scholarly journals Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 167355-167370
Author(s):  
Ivan Lopez-Sanchez ◽  
Jorge Montoya-Chairez ◽  
Ricardo Perez-Alcocer ◽  
Javier Moreno-Valenzuela
Keyword(s):  
Experimental Parameter ◽  
Parameter Identifications

Production and STEM examination of controlled-size silver clusters

1983 ◽  
Vol 41 ◽  
pp. 338-339
Author(s):  
M. A. Listvan ◽  
R. P. Andres
Keyword(s):  
Cluster Size ◽  
Metal Clusters ◽  
Experimental Parameter ◽  
Carbon Films ◽  
Metal Species ◽  
Silver Clusters ◽  
Free Jet ◽  
Size Dependent ◽  
Cluster Properties ◽  
Controllable Size

Knowledge of the function and structure of small metal clusters is one goal of research in catalysis. One important experimental parameter is cluster size. Ideally, one would like to produce metal clusters of regulated size in order to characterize size-dependent cluster properties.A source has been developed which is capable of producing microscopic metal clusters of controllable size (in the range 5-500 atoms) This source, the Multiple Expansion Cluster Source, with a Free Jet Deceleration Filter (MECS/FJDF) operates as follows. The bulk metal is heated in an oven to give controlled concentrations of monomer and dimer which were expanded sonically. These metal species were quenched and condensed in He and filtered to produce areosol particles of a controlled size as verified by mass spectrometer measurements. The clusters were caught on pre-mounted, clean carbon films. The grids were then transferred in air for microscopic examination. MECS/FJDF was used to produce two different sizes of silver clusters for this study: nominally Ag6 and Ag50.

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

scholarly journals Thermo-mechanical forging of 708M40 steel ring samples: experiments and modelling

2021 ◽  
Author(s):  
Steven Hill ◽  
Richard P.Turner
Keyword(s):  
Finite Element ◽  
Experimental Parameter ◽  
Compression Tests ◽  
Molybdenum Disulphide ◽  
Heating Unit ◽  
Modelling Framework ◽  
Process Understanding ◽  
Ring Compression ◽  
Water Based ◽  
Temperature Variable

AbstractA series of ring compression tests using BS970:708M40 alloy steel samples were studied. These tests were conducted using a 2-factor soak-temperature variable, namely 1030 °C and 1300 °C, and a 4-factor lubricant variable consisting of unlubricated samples, synthetic water-based, graphite water-based, and graphite and molybdenum disulphide viscous grease. The lubricant agents were all applied to the tool/billet interface. Process variables such as blow force and heating were controlled with the use of a gravitationally operated drop hammer and an automated programmable induction-heating unit. This matrix of the experimental parameters offered a sound base for exploring dominant factors impacting upon bulk deformation. This deformation was measured using fully calibrated equipment and then systematically recorded. A finite element modelling framework was developed to further improve the thermo-mechanical deformation process understanding, with finite element (FE) predictions validated through experimental measurement. Through the combined experimental and FE work, it was shown that temperature variation in the experimental parameter matrix played a larger role in determining deformation than the lubrication agent. Additionally, the use of synthetic and graphite water-based lubricants does not necessarily produce greater deformation when used in high-temperature forgings due to the lubricants breaking down, evaporating, or inducing rapid billet cooling as a result of the carrier used (water). Graphite-molybdenum disulphate grease far outperforms the other lubricants used in this trial in reducing friction and allowing deformation to occur across a die-face.

scholarly journals Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi
Keyword(s):  
Lithium Ion Battery ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Drive Cycle ◽  
Cell Dynamics ◽  
Battery Cell ◽  
Parameter Identifications

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

Experimental Parameter Study on Synthesis Gas Production by Steam-Oxygen Fluidized Bed Gasification of Sewage Sludge

2021 ◽  
Vol 11 (2) ◽  
pp. 579
Author(s):  
Max Schmid ◽  
Selina Hafner ◽  
Günter Scheffknecht
Keyword(s):  
Sewage Sludge ◽  
Fluidized Bed ◽  
Experimental Parameter ◽  
Space Velocity ◽  
Phosphate Fertilizer ◽  
Gas Production ◽  
Raw Material ◽  
Parameter Study ◽  
Syngas Production ◽  
Fuels And Chemicals

The conversion of biogenic residues to fuels and chemicals via gasification and synthesis processes is a promising pathway to replace fossil carbon. In this study, the focus is set on sewage sludge gasification for syngas production. Experiments were carried out in a 20 kW fuel input bubbling fluidized bed facility with steam and oxygen as gasification agent. In-situ produced sewage sludge ash was used as bed material. The sensitivity of the key operation parameters gasifier temperature, oxygen ratio, steam to carbon ratio, and the space velocity on the syngas composition (H2, CO, CO2, CH4, CxHy, H2S, COS, NH3, and tars) was determined. The results show that the produced syngas has high H2 and CO concentrations of up to 0.37 m3 m−3 and 0.18 m3 m−3, respectively, and is thus suitable for synthesis of fuels and chemicals. By adjusting the steam to carbon ratio, the syngas’ H2 to CO ratio can be purposely tailored by the water gas shift reaction for various synthesis products, e.g., synthetic natural gas (H2/CO = 3) or Fischer–Tropsch products (H2/CO = 2). Also, the composition and yields of fly ash and bed ash are presented. Through the gasification process, the cadmium and mercury contents of the bed ash were drastically reduced. The ash is suitable as secondary raw material for phosphorous or phosphate fertilizer production. Overall, a broad database was generated that can be used for process simulation and process design.

