On the Motion Characteristics of Tripode Joints. Part 2: Applications

1984 ◽  
Vol 106 (2) ◽  
pp. 235-241 ◽  
Author(s):  
T. W. Lee ◽  
E. Akbil
Keyword(s):  
Constant Velocity ◽  
Front Wheel ◽  
Exact Constant ◽  
Phase Lag ◽  
Wheel Drive ◽  
Crank Angle ◽  
Motion Characteristics ◽  
Insight Into ◽  
Fourth Order Polynomial

The theory developed in Part 1 has been applied to the determination of motion characteristics of two typical types of shaft couplings involving tripode joints. An ideal case of an exact constant-velocity coupling is first described and analyzed. The remainder of the paper focuses on a practical shaft coupling of the tripode-Rzeppa combination which is widely used in front-wheel drive vehicles. The results demonstrate that the displacement equation of this combination is a fourth-order polynomial in the tangent of the output crank angle, and this coupling does not provide a true constant-velocity joint, but rather an approximate one. Parameters affecting the velocity fluctuation and their influences are presented. In addition, it is found that there exists a definite phase lag of thirty degrees between the displacement and velocity curves at all times on the output shaft. Whenever possible, numerical results for the motion characteristics of the joint and its components are illustrated by computer-plotted graphs, which may also offer some insight into the behavior of the tripode joint.

Regular perturbations to the motion of a three-wheeled mobile robot with the front-wheel drive under restricted state variables*

2020 ◽  
Author(s):  
Roman Chertovskih ◽  
Anna Daryina ◽  
Askhat Diveev ◽  
Dmitry Karamzin ◽  
Fernando L. Pereira ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Front Wheel ◽  
Wheeled Mobile Robot ◽  
State Variables ◽  
Wheel Drive

scholarly journals Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

2021 ◽  
Author(s):  
Hao Zhang ◽  
Zongxia Jiao ◽  
Yaoxing Shang ◽  
Xiaochao Liu ◽  
Pengyuan Qi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Front Wheel ◽  
Wheel Drive

An insight into the determination of size and number concentration of silver nanoparticles in blood using single particle ICP-MS (spICP-MS): Feasibility of application to samples relevant to in vivo toxicology studies

2021 ◽  
Author(s):  
Isabel Abad-Álvaro ◽  
Diego Leite ◽  
Dorota Bartczak ◽  
Susana Cuello ◽  
Beatriz Gomez-Gomez ◽  
...  
Keyword(s):  
Silver Nanoparticles ◽  
Single Particle ◽  
Number Concentration ◽  
Biological Matrices ◽  
Icp Ms ◽  
Toxicological Studies ◽  
Insight Into

Toxicological studies concerning nanomaterials in complex biological matrices usually require a carefully designed workflow that involves handling, transportation and preparation of a large number of samples without affecting the nanoparticle...

scholarly journals Insight into Glutamatergic Involvement in Rewarding Effects of Mephedrone in Rats: In Vivo and Ex Vivo Study

2021 ◽  
Author(s):  
Olga Wronikowska ◽  
Maria Zykubek ◽  
Agnieszka Michalak ◽  
Anna Pankowska ◽  
Paulina Kozioł ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Interdisciplinary Approach ◽  
Nmda Antagonist ◽  
Exchange Chromatography ◽  
Resonance Spectroscopy ◽  
Monoamine Transporters ◽  
Ex Vivo Study ◽  
Insight Into

AbstractMephedrone is a widely used drug of abuse, exerting its effects by interacting with monoamine transporters. Although this mechanism has been widely studied heretofore, little is known about the involvement of glutamatergic transmission in mephedrone effects. In this study, we comprehensively evaluated glutamatergic involvement in rewarding effects of mephedrone using an interdisciplinary approach including (1) behavioural study on effects of memantine (non-selective NMDA antagonist) on expression of mephedrone-induced conditioned place preference (CPP) in rats; (2) evaluation of glutamate concentrations in the hippocampus of rats following 6 days of mephedrone administration, using in vivo magnetic resonance spectroscopy (MRS); and (3) determination of glutamate levels in the hippocampus of rats treated with mephedrone and subjected to MRS, using ion-exchange chromatography. In the presented research, we confirmed priorly reported mephedrone-induced rewarding effects in the CPP paradigm and showed that memantine (5 mg/kg) was able to reverse the expression of this effect. MRS study showed that subchronic mephedrone administration increased glutamate level in the hippocampus when measured in vivo 24 h (5 mg/kg, 10 mg/kg and 20 mg/kg) and 2 weeks (5 mg/kg and 20 mg/kg) after last injection. Ex vivo chromatographic analysis did not show significant changes in hippocampal glutamate concentrations; however, it showed similar results as obtained in the MRS study proving its validity. Taken together, the presented study provides new insight into glutamatergic involvement in rewarding properties of mephedrone.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Systematic Approach to Measuring ILI Tool Performance

2014 ◽  
Author(s):  
Sean Keane ◽  
Karmun Cheng ◽  
Kaitlyn Korol
Keyword(s):  
Performance Measurement ◽  
Performance Measures ◽  
New Technologies ◽  
Efficient Reduction ◽  
Tool Performance ◽  
Typical Performance ◽  
Substantial Investment ◽  
Integrity Management ◽  
Insight Into

In-line inspection (ILI) tools play an important role within integrity management and substantial investment is made to continuously advance performance of the existing technologies and, where necessary, to develop new technologies. Performance measurement is typically focused for the purpose of understanding the measured performance in relation to the ILI vendor specification and for the determination of residual uncertainty regarding pipeline integrity. These performance measures may not provide the necessary insight into what type of investment into a technology is necessary to further reduce residual uncertainty regarding pipeline integrity, and beyond that, what investment, as an operator, results in an effective and efficient reduction in uncertainty. The paper proposes a reliability based approach for investigating uncertainty associated with ultrasonic crack ILI technology for the purpose of identifying efficient investment into the technology that results in an effective and measurable improvement. Typical performance measures and novel performance measurement methods are presented and reviewed with respect to what information they can provide to assist in investment decisions. Finally, general observations are made regarding Enbridge’s experience using ultrasonic crack ILI technology and areas currently being investigated.

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