Fatigue life prediction on nickel base superalloys

Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends. A neural network modelling process has been developed which includes data collection methodology and subsequent filtering techniques in conjunction with training of a neural network model.It has been shown that by using certain techniques to ‘improve’ the input data a network will not only fit seen and unseen Ultimate Tensile Strength (UTS) and Yield Strength (YS) data but correctly predict trends consistent with metallurgical understanding.Using the methods developed with the UTS and YS models, a Low Cycle Fatigue (LCF) life model has been developed with promising initial results.Crack initiation at high temperatures has been studied in CMSX4 in both air and vacuum environments, to elucidate the effect of oxidation on the notch fatigue initiation process. In air, crack initiation occurred at sub-surface interdendritic pores in all cases.The sub-surface crack grows initially under vacuum conditions, before breaking out to the top surface. Lifetime is then dependent on initiating pore size and distance from the notch root surface. In vacuum conditions, crack initiation has been observed more consistently from surface or close-to-surface pores - indicating that surface oxidation is in-filling/”healing” surface pores or providing significant local stress transfer to shift initiation to sub-surface pores. Complementary work has been carried out using PWA1484 and Rene N5. Extensive data has been collected on initiating pores for all 3alloys. A model has been developed to predict fatigue life based upon geometrical information from the initiating pores. A Paris law approach is used in conjunction with long crack propagation data. The model shows a good fit with experimental data and further improvements have been recommended in order to increase the capability of the model

Materials & Machines: Simplifying the Mosaic of Modern Manufacturing

Manufacturing in modern society has taken on a different role than in previous generations. Today’s manufacturing processes involve many different physical phenomenon working in concert to produce the best possible material properties. It is the role of the materials engineer to evaluate, develop, and optimize applications for the successful commercialization of any potential materials. Laser-assisted cold spray (LACS) is a solid state manufacturing process relying on the impact of supersonic particles onto a laser heated surface to create coatings and near net structures. A process such as this that involves thermodynamics, fluid dynamics, heat transfer, diffusion, localized melting, deformation, and recrystallization is the perfect target for developing a data science framework for enabling rapid application development with the purpose of commercializing such a complex technology in a much shorter timescale than was previously possible. A general framework for such an approach will be discussed, followed by the execution of the framework for LACS. Results from the development of such a materials engineering model will be discussed as they relate to the methods used, the effectiveness of the final fitted model, and the application of such a model to solving modern materials engineering challenges

Machine learning algorithms for fluid mechanics

Neural networks have become increasingly popular in the field of fluid dynamics due to their ability to model complex, high-dimensional flow phenomena. Their flexibility in approximating continuous functions without any preconceived notion of functional form makes them a suitable tool for studying fluid dynamics. The main uses of neural networks in fluid dynamics include turbulence modelling, flow control, prediction of flow fields, and accelerating high-fidelity simulations. This thesis focuses on the latter two applications of neural networks. First, the application of a convolutional neural network (CNN) to accelerate the solution of the Poisson equation step in the pressure projection method for incompressible fluid flows is investigated. The CNN learns to approximate the Poisson equation solution at a lower computational cost than traditional iterative solvers, enabling faster simulations of fluid flows. Results show that the CNN approach is accurate and efficient, achieving significant speedup in the Taylor-Green Vortex problem. Next, predicting flow fields past arbitrarily-shaped bluff bodies from point sensor and plane velocity measurements using neural networks is focused on. A novel conformal-mapping-aided method is devised to embed geometry invariance for the outputs of the neural networks, which is shown to be critical for achieving good performance for flow datasets incorporating a diverse range of geometries. Results show that the proposed methods can accurately predict the flow field, demonstrating excellent agreement with simulation data. Moreover, the flow field predictions can be used to accurately predict lift and drag coefficients, making these methods useful for optimizing the shape of bluff bodies for specific applications.Open Acces

APPLYING MACHINE LEARNING MODELS TO DIAGNOSE FAILURES IN ELECTRICAL SUBMERSIBLE PUMPS

Electrical Submersible Pump (ESP) failures are unanticipated but common occurrences in oil and gas wells. It is necessary to detect the onset of failures early and prevent excessive downtime. This study proposes a novel approach utilizing multi-class classification machine learning models to predict various ESP specific failure modes (SFM’s). A comprehensive dataset and various machine learning algorithms are utilized. The prediction periods of 3 hours to 7 days before the failure are evaluated to minimize false alarms and predict the true events. The ML models are based on field data gathered from surface and downhole ESP monitoring equipment over five years of production of 10 wells. The dataset includes the failure cause, duration of downtime, the corresponding high-frequency pump data, and well production data. According to these data, most ESP operational failures are characterized as electrical failures. Four modeling designs are used to handle the data and transform them into actionable information to predict various ESP failure modes at different prediction periods. Several ML models are tested and evaluated using precision, recall, and F1-score performance measures. The K-Nearest Neighbor (KNN) model outperforms the other algorithms in forecasting ESP failures. Some other tested models are Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP) Neural Network, etc. The findings of these ML models reveal that as the prediction period extends beyond three days, it becomes more challenging to predict the true failures. Furthermore, all tested designs show similarly good performances in predicting ESP specific failures. The design that integrates the impacts of gas presence and pump efficiency while minimizing the number of input variables is suggested for general use. Based on the field data, a Weibull model is built to estimate the probability of failure. The mean time between failure (MTBF) values are utilized as inputs to the Weibull analysis. The Weibull shape and scale parameters are estimated using Median Rank Regression. Then the Weibull Probability plots are generated with high R2 values (86.5-99.4%) and a low p-value for all wells. The results show increases in pump unreliability with time for all the wells. By integrating the outcomes of the ESP Failure prediction ML model with the Weibull unreliability model, a powerful tool is provided. This tool allows the engineers to detect failures early, diagnose potential causes, and propose preventive actions. It is crucial in aiding the operators in transitioning from reactive to proactive and predictive maintenance of artificial lift operations

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

Marine Power Systems

Marine power systems have been designed to be a safer alternative to stationary plants in order to adhere to the regulations of classification societies. Marine steam boilers recently achieved 10 MPa pressure, in comparison to stationary plants, where a typical boiler pressure of 17 MPa was the standard for years. The latest land-based, ultra-supercritical steam boilers reach 25 MPa pressure and 620 °C temperatures, which increases plant efficiency and reduces fuel consumption. There is little chance that such a plant concept could be applied to ships. The reliability of marine power systems has to be higher due to the lack of available spare parts and services that are available for shore power systems. Some systems are still very expensive and are not able to be widely utilized for commercial merchant fleets such as COGAS, mainly due to the high cost of gas turbines. Submarine vehicles are also part of marine power systems, which have to be reliable and accurate in their operation due to their distant control centers. Materials that are used in marine environments are prone to faster corrosive wear, so special care also should be taken in this regard. The main aim of this Special Issue is to discuss the options and possibilities of utilizing energy in a more economical way, taking into account the reliability of such a system in operation

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules