scholarly journals Multirotor Sizing Methodology with Flight Time Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Marcin Biczyski ◽  
Rabia Sehab ◽  
James F. Whidborne ◽  
Guillaume Krebs ◽  
Patrick Luk
Keyword(s):  
Time Estimation ◽  
Flight Time ◽  
Air Transport ◽  
Comparison Process ◽  
Input And Output ◽  
Multicriteria Optimisation ◽  
Operating Points ◽  
Automated Tool ◽  
Optimisation Algorithms

This paper addresses the need for sizing of rotors for multirotor vehicle applications such as personal air transport, delivery, surveillance, and photography. A methodology for the propeller and motor selection is developed and augmented with flight time estimation capabilities. Being multirotor-specific it makes use of the platform’s simplicity to rapidly provide a set of off-the-shelf components ready to be used in the vehicle. Use of operating points makes the comparison process fast, precise, and tailored to specific application. The method is easily implemented in software to provide an automated tool. Furthermore, clearly defined input and output parameters make it also usable as a module in other multicriteria optimisation algorithms. The new methodology is validated through comparison with a consumer-grade drone and the calculated results are compliant with manufacturer’s specification in terms of maximum hover time.

Data-Driven Method for the Prediction of Estimated Time of Arrival

2021 ◽  
pp. 036119812110332
Author(s):  
Xuhao Gui ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Chunwei Yang
Keyword(s):  
Pattern Classification ◽  
Trajectory Optimization ◽  
Time Estimation ◽  
Flight Time ◽  
Classification Model ◽  
Time Of Arrival ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Arrival Pattern ◽  
Multiple Stages

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

Multivariable Adaptive Control Using Only Input and Output Measurements for Turbojet Engines

1994 ◽  
Author(s):  
Jin-Quan Huang ◽  
Jian-Guo Sun
Keyword(s):  
Adaptive Control ◽  
Power Level ◽  
Simulation Studies ◽  
Aircraft Engines ◽  
Control Approach ◽  
Performance Requirements ◽  
Input And Output ◽  
Adaptation Law ◽  
Operating Points ◽  
Engine Power

Current and future aircraft engines are increasingly relying upon the use of multivariable control approach for meeting advanced performance requirements. A multivariable adaptive control (MRAC) scheme is proposed in this paper. The adaptation law is derived using only input and output (I/O) measurements. Simulation studies are performed for a two–spool turbojet engine. The satisfactory transient responses are obtained at different operating points from idle to maximum dry power within the flight envelope. These show insensitivity of the design to engine power level and flight condition. Simulation results also show high effectiveness of reducing interaction in multivariable systems with significant coupling. Using the multivariable MRAC controller, the engine acceleration time is reduced by about 19 percent in comparison with the conventional engine controller.

scholarly journals Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 925 ◽  
Author(s):  
Yeonseok Park ◽  
Anthony Choi ◽  
Keonwook Kim
Keyword(s):  
Sound Localization ◽  
Source Localization ◽  
Sound Source ◽  
Average Length ◽  
Time Estimation ◽  
Flight Time ◽  
Ensemble Average ◽  
Sound Source Localization ◽  
Localization System ◽  
Non Parametric

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.

Torque Phase Shift Control Based on Clutch Torque Estimation

2013 ◽  
Author(s):  
Diana Yanakiev ◽  
Yuji Fujii ◽  
Eric Tseng ◽  
Gregory M. Pietron ◽  
Joseph Kucharski ◽  
...  
Keyword(s):  
Closed Loop ◽  
Automatic Transmission ◽  
Time Estimation ◽  
Transfer Phase ◽  
Shift Method ◽  
Shift Control ◽  
Input And Output ◽  
Torque Estimation ◽  
Real Time Estimation ◽  
Clutch Torque

An automatic transmission shift method is presented, in which the torque transfer phase is controlled in closed loop. This is made possible by real-time estimation of the torque transmitted by the off-going and on-coming clutches participating in the shift. Each clutch torque is determined based on measured or estimated input and output shaft torques and accelerations. To illustrate an application of the method, traditional friction elements are used to emulate one-way-clutch function during a power-on upshift.

scholarly journals Flight Time Estimation for Continuous Surveillance Missions Using a Multirotor UAV

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 867 ◽  
Author(s):  
Sunghun Jung ◽  
Yonghyeon Jo ◽  
Young-Joon Kim
Keyword(s):  
Power Management ◽  
Time Estimation ◽  
Flight Time ◽  
State Of Charge ◽  
Soc Estimation ◽  
Complementary Filter ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Power Management System ◽  
Solar Powered

To achieve the continuous surveillance capable multirotor type solar-powered unmanned aerial vehicle (UAV), we develop the photovoltaic power management system (PPMS) which manages power from photovoltaic (PV) modules and a battery pack to support the power of the UAV. To estimate the possible flight time of the UAV, we use the concept of state of charge (SOC) estimation based on the extended Kalman filter (EKF) and complementary filter (CF) and then calculate the possible flight time by using the slope of the SOC graph during hovering flight mode. According to the results, estimated flight time increases up to 54.14 min at 11:00 a.m. and decreases down to 6.70 min at 18:00 p.m.

