http://dspace.dtu.ac.in:8080/jspui/handle/123456789/105 2026-07-23T07:14:51Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/23009 Title: ANALYSIS OF SWIRLING FLOW IN ANNULAR DIFFUSER Authors: PRIYA; Arora, B. B. (supervisor) Abstract: In today’s rapidly advancing world, there is an increasing demand for devices that are energy-efficient, safe, and cost-effective. Available usable energy resources are depleting quickly, creating an urgent need to address this challenge. One possible solution is to develop techniques that can convert otherwise unusable energy into useful forms. However, creating such methods often requires significant effort, and in many cases, the outcomes may not justify the cost or may lack economic feasibility. An alternative and more practical approach is to conserve existing resources by designing energy-efficient systems that minimize energy losses. A diffuser is one such device that plays an important role in energy conservation. It converts the kinetic energy of a flowing fluid—energy that would otherwise be lost—into an increase in static pressure. In turbomachinery systems used for power generation, annular diffusers are commonly employed. These diffusers typically operate under conditions where the incoming flow may contain varying degrees of swirl. Therefore, improving their performance is essential and requires systematic investigation. Experimental research on annular diffusers is often challenging due to the need for advanced instrumentation and complex, time-intensive procedures, making such studies costly and limiting the extent of research in this field. The present study combines both experimental and analytical approaches to investigate the aerodynamic behaviour of axial annular diffusers. A specialized test setup was developed to introduce different levels of inlet swirl. Measurements were conducted v using a three-hole cobra probe to determine static pressure distribution, axial velocity, and swirl velocity profiles at various sections along the diffuser length. In addition to experimental work, computational analysis was carried out using CFD modelling. The study includes grid independence testing and the selection of an appropriate turbulence model that closely matches experimental observations as well as results reported in existing literature. After validation, the CFD model was used to analyse flow characteristics in two types of annular diffusers: one with a parallel hub and diverging casing, and another with both hub and casing diverging at equal angles. Both diffuser configurations were studied for equivalent cone angles of 10° and 20°, and area ratios of 2 and 3. The influence of inlet conditions—specifically velocity profiles with and without swirl angles of 7.5°, 12°, 17°, and 25° — was examined to evaluate diffuser performance. Detailed flow behaviour was analysed, and key performance parameters were calculated. The development of the flow was studied to identify regions of flow separation and reversal within the diffuser. The effects of various factors, including inlet swirl, area ratio, diffuser geometry, and cone angle, were systematically analysed to understand their impact on flow separation and overall performance. The results indicate that inlet swirl has an optimal value that maximizes diffuser performance. This optimal swirl level depends on the geometry and configuration of the diffuser. 2026-06-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22995 Title: FATIGUE FAILURE ANALYSIS OF LOW-PRESSURE STEAMTURBINE BLADE Authors: NITIN; Rani, Sushila (SUPERVISOR) Abstract: Steam turbine blades are the most vital and critical components in the power plants for electricity generation as they convert heat energy into mechanical work. A single failure of a blade can cause the complete shutdown of a plant. Low pressure (LP) blades are exposed to critical working conditions of large centrifugal forces, and dynamic forces due to changing steam loads along with corrosive effects that contribute to the susceptibility of LP blades to fatigue failure. Thus, in particular, L-0 stage blades are most susceptible to fatigue and fracture type of failures. Therefore, the reliability and performance of LP blades depend on preventing or understanding such failures. This paper provides a comprehensive review of fatigue failure behavior of low-pressure steam turbine blades using both an experimental investigation and an integrated computational and residual stress analysis. A failure analysis of a martensitic stainless-steel alloy X10CrNiMoV 12-2-2, L-0 stage low-pressure steam turbine blade was conducted in this research. A transverse crack existed at the leading edge of the blade that propagated toward the trailing edge of the blade. Detailed examinations of the morphology and origin of the crack were performed using a combination of mechanical testing, fractographic examination utilizing scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), and visual inspections. The microstructural examination showed a disturbed martensitic structure which is typical of a heat-treated turbine-grade steel and SEM fractography showed typical fatigue striations and intergranular cracking. The EDS results confirm the presence of corrosion promoting products such as chlorine, silicon and oxygen and Sio2 particles on the fracture surface of