547 research outputs found
Emerging Trends in Indian Agriculture: What Can We Learn from these?
No full textAgricultural and Food Policy,
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Map based visual design process for multi-stage gear drives
No full texttextThe primary objective of this research is to develop a visual design process for gear trains with multiple stages of reduction and varying configurational architectures. One of the main challenges in the design of such gear trains is in the sizing of the individual gears such that high levels of performance are obtained in spite of constraints due to different gear configurations. Formal design procedures that successfully meet this challenge are developed. A key contribution of this research is the utilization of these design procedures to create sets of three-dimensional design maps. The design procedures help a designer manage more than 20 design parameters in designing for a broad range of gear train requirements (Rated torque capacity, Volume, Weight, Inertia, Responsiveness, Torque Density etc.) while accounting for assembly constraints. Each set of design maps corresponds to a given set of design parameters, some of which are held fixed and some of which are put in the hands of the designer. The latter set of design parameters are termed in this research as design knobs. They can be ‘tuned’ by a designer in order to generate new sets of design maps. The idea is that a designer, using the design information conveyed to him/her graphically through a given set of design maps, is able to then tune the design knobs to generate an updated set of design maps which reflect design solutions that are more desirable in terms of the application requirements. By adjusting the design knobs and looking at updated design maps, a designer is able to quickly assess the effect of his/her design decisions. The end result is that a single designer is empowered with the ability to quickly arrive at a preliminary design of a gear train that satisfies the design requirements. This preliminary design would be a good starting point for more detailed design development.Mechanical Engineerin
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A performance map framework for maximizing soldier performance
No full texttextSoldiers in the Unites States Army operate under uniquely demanding conditions with increasingly high performance expectations. Modern missions, including counter-insurgency operations in Iraq and Afghanistan, are complex operations. The Army expects this complexity to continue to increase. These conditions affect Soldier performance in combat. Despite spending billions of dollars to provide Soldiers with better equipment to meet the demands of the modern battlefield, the U.S. Army has dedicated comparatively little resources to measuring and improving individual Soldier performance in real-time. As a result, the Army does not objectively measure a Soldier’s performance at any point in their active duty career.
The objective of this report is to demonstrate the utility and feasibility of monitoring Soldier performance in real-time by means of visual 3D performance maps supported by a Bayesian network model of Soldier performance. This work draws on techniques developed at the University of Texas’ Robotics Research Group for increasing performance in electro-mechanical systems. Humans and electro-mechanical systems are both complex and demonstrate non-linear performance trends which are often ignored by simplified analytical models. Therefore, application of empirical Bayesian models with visual presentation of data in 3D performance maps enables rapid understanding of important performance parameters for a specific Soldier. The performance maps can easily portray areas of non-linear performance that should be avoided or exploited, while presenting levels of uncertainty regarding the assessments, thus empowering the individual to make informed decisions regarding control and allocation of resources.
