (IOP &gt; 21 mmHg). Both models presented here demonstrate sufficient accuracy for the development of a tactile tonometer. Through simulations, it has been shown that accurate prediction of IOP from tactile data requires data from multiple displacements, i.e. an active palpation system is needed in order to realize a tactile tonometer. The ability to predict changes of IOP during palpation tonometery can also be useful in the development of mechanical eye stress tests, replacing the drug-induced Priscol provocative test.
Thermal-Fluid Behaviors and Morphology Evolution of Molten Pool During Selective Laser Sintering of Inconel 625
Authors: Xiao Bin , You Byoung Hee , Jin Tongdan 
Published: February 2024

Abstract
A two-dimensional multi-physics finite element model of the Selective laser sintering (SLS) process on Inconel 625 powders is developed. The model is validated through comparison with experimental data previously published in the literature. Thermal/fluid behaviors and morphological evolution of the molten pool are studied, considering phase transition, recoil pressure, surface tension, and the Marangoni force. The simulation outcomes exhibit the time-varying temperature distribution, fluid dynamics, and surface morphology during the SLS process under different conditions, which helps to identify the impacts of process parameters on the SLS process. The results demonstrate that during the SLS process, the morphology and thermal-fluid dynamics of the molten pool are primarily influenced by the Marangoni force and recoil pressure acting on its surface. Specifically, the recoil pressure at the leading edge of the laser spot plays a crucial role in determining the molten pool’s behavior. The recoil pressure increases exponentially as the temperature rises, causing the liquid metal to move downward and form a depression area at the head of the molten pool, and generates particles that are splashed from the rear edge of the molten pool. The backward Marangoni force, which causes high-temperature liquid at the head of the molten pool to flow towards the cooler liquid at the rear, results in the formation of a vortex that moves the molten pool in the rear region. This study analyzes process parameters such as laser intensity, scan speed, and spot size. The results indicate that increasing laser power and decreasing scanning speed and laser beam spot size can lead to an expansion in both the depth and width of the molten pool.