Modeling of fuel flow-rate of commercial aircraft for the descent flight using particle swarm optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ridvan Oruc ◽  
Tolga Baklacioglu
Keyword(s):  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Flow Rate ◽  
Particle Swarm ◽  
Management Decision ◽  
Rate Model ◽  
Fuel Flow Rate ◽  
Swarm Optimization ◽  
Content Type ◽  
Fuel Flow

Purpose The purpose of this paper is to create a new fuel flow rate model for the descent phase of the flight using particle swarm optimization (PSO). Design/methodology/approach A new fuel flow rate model was developed for the descent phase of the B737-800 aircraft, which is frequently used in commercial air transport using PSO method. For the analysis, the actual flight data records (FDRs) data containing the fuel flow rate, speed, altitude, engine speed, time and many more data were used. In this regard, an empirical formula has been created that gives real fuel flow rate values as a function of altitude and true airspeed. In addition, in the fuel flow rate predictions made for the descent phase of the specified aircraft, a different model has been created that can be used without any optimization process when FDR data are not available for a specific aircraft take-off weight condition. Findings The error analysis applied to the models showed that both models predict real fuel flow rate values with high precision. Practical implications Because of the high accuracy of the PSO model, it is thought to be useful in air traffic management, decision support systems, models used for trajectory prediction, aircraft performance models, strategies used to reduce fuel consumption and emissions because of fuel consumption. Originality/value This study is the first fuel flow rate model for descent flight using PSO algorithm. The use of real FDR data in the analysis shows the originality of this study.

Fuel flow rate modeling for descent using cuckoo search algorithm: a case study for point merge system procedure at Istanbul airport

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ridvan Oruc ◽  
Ozlem Sahin ◽  
Tolga Baklacioglu
Keyword(s):  
Fuel Consumption ◽  
Flow Rate ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Cuckoo Search Algorithm ◽  
Rate Model ◽  
Fuel Flow Rate ◽  
Content Type ◽  
Fuel Flow

Purpose The purpose of this paper is to create a new fuel flow rate model using cuckoo search algorithm (CSA) for the descending stage of the flight. Design/methodology/approach Using the actual flight data record data of the B737-800 aircraft, a new fuel flow rate model has been developed for this aircraft type. The created model is to predict the fuel flow rate with high accuracy depending on the altitude and true airspeed. In addition, the CSA fuel flow rate model was used to calculate the fuel consumption for the point merge system, which is used for combining the initial approach to the final approach at Istanbul Airport, the largest airport of Turkey. Findings As a result of the analysis, the correlation coefficient value is found as 0.996858 for Flight 1, 0.998548 for Flight 2, 0.995363 and 0.997351 for Flight 3 and Flight 4, respectively. The values that are so close to 1 indicate that the model predicts the real fuel flow rate data with high accuracy. Practical implications This model is considered to be useful in air traffic management decision support systems, aircraft performance models, models used for trajectory prediction and strategies used by the aviation community to reduce fuel consumption and related emissions. Originality/value The importance of this study lies in the fact that to the best of the authors’ knowledge, it is the first fuel flow rate model developed using CSA for the descent stage in the existing literature; the data set used is real values.

Comparative analysis of lunar capture braking method based on particle swarm optimization

2019 ◽  
Vol 91 (4) ◽  
pp. 558-566
Author(s):  
Chengchao Bai ◽  
Jifeng Guo ◽  
Wenyuan Zhang ◽  
Tianhang Liu ◽  
Linli Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Lunar Exploration ◽  
Swarm Optimization ◽  
Capture Process ◽  
Content Type ◽  
Advantages And Disadvantages