The present work demonstrates the utility of visual performance maps by structuring 19 relatively mature 3D performance maps based on published empirical research data and analytical models related to human performance. Based on a broad review of the literature, the present research evaluated 10 potential physiological indicators, termed biomarkers that correlate with human responses to a select set of stressors, referred to as impact parameters. The 10 evaluated impact parameters affect various components of Soldier performance. The present research evaluated the documentation of these relationships in the existing literature with regard to 9 general Soldier performance measures. Identifying the research supported relationships from biomarkers to impact parameters to Soldier performance measures resulted in a preliminary Bayesian Soldier Performance Model, from which it is possible to create 70 distinct 3D performance maps. Based on the quality of the relationships identified in the reviewed literature, and a contemporary evaluation of existing sensor technology for the related biomarkers, the present research assessed 26 of the potential 70 performance maps as being achievable in the near-term. Continuing development of the Soldier Performance Model (SPM) as proposed in this report has the potential to increase Soldier performance while simultaneously improving Soldier well-being, reducing risk of physical and mental injury, and reducing downstream treatment cost.Mechanical Engineerin
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Standardization for intelligent detection and autonomous operation of non-structured hardware, and its application on railcar brake release operation
No full texttextThis thesis introduces a standard framework for evaluating and planning for desired autonomous (or semi-autonomous) operations, then applies the framework, in detail, to the task of automating emergency brake release before rail-car decoupling. A significant hurdle to be accounted for is the lack of standardization of much of the hardware of interest in industry. Non-standardized rail car components must be formally structured as fully as possible to improve the reliability of the robotic automation. This brake release task requires either pushing or pulling a “bleed rod” that protrudes from the side of each rail car. The requirements for each step of the evaluation and planning process will be laid out in this thesis, as an example of how it should be applied to future automation tasks.Mechanical Engineerin
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Mud motor failure analysis using surface sensor data features and trends
No full textMud motor failure is a significant contributor to non-productive time in North American land drilling operations. Currently, there is limited work on mud motor failure analysis using surface drilling data. The objective of this work was to apply data analytics methodologies on historical drilling datasets to identify features and trends in surface data that contribute most to indicating impending mud motor failure. The methodology involved investigating a large dataset of mud motor runs for certain on-bottom and off-bottom drilling events that are generally known to be leading causes of motor failure. Spikes in differential pressure, pick up practices, drill-off time, etc., were all investigated. Additionally, the impact of temperature on mud motor failure was studied. A combination of the investigated features was analyzed using statistical measures and supervised machine learning algorithms to determine the leading contributors to motor failure. The dataset consisted of 32 motor runs drilled in the lateral section of wells from early-to-mid 2019. These motor runs represented a mix of both failure and non-failure cases. The motor stalls were categorized as either low, medium, or high impact depending on the severity of the differential pressure spike. It was observed that high impact motor stalls and the rate of stalling correlated strongly with eventual failure. Likewise, prolonged exposure to high bottom-hole temperature (exceeding the motor temperature limit) increased the risk of motor damage, most likely through thermal expansion of the stator elastomer. The drill-off time was also found to have some influence on motor failure for the investigated cases. Using these features, statistical significance tests and supervised machine learning models were trained on the dataset to determine the features that contribute most to motor failure. This work shows the feasibility of performing mud motor failure analysis using readily available surface data. It is expected to inform drilling engineers on data analysis methodologies for efficient failure analysis and help them plan future wells more efficiently.Petroleum and Geosystems Engineerin
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An automated cuttings and cavings sensor for monitoring wellbore stability and hole cleaning during well construction
No full textThis research project introduces the first working prototype, that could be deployed in the field and taken into production, of a 3D-imaging cuttings monitoring system that can quantify the volumetric return of cuttings on surface and provide information about the size and shape of cuttings and cavings. Initial challenges, identified in an early field test, and their solutions, incorporated into a working field prototype, are presented. The results of preliminary outdoor tests validate system performance and demonstrate its ability to quantify the volumetric return of cuttings in real-time. Additionally, the test results reveal the potential to detect cavings and to estimate the particle size distribution of cuttings. The results can be used directly for improved hole cleaning management and stuck pipe avoidance in field operations. The development of this cuttings sensor prototype is a major milestone in the field of drilling automation, bringing the industry closer to achieving a fully automated hole cleaning and stuck pipe prevention system.Petroleum and Geosystems Engineerin
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Casing point selection optimization for MPD wells through improved kick tolerance analysis