the blade which results in corrosion fatigue a predominant failure mode. A residual stress analysis was conducted to gain a further insight into the internal stress conditions that contribute to the initiation and propagation of cracks, using a µ-X360 FULL 2D portable X ray residual stress analyzer that relies on the cos v α method of analysis. The findings showed that tensile residual stresses on the blade surface exist, which are the reason for stress concentrators and enhance fatigue crack development during cyclic loading. The residual stresses that were generated during the manufacturing and service in operation were discovered to have a major impact on the fatigue performance and life of the blade. The stress distribution at steady operational loading was assessed by means of the static structural analysis to determine the potential regions that are critical and could fail during operation. Dynamic analysis was also used to calculate the natural frequencies and critical speeds of the blades by Campbell diagrams, to avoid resonance at start-up and shut-down periods. The simulations of fatigue crack propagation were possible with the help of a hybrid computational method combining ANSYS and FRANC 3D. The rubber box method in FRANC 3D was used to create a sub model with a template radius of 0.5 mm and an initial edge crack of 2 mm and then the evolution of stress intensity factors KI was analyzed through a series of load cycles. The driving force that controlled the crack propagation was assessed using the stress intensity factor KI and the simulated fatigue life was about 38,414 cycles. When the loading continued, KI reached a maximum of approximately 3640 MPa√mm at the 86th step, which was greater than the fracture toughness of the material and signified the beginning of an unstable crack propagation. The findings emphasize the importance of advanced simulation tools in predicting the fatigue behaviour of LPST blade in order to minimize the catastrophic consequences associated with failure. In addition to providing an integrated understanding of fatigue failure in low-pressure steam turbine blades via a correlation of metallurgical studies, residual stress distributions, and computational fracture mechanics; the study also shows that both tensile residual stress and vibrational resonance contribute to the failure of these blades; as well as corrosion fatigue. 2026-06-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22973 Title: EXPLAINABLE AI BASED PREDICTIVE MAINTENANCE AND ANOMALY DETECTION SYSTEM FOR INDUSTRY 4.0 USING MACHINE LEARNING AND DEEP LEARNING Authors: SOREN, SUNNY; Srinivas, K. (SUPERVISOR); Ansari, Naushad A.(CO- SUPERVISOR) Abstract: The advent of Industry 4.0 has revolutionized the way manufacturing companies operate by monitoring, maintaining and optimising industrial equipment. One of the most effective uses of this transformation is Predictive Maintenance (PdM), which moves maintenance from a reactive and time-based approach to a data-driven, condition-based and anticipatory approach. There is a major lack in the literature and currently in systems: most of the predictive maintenance models focus either on predictive accuracy or explainability, or on explainability without considering robustness under realistic data scenarios, like sensor noise and class imbalance. This big project proposes an Explainable AI (XAI) framework for Predictive Maintenance and Anomaly Detection System, designed and tested with the widely used UCI Machine Learning Repository AI4I 2020 Predictive Maintenance Dataset. To systematically compare the three representative machine learning models, Logistic Regression (LR), Random Forest Classifier (RF) and a Feed-Forward Neural Network (Multi-Layer Perceptron, MLP), three carefully designed experimental conditions are provided: (1) a clean baseline dataset, representing ideal data quality; (2) a synthetically noised dataset, with Gaussian noise added to the data, representing real world sensor drift, calibration errors and measurement uncertainty; and (3) a synthetically over-sampled dataset (via Synthetic Minority Over-sampling Technique, SMOTE), to address the class imbalance encountered in industrial failure prediction tasks. The project puts in place an Autoencoder-based unsupervised module for anomaly detection in addition to supervised classification. The Autoencoder is trained with operational data without any failure labels and considers a failure as being flagged when the reconstruction error exceeds the 95th percentile set using a statistically motivated threshold, which is determined by the learned data, setting the limit to be the values of the normal machine behaviour. Two complementary approaches are used to obtain model explainability: using SHAP (SHapley Additive exPlanations) with the Random Forest through TreeExplainer, and LIME (Local Interpretable Model-Agnostic Explanations) with the Neural Network. SHAP offers explanations at the individual instance level using waterfall plots and explanations at a higher level of abstraction by ranking the most important global features via a beeswarm plot, the mean absolute SHAP bar plot, and a feature interaction dependence plot. Local linear surrogate explanations are offered for individual predictions by LIME, allowing maintenance engineers to grasp the model