Finite difference code for velocity and surface traction of a Fluid between Two Eccentric Spheres

The Numerical study of the flow of a fluid in the annular region between two eccentric sphere susing PHP Code is investigated. This flow is created by considering the inner sphere to rotate with angular velocity 1 ï— and the outer sphere rotate with angular velocity 2 ï— about the axis passing through their centers, the z-axis, using the three dimensional Bispherical coordinates (ï¡,ï¢ ,ïª) .The velocity field of fluid is determined by solving equation of motion using PHP Code at different cases of angular velocities of inner and outer sphere. Also Finite difference code is used to calculate surface tractions at outer sphere

Analysis of interplanetary solar sail trajectories with attitude dynamics

We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system

Correlations Between Shoulder Rotational Motion, Strength Measures and Throwing Biomechanics in Collegiate Baseball Pitchers

Pitching involves high stresses to the arm that may alter soft tissue responsible for controlling biomechanics. It has been hypothesized that imbalances in strength and flexibility of the dominant shoulder lead to decreased performance and increased injury risk, but it is not fully known what specific pitching biomechanics are altered. There is a critical need to determine correlations between shoulder rotational strength, range of motion and pitching kinetics. Without such knowledge, identifying potential for injury from shoulder imbalances will likely remain difficult and invasive. The goal of this study was to determine correlations between shoulder rotational strength and range of motion and kinetics. Twelve collegiate pitchers participated in this IRB approved study. The clinical measures session tested shoulder rotational range of motion and strength and grip strength. The motion analysis session tested pitching biomechanics. Paired t-tests investigated differences in strength and range of motion between arms. Linear regression was performed to determine correlations between clinical measures, kinetics and pitch velocity. Regression learner neural networks were created to predict pitch velocity and elbow varus torque using clinical measures as inputs. The dominant arm had significantly higher external rotation and total range of motion than the nondominant arm. The nondominant arm normalized external rotation peak torque was significantly greater than the dominant arm at 0˚ external rotation. Correlations were found between elbow varus torque and isometric external/internal rotation ratio, and between shoulder posterior shear force and isokinetic eccentric external rotation/internal rotation ratios. Correlations to velocity included grip strength, concentric external rotation peak torque, isometric internal rotation peak torques, and isometric external rotation peak torques. The neural network accurately predicted velocity, with the standard deviation of the error equal to 2.29 (2.97%). These correlations associate two testing methods to identify injury risk. Increasing external/internal rotation ratios may decrease elbow varus torque and shoulder posterior shear force. Increasing external rotation, internal rotation, and grip strength may lead to velocity gains. Velocity can be predicted using clinical measures and a neural network

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program: 1996

The objectives of the program, which began nationally in 1964 and at JSC in 1965 are to (1) further the professional knowledge qualified engineering and science faculty members, (2) stimulate an exchange of ideas between participants and NASA, (3) and refresh the research and teaching activities of participants' institutions, and (4) contribute to the research objectives of NASA centers. Each faculty fellow spent at least 10 weeks at JSC engaged in a research project in collaboration with a NASA JSC colleague

Stuck pipe prediction in deviated wellbores: a numerical and statistical analysis.

Due to the significant non-productive times and recovery costs associated with stuck pipe events in oil and gas drilling operations, there is value in being able to predict an impending stuck pipe event. To achieve this, the use of numerical cuttings transport (hole cleaning) models and statistical analysis of real-time drilling data is proposed by this research. Current cuttings transport models are based on unhindered, free settling in the wellbore and do not adequately account for the effect of vortices created as the drill string rotates about its axis. This thesis addresses both shortcomings, and presents improved cutting transport models that consider hindered centrifugal settling of drilled cuttings, effect of Taylor vortices and Van der Waals forces. The implication is that the resulting cuttings settling velocity used to estimate critical transport velocities and flow rates are more representative. The transport ratio, a measure of the hole cleaning efficiency, is consequently more realistically predicted. Although several proprietary automated stuck pipe prediction tools exist in the industry, this research found that they broadly fall into five main groups. It is also apparent that current capabilities do not simultaneously and continuously combine real-time data, offset wells data and well design analytical models in a single approach. On that basis, this thesis presents an integrated stuck pipe prediction concept that utilizes all three data streams, called the "ROW" approach. The concept presented in this thesis was then coded into a tool called the stuck pipe index (SPI). The SPI tool risk assessment is determined in real-time and is referenced by a traffic light alert system (green – amber – red), to warn the user of an impending potential stuck pipe situation. The numerical models developed in this research estimate critical velocities to within 10 – 15% and show strong agreement with published empirical data. Combined with the cuttings transport numerical models developed in this research and other publicly available well design models (such as hydraulics, and torque and drag), the SPI tool has been tested with several case histories and proven to detect stuck pipe events with warning alerts significantly ahead of the event. The tool has equally been deployed in real-time with >90% success rate and without spurious alerts recorded. The results thus confirm that the developed numerical models and the "ROW" approach are robust, and offer an improvement to current industry capabilities in terms of accuracy and sensitivity to changing downhole wellbore conditions