Purpose The purpose of this paper is to verify the feasibility of lunar capture braking through three methods based on particle swarm optimization (PSO) and compare the advantages and disadvantages of the three strategies by analyzing the results of the simulation. Design/methodology/approach The paper proposes three methods to verify capture braking based on PSO. The constraints of the method are the final lunar orbit eccentricity and the height of the final orbit around the Moon. At the same time, fuel consumption is used as a performance indicator. Then, the PSO algorithm is used to optimize the track of the capture process and simulate the entire capture braking process. Findings The three proposed braking strategies under the framework of PSO algorithm are very effective for solving the problem of lunar capture braking. The simulation results show that the orbit in the opposite direction of the trajectory has the most serious attenuation at perilune, and it should consume the least amount of fuel in theoretical analysis. The methods based on the fixed thrust direction braking and thrust uniform rotation braking can better ensure the final perilune control accuracy and fuel consumption. As for practice, the fixed thrust direction braking method is better realized among the three strategies. Research limitations/implications The process of lunar capture is a complicated process, involving effective coordination between multiple subsystems. In this article, the main focus is on the correctness of the algorithm, and a simplified dynamic model is adopted. At the same time, because the capture time is short, the lunar curvature can be omitted. Furthermore, to better compare the pros and cons of different braking modes, some influence factors and perturbative forces are not considered, such as the Earth’s flatness, light pressure and system noise and errors. Practical implications This paper presents three braking strategies that can satisfy all the constraints well and optimize the fuel consumption to make the lunar capture more effective. The results of comparative analysis demonstrate that the three strategies have their own superiority, and the fixed thrust direction braking is beneficial to engineering realization and has certain engineering practicability, which can also provide reference for lunar exploration orbit design. Originality/value The proposed capture braking strategies based on PSO enable effective capture of the lunar module. During the lunar exploration, the capture braking phase determines whether the mission will be successful or not, and it is essential to control fuel consumption on the premise of accuracy. The three methods in this paper can be used to provide a study reference for the optimization of lunar capture braking.

Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm

2020 ◽  
Vol 92 (3) ◽  
pp. 495-501
Author(s):  
Ridvan Oruc ◽  
Tolga Baklacioglu
Keyword(s):  
Flow Rate ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Cuckoo Search Algorithm ◽  
Rate Model ◽  
Fuel Flow Rate ◽  
Content Type ◽  
Flight Data ◽  
Fuel Flow

Purpose The purpose of this study is to create a new fuel flow rate model adopting cuckoo search algorithm (CSA) for the climbing phase of the flight. Design/methodology/approach Using the real flight data records (FDRs) of B737-800 passenger aircraft, a new fuel flow rate model for the climbing phase of the flight was developed by incorporating CSA. In the model, fuel flow rate is given as a function of altitude and true airspeed. The aim is to create a model that yields results that are closest to the real fuel flow rate values obtained from flight data records. Various error analysis methods were used to test the accuracy of the obtained values. Finally, the effect of change of some CSA parameters on the model was investigated. Findings It was observed that the derived model is able to predict real fuel flow rate values with high accuracy. It has been deduced that increasing the number of nest (n) and discovery rate of alien nests (pa) values of CSA parameters to a certain value gradually decreases the model’s accuracy. Practical implications This model is considered to be useful in air traffic management decision support systems, simulation applications, aircraft trajectory prediction models and aircraft performance modelling studies because of the high accuracy accomplished by the CSA model. Originality/value The originality of this study is the development of a new fuel flow rate model using CSA as a first attempt in the literature. The use of real flight data is important for the originality and reliability of the model.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

2019 ◽  
Vol 40 (2) ◽  
pp. 235-247
Author(s):  
Asma Ayari ◽  
Sadok Bouamama
Keyword(s):  
Particle Swarm Optimization ◽  
State Space ◽  
Task Allocation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Content Type ◽  
Automatic Clustering ◽  
Run Time ◽  
Robot Task ◽  
Multi Robot

Purpose The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO. Design/methodology/approach This approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid. Findings Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time. Practical implications The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks. Originality/value In this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

Optimizing parameters of support vector machines using team-search-based particle swarm optimization

2015 ◽  
Vol 32 (5) ◽  
pp. 1194-1213 ◽  
Author(s):  
Long Zhang ◽  
Jianhua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Search Strategy ◽  
Cross Validation ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type ◽  
Vector Machines

Purpose – It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors propose a novel variant of particle swarm optimization (PSO) for the selection of parameters in SVM. The proposed algorithm is denoted as PSO-TS (PSO algorithm with team-search strategy), which is with team-based local search strategy and dynamic inertia factor. The ultimate design purpose of the strategy is to realize that the algorithm can be suitable for different problems with good balance between exploration and exploitation and efficiently control the inertia of the flight. In PSO-TS, the particles accomplish the assigned tasks according to different topology and detailedly search the achieved and potential regions. The authors also theoretically analyze the behavior of PSO-TS and demonstrate they can share the different information from their neighbors to maintain diversity for efficient search. Findings – The validation of PSO-TS is conducted over a widely used benchmark functions and applied to tuning the parameters of SVM. The experimental results demonstrate that the proposed algorithm can tune the parameters of SVM efficiently. Originality/value – The developed method is original.

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