A New Approach for Identification of Forces on Slender Beams Subjected to Vortex Induced Vibrations

2008 ◽  
Author(s):  
Jie Wu ◽  
Carl M. Larsen ◽  
Karl E. Kaasen
Keyword(s):  
Force Measurement ◽  
Estimation Method ◽  
Hydrodynamic Forces ◽  
Recursive Least Squares ◽  
Beam Model ◽  
Force Estimation ◽  
Vortex Induced Vibrations ◽  
Slender Beams ◽  
Space Formulation ◽  
Inverse Force

Vortex induced vibration (VIV) has been subjected to extensive research during the last 20 years. A large number of laboratory and ocean tests with long slender beams or cables have been reported. Key results from such experiments have been response frequencies and amplitudes, but also information on mode composition and traveling waves. Due to the difficulty of direct force measurement, accelerometer and bending strain measurement are used in such experiments. Formally, it should be possible to identify the forces that have created the measured response, but so far few results from such attempts have been reported. An inverse force estimation method is adopted to provide an accurate way of reconstructing the unknown hydrodynamic forces from measured dynamic response data. The method is based on state space formulation of a finite element beam model. It incorporates the Kalman filtering and recursive least squares algorithm to remove the noise from measurement and obtain force estimation in discrete time domain. The inverse force estimation method is verified with numerical simulations. The input force of a tensioned beam structure is estimated from response. The result indicates its capability to accurately estimate the input forces from stochastic response. The method is applied to the data from Rotating Rig Test to identify hydrodynamic forces along the riser. The lift force and added mass coefficients are calculated and compared with existing data.

scholarly journals Position Control and Force Estimation Method for Surgical Forceps Using SMA Actuators and Sensors

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5111
Author(s):  
Dennis Braun ◽  
David Weik ◽  
Sophia Elsner ◽  
Sandra Hunger ◽  
Michael Werner ◽  
...  
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Force Feedback ◽  
Haptic Feedback ◽  
Position Control ◽  
Force Measurement ◽  
Functional Model ◽  
Estimation Method ◽  
Force Estimation ◽  
Mechanical Coupling

Minimally invasive surgery is increasingly used in many medical operations because of the benefits for the patients. However, for the surgeons, accessing the situs through a small incision or natural orifice comes with a reduction of the degrees of freedom of the instrument. Due to friction of the mechanical coupling, the haptic feedback lacks sensitivity that could lead to damage of the tissue. The approach of this work to overcome these problems is to develop a control concept for position control and force estimation with shape memory alloys (SMA) which could offer haptic feedback in a novel handheld instrument. The concept aims to bridge the gap between manually actuated laparoscopic instruments and surgical robots. Nickel-titanium shape memory alloys are used for actuation because of their high specific energy density. The work includes the manufacturing of a functional model as a proof of concept comprising the development of a suitable forceps mechanism and electronic circuit for position control and gripping force measurement, as well as designing an ergonomic user interface with haptic force feedback.

Aerodynamic parameter measurement using the wind driven manipulator - Inverse force measurement on wings

1997 ◽  
Author(s):  
Richard Ames ◽  
Richard Ames ◽  
N. Komerath ◽  
J. Magill ◽  
N. Komerath ◽  
...  
Keyword(s):  
Force Measurement ◽  
Parameter Measurement ◽  
Aerodynamic Parameter ◽  
Inverse Force

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

scholarly journals Verification of the Design Force Estimation Method for the Steel-type Breakwater in the Real Sea

2012 ◽  
Vol 12 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Hyuck Min Kweon ◽  
Oh Kyun Kwon ◽  
Yu Sik Han ◽  
Kang Hun Yoon
Keyword(s):  
Estimation Method ◽  
Force Estimation ◽  
The Real ◽  
Steel Type

Estimation of Hydrodynamic Coefficients for VIV of Slender Beam at High Mode Orders