Prediction of the aerodynamic behavior of a rounded corner square cylinder at zero incidence using ANN

AbstractThe aerodynamic behavior of a square cylinder with rounded corner edges in steady flow regime in the range of Reynolds number (Re) 5–45; is predicted by Artificial Neural Network (ANN) using MATLAB. The ANN has trained by back propagation algorithm. The ANN requires input and output data to train the network, which is obtained from the commercial Computational Fluid Dynamics (CFD) software FLUENT in the present study. In FLUENT, all the governing equations are discretized by the finite volume method. Results from numerical simulation and back propagation based ANN have been compared. It has been discovered that the ANN predicts the aerodynamic behavior correctly within the given range of the training data. It is additionally observed that back propagation based ANN is an effective tool to forecast the aerodynamic behavior than simulation, that has very much longer computational time

Neuromechanical Tuning for Arm Motor Control

Movement is a fundamental behavior that allows us to interact with the external world. Its importance to human health is most evident when it becomes impaired due to disease or injury. Physical and occupational rehabilitation remains the most common treatment for these types of disorders. Although therapeutic interventions may improve motor function, residual deficits are common for many pathologies, such as stroke. The development of novel therapeutics is dependent upon a better understanding of the underlying mechanisms that govern movement. Movement of the human body adheres to the principles of classic Newtonian mechanics. However, due to the inherent complexity of the body and the highly variable repertoire of environmental contexts in which it operates, the musculoskeletal system presents a challenging control problem and the onus is on the central nervous system to reliably solve this problem. The neural motor system is comprised of numerous efferent and afferent pathways with a hierarchical organization which create a complex arrangement of feedforward and feedback circuits. However, the strategy that the neural motor system employs to reliably control these complex mechanics is still unknown. This dissertation will investigate the neural control of mechanics employing a “bottom-up” approach. It is organized into three research chapters with an additional introductory chapter and a chapter addressing final conclusions. Chapter 1 provides a brief description of the anatomical and physiological principles of the human motor system and the challenges and strategies that may be employed to control it. Chapter 2 describes a computational study where we developed a musculoskeletal model of the upper limb to investigate the complex mechanical interactions due to muscle geometry. Muscle lengths and moment arms contribute to force and torque generation, but the inherent redundancy of these actuators create a high-dimensional control problem. By characterizing these relationships, we found mechanical coupling of muscle lengths which the nervous system could exploit. Chapter 3 describes a study of muscle spindle contribution to muscle coactivation using a computational model of primary afferent activity. We investigated whether these afferents could contribute to motoneuron recruitment during voluntary reaching tasks in humans and found that afferent activity was orthogonal to that of muscle activity. Chapter 4 describes a study of the role of the descending corticospinal tract in the compensation of limb dynamics during arm reaching movements. We found evidence that corticospinal excitability is modulated in proportion to muscle activity and that the coefficients of proportionality vary in the course of these movements. Finally, further questions and future directions for this work are discussed in the Chapter 5

Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction

Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users' natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far. Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment. Specifically, by learning from collected physiological data, we develop a bio-physically inspired computational model to predict neck MCL under diverse head kinematic states. Beyond quantifying the cumulative MCL of completed head movements, our model can also predict potential MCL requirements with target head poses only. A series of objective evaluations and user studies demonstrate its prediction accuracy and generality, as well as its ability in reducing users' neck discomfort by optimizing the layout of visual targets. We hope this research will motivate new ergonomic-centered designs for VR/AR and interactive graphics applications. Source code is released at: https://github.com/NYU-ICL/xr-ergonomics-neck-comfort.Comment: ACM SIGGRAPH 2023 Conference Proceeding