scholarly journals Automated Estimation of Time and Cost for Determining Optimal Machining Plans

2012 ◽  
Author(s):  
Benjamin Van Blarigan ◽  
Matthew I. Campbell ◽  
Ata A. Eftekharian ◽  
Tolga Kurtoglu
Keyword(s):  
Evaluation Model ◽  
Time Estimation ◽  
Computational Time ◽  
Selection Strategy ◽  
Tool Selection ◽  
Individual Values ◽  
Machining Time ◽  
Process Plan ◽  
Preliminary Results ◽  
Automated Tool

In any manufacturing setting, producing a machined part is a complicated, multi-step process. It requires coordination between an engineer and a machinist to ensure all goals are met. A machinist creates a part based generally on personal experience and intuition, and while they know that this will result in the finished product, it is not guaranteed that the process plan chosen is the quickest or least expensive way to make the part. In the past year, we have been developing an automated tool that analyzes a solid model to determine its best process plan. The tool is essentially comprised of a reasoning engine that determines what processes are valid for particular sections of the part, and an evaluation engine that estimates the time and cost of the candidate processes. This paper presents the implemented evaluation engine, which assigns individual values of time and cost to machine operations. The evaluation starts with an automated tool selection strategy. The engineering model is able to determine the machining time of the tool(s) chosen. The method presented here takes a unique approach to machining time estimation that balances the trade-off between accuracy and computational time. Preliminary results suggest that the method is able to achieve accuracy near that of commercial CAM packages, with a much lower computational expense. The evaluation model takes into account non-productive manufacturing times (e.g. fixturing, inspection), and translates these to related costs. The method will be presented and discussed in this paper along with some preliminary results.

scholarly journals Assembly Time Estimation: Assembly Mate Based Structural Complexity Metric Predictive Modeling

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Joseph E. Owensby ◽  
Joshua D. Summers
Keyword(s):  
Structural Complexity ◽  
Network Models ◽  
Time Estimation ◽  
Product Variety ◽  
Neural Network Models ◽  
Cad System ◽  
Predicted Values ◽  
Artificial Neural Network Models ◽  
And Training ◽  
Automated Tool

This paper presents an automated tool for estimating assembly times of products based on a three step process: connectivity graph generation from assembly mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool.

scholarly journals Increasing Sustainability of Logistic Networks by Reducing Product Losses: A Network DEA Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-21
Author(s):  
S. Lozano ◽  
B. Adenso-Díaz
Keyword(s):  
Data Envelopment Analysis ◽  
Experimental Design ◽  
Relative Efficiency ◽  
Loss Factor ◽  
Network Data ◽  
Perishable Products ◽  
Data Envelopment ◽  
Network Dea ◽  
Input And Output ◽  
Operating Points

This paper considers a multiproduct supply network, in which losses (e.g., spoilage of perishable products) can occur at either the nodes or the arcs. Using observed data, a Network Data Envelopment Analysis (NDEA) approach is proposed to assess the efficiency of the product flows in varying periods. Losses occur in each process as the observed output flows are lower than the observed input flows. The proposed NDEA model computes, within the NDEA technology, input and output targets for each process. The target operating points correspond to the minimum losses attainable using the best observed practice. The efficiency scores are computed comparing the observed losses with the minimum feasible losses. In addition to computing relative efficiency scores, an overall loss factor for each product and each node and link can be determined, both for the observed data and for the computed targets. A detailed illustration and an experimental design are used to study and validate the proposed approach. The results indicate that the proposed approach can identify and remove the inefficiencies in the observed data and that the potential spoilage reduction increases with the variability in the losses observed in the different periods.

Multivariable Adaptive Control Using Only Input and Output Measurements for Turbojet Engines

1995 ◽  
Vol 117 (2) ◽  
pp. 314-319 ◽  
Author(s):  
Jin-Quan Huang ◽  
Jian-Guo Sun
Keyword(s):  
Adaptive Control ◽  
Power Level ◽  
Simulation Studies ◽  
Control Approach ◽  
Performance Requirements ◽  
Input And Output ◽  
Adaptation Law ◽  
Model Reference Adaptive ◽  
Operating Points ◽  
Engine Power

Current and future aircraft engines are increasingly relying upon the use of multivariable control approach for meeting advanced performance requirements. A multivariable model reference adaptive control (MRAC) scheme is proposed in this paper. The adaptation law is derived using only input and output (I/O) measurements. Simulation studies are performed for a two-spool turbojet engine. The satisfactory transient responses are obtained at different operating points from idle to maximum dry power within the flight envelope. These show insensitivity of the design to engine power level and flight condition. Simulation results also show high effectiveness of reducing interaction in multivariable systems with significant coupling. Using the multivariable MRAC controller, the engine acceleration time is reduced by about 19 percent in comparison with the conventional engine controller.

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