2010 ◽  
Author(s):  
Jie Wu ◽  
Carl M. Larsen ◽  
Halvor Lie
Keyword(s):  
Modal Analysis ◽  
Traveling Waves ◽  
Flow Direction ◽  
Research Effort ◽  
Cross Flow ◽  
Bending Moment ◽  
Hydrodynamic Forces ◽  
High Mode ◽  
Excitation Region ◽  
Inverse Force

The Hano̸ytangen test program was caried out by MARINTEK for Norsk Hydro in 1997. One purpose of this research effort was to investigate VIV response of deep sea risers subjected to sheared current. A densely instrumented 90 meter long riser model was tested in shear current, and bending strains along the riser was measured. Oscillatory part of both in-line (IL) and cross-flow (CF) displacements can be obtained by applying modal analysis on the bending moment measurements. The primary results from the analysis are that the riser is vibrating at high modes in cross-flow direction (10th–30th mode). The response is dominated standing waves for the lowest speed cases and gradually is influenced by traveling waves for increasing speed. For highest speed cases, it is dominated by traveling waves. The vibration amplitude is significantly smaller than for a rigid cylinder under equivalent conditions. Inverse force analysis estimates hydrodynamic forces from measured response of a slender beam. The method has previously been applied to rotating rig test data. The response was for these cases dominated by relatively low mode orders and standing wave responses. To understand the stochastic behaviour of high mode VIV response, the method is applied to Hano̸ytangen test in the present study to provide valuable insights by estimating CF hydrodynamic forces and coefficients from displacement time series found from modal analysis of measured strains. The results from this work are presented in terms of CF hydrodynamic force coefficients, excitation region and their variations in time and space. New excitation database is extracted based on the analysis results. They are used in VIVANA to predict the displacement and stress against experiment results.

Inverse Force Estimation for Resonant Shock Plate Application

2018 ◽  
pp. 381-392
Author(s):  
William Larsen ◽  
Jason R. Blough ◽  
James DeClerck ◽  
Charles VanKarsen ◽  
David Soine ◽  
...  
Keyword(s):  
Force Estimation ◽  
Inverse Force

scholarly journals Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4772 ◽  
Author(s):  
Kaizhi Liang ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Zhenpo Wang ◽  
Shangfeng Jiang
Keyword(s):  
Electric Vehicles ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Gradient Boosting ◽  
Estimation Model ◽  
Ohmic Resistance ◽  
Extreme Gradient Boosting ◽  
Battery Packs ◽  
Mean Square Errors

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

Effect of Load-Dependent Friction on the Estimation of Rack Force in Electric Power-Assisted Steering System

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Yijun Li ◽  
Taehyun Shim ◽  
Dexin Wang ◽  
Timothy Offerle
Keyword(s):  
Electric Power ◽  
A Priori ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Steering System ◽  
Estimation Methods ◽  
Current Steering ◽  
Force Estimation ◽  
Operation Conditions ◽  
Friction Estimation

The rack force is valuable information for a vehicle dynamics control system, as it relates closely to the road conditions and steering feel. Since there is no direct measurement of rack force in current steering systems, various rack force estimation methods have been proposed to obtain the rack force information. In order to get an accurate rack force estimate, it is important to have knowledge of the steering system friction. However, it is hard to have an accurate value of friction, as it is subject to variation due to operation conditions and material wear. Especially for the widely used column-assisted electric power steering (C-EPAS) system, the load-dependent characteristic of its worm gear friction has a significant effect on rack force estimation. In this paper, a rack force estimation method using a Kalman filter and a load-dependent friction estimation algorithm is introduced, and the effect of C-EPAS friction on rack force estimator performance is investigated. Unlike other rack force estimation methods, which assume that friction is known a priori, the proposed system uses a load-dependent friction estimation algorithm to determine accurate friction information in the steering system, and then a rack force is estimated using the relationship between steering torque and angle. The effectiveness of this proposed method is verified by carsim/simulink cosimulation.

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