No full textTraditional casing point selection fails to reflect influx dynamics and leverage managed pressure drilling (MPD) advancements in the well design process, which often leads to over-engineering and increased expenditure. This study proposes a new approach that integrates MPD into the casing design process, employing a reduced drift-flux model (RDFM) to account for complexity. The methodology aims to optimize casing design and enable real-time assessment of kick tolerance (KT) while drilling, exploiting opportunities for optimization. In this study, we present a comprehensive approach for determining kick tolerance thresholds, considering current operating conditions and equipment limitations. Improving on industry-accepted rules of thumb for kick volume (25 bbl) and kick intensity (0.5 ppg), this work delivers a fit-for-purpose kick tolerance criterion. The research indicates that casing point selection can be significantly influenced by previously overlooked factors, such as the impact of temperature on formation stress, kick dilution in drilling fluid and annular distribution, as well as pressure and temperature effects and rig kick detection capabilities. Including these in the design criteria yields more accurate and efficient well construction planning and reduced well control risks. Practical case history results demonstrate the potential to extend critical sections by up to 60% while increasing the manageable kick volume, consequently eliminating at least 2 casing strings out of a 7 string well design. The proposed methodology is shown to be suitable for real-time implementation, allowing drillers to make more informed decisions about casing point selection and section length extension while still ensuring safe well operations. This enhances the flexibility of the drilling process and leads to the reduction and simplification of over-engineered well schematics and excess number of casing strings, resulting in significant well construction cost savings. This work presents a novel approach for MPD well design and casing point selection, considering overlooked factors such as case-specific defined thresholds for kick volume and intensity, complex gas behavior, and temperature-dependent formation stresses for pressure evaluation. Furthermore, it extends the scope to the drilling process by introducing the real-time assessment of KT, generating additional opportunities for optimization, and improving well control safety. In addition, it proves the suitability of an RDFM model for KT assessment.Petroleum and Geosystems Engineerin
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Automated production activity and abnormality detection for well integrity monitoring in HPHT oil production wells
No full textWell integrity monitoring, particularly annular pressure monitoring, is a routine task for detecting signs of abnormality in a producing well and verifying well integrity. Modern technology enables sensors to be deployed in wells and wellheads to monitor pressure, temperature, and other parameters in real-time. However, this process still requires human involvement to analyze graphs, make judgments, and identify abnormalities. Machine learning methods can enhance well integrity monitoring by automating well activity identification and detecting abnormalities in real-time data. In this thesis, two machine learning techniques, an Inference System and Long Short-Term Memory (LSTM) are compared, by applying the models to real-time data to identify production activities and detecting abnormalities. Time-series data including pressure, temperature, and related valve statuses, recorded every minute using sensors on four HPHT oil production wells, was analyzed, cleaned, and imputed before being used in the model. The inference system combined expert knowledge and statistically derived insights to form rules as a part of a rule-based method. In addition, the LSTM model learned from supervised input data and categorized it into production activities such as ‘well shut-in, steady’, ‘well shut-in, transient’, ‘production, steady’, and ‘production, ramp-up’, ‘production, ramp-down’, and ‘other well activities’. This well activity classification helps operators better understand real-time well activities. Furthermore, the detected pressure and temperature abnormalities from steady-state activities, such as ‘well shut-in, steady’ and ‘production, steady’, can be used to determine the state of well integrity. By focusing on trends in tubing and annulus pressure and temperature, the rule-based method used statistical analysis to set rules for abnormality detection, while the LSTM model learned from supervised data for the same. Results show that the LSTM model outperformed the inference system, achieving an F1-score of 94% for well activity classification and 91% for abnormality detection. This method enhances well activity recognition, improves abnormality detection, reduces the risk of delayed well integrity issue identification, minimizes reliance on human judgment, and assists in identifying the cause of well integrity issues with other techniques.Petroleum and Geosystems Engineerin
Original Thinking
No full textHistory that comes to us as a chronology of events is really a collective existence that is evolving through several stages to develop Individuality in all members of the society. The human community, nation states, linguistic groups, local castes and classes, and families are the intermediate stages in development of the Individual. The social process moves through phases of survival, growth, development and evolution. In the process it organizes the consciousness of its members at successive levels from social external manners, formed behavior, value-based character and personality to culminate in the development of Individuality. Through this process, society evolves from physicality to Mentality. The power of accomplishment in society and its members develops progressively through stages of skill, capacity, talent, and ability. Original thinking is made possible by the prior development of thinking that organizes facts into information. The immediate result of the last world war was a shift in reliance from physical force and action to mental conception and mental activity on a global scale. At such times no problem need defy solution, if only humanity recognizes the occasion for thinking and Original Thinking. The apparently insoluble problems we confront are an opportunity to formulate a comprehensive theory of social evolution. The immediate possibility is to devise complete solutions to all existing problems, if only we use the right method of thought development
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Automated computer vision system for real-time drilling cuttings monitoring
No full textIn rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin
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