scholarly journals A Mixed Numerical-Experimental Method to Characterize Metal-Polymer Interfaces for Crash Applications

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 818
Author(s):  
Jonas Richter ◽  
Moritz Kuhtz ◽  
Andreas Hornig ◽  
Mohamed Harhash ◽  
Heinz Palkowski ◽  
...  
Keyword(s):  
Experimental Parameter ◽  
Dissimilar Materials ◽  
Calibration Method ◽  
Parameter Determination ◽  
Failure Behavior ◽  
Cohesive Elements ◽  
Interface Failure ◽  
Wide Range ◽  
Polymer Metal ◽  
Metal Polymer

Metallic (M) and polymer (P) materials as layered hybrid metal-polymer-metal (MPM) sandwiches offer a wide range of applications by combining the advantages of both material classes. The interfaces between the materials have a considerable impact on the resulting mechanical properties of the composite and its structural performance. Besides the fact that the experimental methods to determine the properties of the single constituents are well established, the characterization of interface failure behavior between dissimilar materials is very challenging. In this study, a mixed numerical–experimental approach for the determination of the mode I energy release rate is investigated. Using the example of an interface between a steel (St) and a thermoplastic polyolefin (PP/PE), the process of specimen development, experimental parameter determination, and numerical calibration is presented. A modified design of the Double Cantilever Beam (DCB) is utilized to characterize the interlaminar properties and a tailored experimental setup is presented. For this, an inverse calibration method is used by employing numerical studies using cohesive elements and the explicit solver of LS-DYNA based on the force-displacement and crack propagation results.

Effects of Cutting Parameters on Residual Stresses in High-Speed Milling of Ti-17

2013 ◽  
Vol 834-836 ◽  
pp. 861-865 ◽  
Author(s):  
Yong Shou Liang ◽  
Jun Xue Ren ◽  
Yuan Feng Luo ◽  
Ding Hua Zhang
Keyword(s):  
Experimental Study ◽  
Residual Stresses ◽  
High Speed ◽  
Experimental Parameter ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Single Factor ◽  
Cutting Depth ◽  
High Speed Milling ◽  
Parameter Ranges

An experimental study was conducted to determine cutting parameters of high-speed milling of Ti-17 according to their effects on residual stresses. First, three groups of single factor experiments were carried out to reveal the effects of cutting parameters on residual stresses. Then sensitivity models were established to evaluate the influence degrees of cutting parameters on residual stresses. After that, three criteria were proposed to determine cutting parameters from experimental parameter ranges. In the experiments, the cutting parameter ranges are recommended as [371.8, 406.8] m/min, [0.363, 0.412] mm and [0, 0.018] mm/z for cutting speed, cutting depth and feed per tooth, respectively.

Comparison of Four Friction Models: Feature Prediction

2015 ◽  
Vol 11 (3) ◽  
Author(s):  
Yun-Hsiang Sun ◽  
Tao Chen ◽  
Christine Qiong Wu ◽  
Cyrus Shafai
Keyword(s):  
Numerical Model ◽  
Viscous Friction ◽  
Friction Model ◽  
Dynamic Features ◽  
Numerical Predictions ◽  
Wide Range ◽  
Model Predictions ◽  
Friction Models ◽  
Parameter Identifications ◽  
Model Structures

In this paper, we provide not only key knowledge for friction model selection among candidate models but also experimental friction features compared with numerical predictions reproduced by the candidate models. A motor-driven one-dimensional sliding block has been designed and fabricated in our lab to carry out a wide range of control tasks for the friction feature demonstrations and the parameter identifications of the candidate models. Besides the well-known static features such as break-away force and viscous friction, our setup experimentally demonstrates subtle dynamic features that characterize the physical behavior. The candidate models coupled with correct parameters experimentally obtained from our setup are taken to simulate the features of interest. The first part of this work briefly introduces the candidate friction models, the friction features of interest, and our experimental approach. The second part of this work is dedicated to the comparisons between the experimental features and the numerical model predictions. The discrepancies between the experimental features and the numerical model predictions help researchers to judge the accuracy of the models. The relation between the candidate model structures and their numerical friction feature predictions is investigated and discussed. A table that summarizes how to select the most optimal friction model among a variety of engineering applications is presented at the end of this paper. Such comprehensive comparisons have not been reported in previous literature.

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