68 research outputs found
Multiphase flow measurement and data analytic based on multi-modal sensors
Accurate multiphase flow measurement is crucial in the energy industry. Over
the past decades, separation of the multiphase flow into single-phase flows has
been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase
flows remain a long-standing challenge. To tackle the challenge, this thesis first
explores the feasibility of performing time-difference and frequency-difference imaging
of multiphase flows with complex-valued electrical capacitance tomography (CVECT).
The multiple measurement vector (MMV) model-based CVECT imaging algorithm
is proposed to reconstruct conductivity and permittivity distribution simultaneously,
and the alternating direction method of multipliers (ADMM) is applied to solve
the multi-frequency image reconstruction problem. The proposed multiphase flow
imaging approach is verified and benchmarked with widely adopted tomographic
image reconstruction algorithms. Another focus of this thesis is multiphase flowrate
estimation based on low-cost, multi-modal sensors. Machine learning (ML) has
recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods,
i.e., deep neural network (DNN), support vector machine (SVM), and convolutional
neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows
based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network
(TCN) model is introduced to analyse the collected time series sensing data from the
Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining
the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built
to predict the multiphase flowrate with various data pre-processing methods. The
results provide guidance on data pre-processing methods for multiphase flowrate
measurement and suggest that the proposed combination of low-cost flow sensing
techniques and machine learning can effectively translate the time series sensing
data to achieve satisfactory flowrate measurement under various flow conditions
Multiple measurement vector based image reconstruction for multifrequency impedance imaging using capacitive sensor
AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation
The robotics community is increasingly interested in autonomous aerial
transportation. Unmanned aerial vehicles with suspended payloads have
advantages over other systems, including mechanical simplicity and agility, but
pose great challenges in planning and control. To realize fully autonomous
aerial transportation, this paper presents a systematic solution to address
these difficulties. First, we present a real-time planning method that
generates smooth trajectories considering the time-varying shape and non-linear
dynamics of the system, ensuring whole-body safety and dynamic feasibility.
Additionally, an adaptive NMPC with a hierarchical disturbance compensation
strategy is designed to overcome unknown external perturbations and inaccurate
model parameters. Extensive experiments show that our method is capable of
generating high-quality trajectories online, even in highly constrained
environments, and tracking aggressive flight trajectories accurately, even
under significant uncertainty. We plan to release our code to benefit the
community.Comment: Accepted by IEEE Robotics and Automation Letter
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