1,550 research outputs found
MAGEC_2024
No full textMAGEC (Magma And Gas Equilibrium Calculator)
Author: Chenguang Sun
Copyright, 2025
This Matlab program calculates the equilibrium distribution of C-H-O-S volatiles (H2, H2O, CO, CO2, CH4, H2S, SO2, S2, COS, O2) in gas-melt coupled systems.
Updates on April 27, 2025:
1. Added additional options for CO2 and H2O solubility models
2. Corrected CO solubility models
If you use this program for your publications, please cite the following references:
1. Sun, C. and Yao, L., 2024. Redox equilibria of iron in low-to high-silica melts: A simple model and its applications to CHOS degassing. Earth and Planetary Science Letters, 638, p.118742. [Note: This is the reference for MAGEC_2024 with new Fe- and S-redox models and flexible setting options.]
2. Sun, C. and Lee, C.T.A., 2022. Redox evolution of crystallizing magmas with CHOS volatiles and its implications for atmospheric oxygenation. Geochimica et Cosmochimica Acta, 338, pp.302-321. [Note: This is the reference for the first version of MAGEC.]
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How to run the program:
1. Input T/P/logfO2 (or Fe3+/FeT) and compositions in the input file (.xlsx).
Make sure you use different names for the input and output files.
The batch data could be polybaric/isobaric/adiabatic degassing.
2. Modify the "settings" in the input file.
3. Open [Run_MAGEC_2024_v2.m] in Matlab
4. Go to [Editor] Tab in Matlab and click [Run] button.
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Impeller Design and Performance Analysis of Aviation Fuel Pump Based on the Inverse Method
Full text linkCentrifugal pumps have a wide range of applications in the aviation field. The present work focuses on the optimal design of aviation fuel pump impellers by means of an inverse method. The fuel pump impeller is designed here by solving an inverse problem, in which the impeller geometry is found by imposing a target blade loading. As the inverse procedure is inviscid, an iterative process based on RANS is then applied to finally converge to a fully viscous solution. Three representative loading distributions have been investigated, and the final performances are evaluated by RANS computations. Since flow variables, rather than the blade geometry, are imposed on the target flow field, it is found that the impellers designed by way of the inverse method have high efficiency under the conditions without cavitation; among them, the pump impeller with a higher loading at the hub maintains a high efficiency for a wide range of flow conditions and also has better anti-cavitation performances under low inlet pressure conditions. Moreover, cavitation resistance can be improved by adjusting the loading distribution near the blade leading edge using the inverse design tool
Development of a robotic teaching interface for human to human skill transfer
No full textThe tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutor's motor functionalities into the robot's control architecture. For instance, for hand-in-hand supervision of the writing task, the tutor's corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques
Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery
Full text linkAn improved human-robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot. Redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a compliant behavior for the medical staff. Based on the implemented hierarchical control framework, an RCM constraint and a safe constraint are applied to the nullspace motion to achieve the surgical tasks with human-robot interaction. Due to the physical interactions, safety and accuracy of the surgery may be affected. The control framework integrates an adaptive compensator to enhance the accuracy of the surgical tip and to maintain the RCM constraint in a decoupled way avoiding any physical interactions. The system performance is verified on a patient phantom. Compared with the methods proposed in the literature, results show that the accuracy of both the RCM constraint and the surgical tip is improved. The compliant swivel motion of the robot arm is also constrained in a defined area, and the interaction force on the abdominal wall becomes smaller
Neural Network Enhanced Robot Tool Identification and Calibration for Bilateral Teleoperation
Full text linkIn teleoperated surgery, the transmission of force feedback from the remote environment to the surgeon at the local site requires the availability of reliable force information in the system. In general, a force sensor is mounted between the slave end-effector and the tool for measuring the interaction forces generated at the remote sites. Such as the acquired force value includes not only the interaction force but also the tool gravity. This paper presents a neural network (NN) enhanced robot tool identification and calibration for bilateral teleoperation. The goal of this experimental study is to implement and validate two different techniques for tool gravity identification using Curve Fitting (CF) and Artificial Neural Networks (ANNs), separately. After tool identification, calibration of multi-axis force sensor based on Singular Value Decomposition (SVD) approach is introduced for alignment of the forces acquired from the force sensor and acquired from the robot. Finally, a bilateral teleoperation experiment is demonstrated using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrated that the calibration of the force sensor after identifying tool gravity component by using ANN shows promising performance than using CF. Additionally, the transparency of the system was demonstrated using the force and position tracking between the master and slave manipulators
An overview of human-robot collaboration in smart manufacturing
Full text linkIndustry 4.0, characterized as smart manufacturing, has revolutionized the industrial world with cutting-edge technologies such as collaborative robots and artificial intelligence etc. Productivity and efficiency are two key factors that determine the success level of manufacturing. Many manufacturers are eager to adopt adaptive, intuitive, collaborative and smart techniques to improve the production lines, including key manufacturing machines and other equipment. In this scenario, robotic systems are playing an increasingly vital role to decrease the need for human labour and increase automation level. Material waste is also decreasing as employing robots could provide both stability and accuracy during work. Currently, great research efforts are growing to respond to market size changes and customization processes. Researchers are focusing on enhancing the interactions between humans and robots in the work environment to exploit the benefits of human experience and the capabilities of robotic systems at the same time. This paper presents an overview of Human-Robot Collaboration (HRC) systems that are being employed in smart manufacturing to address the need for collaboration interactions between humans and robot. The research gaps, challenges and future work directions on HRC are highlighted and analysed towards smart manufacturing
Deep Neural Network Approach in Human-like Redundancy Optimization for Anthropomorphic Manipulators
Full text linkHuman-like behavior has emerged in the robotics area for improving the quality of HumanRobot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering
Asymmetric Bimanual Control of Dual-Arm Exoskeletons for Human-Cooperative Manipulations
Full text linkIn this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator’s motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject’s movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject’s muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking
Teaching by demonstration on dual-arm robot using variable stiffness transferring
No full textTeaching by demonstration (TbD) techniques have been extensively investigated in the recent decades to enable transferring various task skills from human to robots. The traditional TbD techniques focus on teaching motion trajectories that may be sufficient for routine tasks with fixed objects. While for interactive tasks in contact with dynamic environment and objects, e.g., the payload of a robot manipulator may change from one to another, teaching robot only by motion demonstration may cause undesired contact force and inefficiency in the task execution. In this paper, we present a novel TbD method enhanced by transferring the stiffness profile during human robot interaction (HRI). The method is developed on a bimanual robot, whereas one slave arm plays the role of the tutee, and the other master arm coupled with human demonstrator plays the role of tutor. A rendering algorithm is employed to provide demonstrator with force feedback via a purposely built coupling device according to the motion disparity between the two arms. The muscle surface electromyography (sEMG) signals collected during HRI is processed to extract the demonstrator's variable stiffness as well as hand grasping patterns. Comparative tests have been carried out on a bimanual Baxter robot for a lifting task with three different set-ups: i) TbD with predefined fixed stiffness; ii) TbD with demonstrator transferred variable stiffness without force feedback; and iii) TbD with demonstrator transferred variable stiffness with force feedback. Results show that the proposed TbD method performs best by transferring the demonstrator's physical interactive skill to the robot in a natural and efficient manner
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