behaviour at the instance level. Experimental results indicate that the Random Forest model gives the best F1 score (1.00) for the clean dataset, followed by SMOTE augmented data (0.99), and lowest under Gaussian noise (0.95), which is the highest noise robustness among the three models. Even on well-structured tabular PdM data, Logistic Regression shows a consistent F1-score of 0.99 for all three data conditions, making it a very well performing simple and interpretable model that remains competitive over a variety of data conditions. The highest variance is in the case of the Neural Network, which achieves F1=0.93 for clean data, F1=0.98 for noisy data (with implicit regularisation effects of the added noise), and F1=0.93 for SMOTE data. The binary failure mode flags — PWF (Power Failure), HDF (Heat Dissipation Failure), OSF (Overstrain Failure) — clearly stand out as the most important failure predictors, while Rotational Speed and Torque are the most impactful continuous feature drivers. LIME explanations from the Neural Network align with SHAP results, thereby offering cross-method validation of explainability results. The distribution of the reconstruction errors of the Autoencoder reveals the separation between the normal and anomalous instances, and the Autoencoder is able to isolate operational records that are anomalous. The results above clearly proof the synergy of the classical and deep learning models with the explainable AI, unsupervised anomaly detection, resulting in a strong, explainable, practically viable PdM solution that can be well adapted to the Industry 4.0 manufacturing environments. It is executed with open source Python libraries and is accessible on standard hardware, so it can be utilized by small and medium scale enterprises without any special computational hardware. 2026-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22969 Title: FREE INFLATION OF AN ANISOTROPIC HYPERELASTIC CIRCULAR MEMBRANE Authors: BISHWAKARMA, SUNIL; Sahu, Satyajit (SUPERVISOR) Abstract: Inflation of a thin elastic membrane is a well-studied classical problem in nonlinear continuum mechanics; however, the impact of material anisotropy on the inflation response of hyperelastic materials has yet to be thoroughly researched. Therefore, this research examines the axisymmetric axial deformation behaviour of a circular membrane composed of an incompressible anisotropic Mooney-Rivlin material being blown up against a uniform transverse pressure load, without any initial radial pre strain. The derivation of governing equations comes from the principle of stationary potential energy, with the strain energy density function being expressed using the two principal stretches from the plane of the membrane and an anisotropic invariant 𝐼4 = 𝜆1 2 for the thickness of the membrane. The invariant thus gives rise to one stretch in the thickness direction, since there can be no volume change under the assumption of incompressibility. Each of these principal stretches is identified by introducing the appropriate field variable so that the governing equations are reduced from two second-order ordinary differential equations into a system of three first-order ordinary differential equations. The converting or changing of the governing equations into a different format allows for easier numerical integration using standard initial value problem solvers. The two-point boundary value problem resulting from this research has been solved using the combination of the shooting method and the optimization procedure using fminsearch. The primary advancement in the numerical solution is the established verification of the scaling invariance property inherent to the equilibrium equations applies to the anisotropic equations, thus allowing for the elimination of the arc-length continuation methods in all cases where the pressure-stretch relationship has limit point instability. Presented here are numerical results for two different pairs of Mooney-Rivlin material parameters, 𝛼 = 𝐶2 𝐶1 =0.01,0.03,0.1 and four different levels of anisotropy, 𝜁 = 0,0.01,0.03,0.05, with no pre-stretch (𝛽 = 1). The results show that anisotropy causes a stiffening effect in the inflation response, through the pressure-deflection curves. The pressure-deflection curves for 𝛼 = 0.01 (close to Neo-Hookean behaviour) show that the limit point is always there regardless of connectivity level 𝜁. Critical pressures remain about the same for each value of 𝜁. When working with 𝛼 = 0.1 (which has a large component for 𝐶2), the response is monotonic and stable; but as the value of 𝜁 increases, maximum deflections will decrease by as much as 56 at a pressure of 10. The stress resultants show that inflation creates anisotropic behaviour in the meridional and circumferential components of the material properties (highest stresses at the clamped edges). The peak stress at the clamp reduces and circumferential stress increases at the interior of the structure when the anisotropy parameter is increased. The 3D surface reconstruction and membrane profiles have shown that hysteresis in the anisotropic membrane is less than that of the isotropic membrane at similar elevations (pressure). This study will benefit the designer of any inflatable structure requiring uniform strains and controlled directional stiffness. 2026-06-01T00:00:00Z