Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation

2016 ◽  
Vol 28 (2) ◽  
pp. 215-225
Author(s):  
Norazila Othman ◽  
◽  
Masahiro Kanazaki
Keyword(s):  
Surrogate Model ◽  
Mean Squared Error ◽  
Simulation Analysis ◽  
Prediction Method ◽  
Solution Space ◽  
Latin Hypercube Sampling ◽  
Flight Simulation ◽  
Prediction Ability ◽  
Aerodynamic Derivatives ◽  
Exact Function

[abstFig src='/00280002/12.jpg' width=""300"" text='3D contour views of Cz [-0.4—1.05],Mach:0.6-1.4, Alpha:0°-30°' ]The accuracy of efficient flight simulation depends on the quality of the aerodynamic data used to simulate aircraft dynamic motion. The accuracy of such data prediction depends strongly on motion variables, aerodynamic derivatives, and the coefficients used when the complete global aerodynamic database is being building. A surrogate model applied as a prediction method based on several measured points (exact function) used to predict unknown points of interest helps reduce time taken by the experiment or computation. Latin hypercube sampling searches the solution space for aerodynamic data to optimize the experimental design, so the key objective is to develop an aircraft's efficient digital flight motion by solving equations of motion and predicting aerodynamic data using a surrogate model. To realize these goals, we use sample surrogate model data, acquired from empirical model USAF Stability and Control DATCOM. The database was built for two main variables, the angle of attack and the Mach number, along the longitudinal and lateral axes. Exact and predicted functions were compared by calculating the mean squared error (MSE). The digital flight was validated through mode motion analysis and a flight quality scale to prove flight mission capabilities. A comparison between results predicted by the surrogate model and the exact function showed that flight simulation analysis and prediction ability of the surrogate model are useful in future analyses.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang
Keyword(s):  
High Pressure ◽  
Surrogate Model ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Expected Improvement ◽  
Total Efficiency ◽  
High Pressure Turbine ◽  
Sweep Angle ◽  
Design Variables ◽  
Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

A Method to Define the Best Weld Sequence Using a Limited Number of Welding Simulation Analysis

2015 ◽  
Author(s):  
Mahyar Asadi ◽  
Ghazi Alsoruji
Keyword(s):  
Simulation Model ◽  
Surrogate Model ◽  
Simulation Analysis ◽  
Combinatorial Problem ◽  
Fem Analysis ◽  
Simulation Models ◽  
Computational Time ◽  
Optimal Sequence ◽  
Welded Structure ◽  
Welding Sequence

Weld sequence optimization, which is determining the best (and worst) welding sequence for welding work pieces, is a very common problem in welding design. The solution for such a combinatorial problem is limited by available resources. Although there are fast simulation models that support sequencing design, still it takes long because of many possible combinations, e.g. millions in a welded structure involving 10 passes. It is not feasible to choose the optimal sequence by evaluating all possible combinations, therefore this paper employs surrogate modeling that partially explores the design space and constructs an approximation model from some combinations of solutions of the expensive simulation model to mimic the behavior of the simulation model as closely as possible but at a much lower computational time and cost. This surrogate model, then, could be used to approximate the behavior of the other combinations and to find the best (and worst) sequence in terms of distortion. The technique is developed and tested on a simple panel structure with 4 weld passes, but essentially can be generalized to many weld passes. A comparison between the results of the surrogate model and the full transient FEM analysis all possible combinations shows the accuracy of the algorithm/model.

scholarly journals Research on the Unstable Branch Screening Method for Power System With High-Proportion Wind Power

2022 ◽  
Vol 9 ◽  
Author(s):  
Fei Tang ◽  
Xiaoqing Wei ◽  
Yuhan Guo ◽  
Junfeng Qi ◽  
Jiarui Xie ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Simulation Analysis ◽  
Screening Method ◽  
Prediction Method ◽  
Stability Margin ◽  
Screening Methods ◽  
Unstable Branch ◽  
Oscillation Characteristics

The sooner the system instability is predicted and the unstable branches are screened, the timelier emergency control can be implemented for a wind power system. In this paper, aiming at the problem that the existing unstable branch screening methods are lack prejudgment, an unstable branch screening method for power system with high-proportion wind power is proposed. Firstly, the equivalent external characteristics model of the wind farm was deduced. And based on this, the out-of-step oscillation characteristics of the power system with high proportion wind power was analyzed. Secondly, based on the oscillation characteristics, line weak-connection index (LWcI) was proposed to quantify the stability margin of a branch. Then an instability prediction method and an unstable branch screening method were proposed based on LWcI and voltage phase angle difference. Finally, the rapidity and effectiveness of the proposed method are verified through the simulation analysis of IEEE-118 system.

Optimizing assembly sequence planning using precedence graph-based assembly subsets prediction method

2019 ◽  
Vol 40 (2) ◽  
pp. 361-375 ◽  
Author(s):  
Nan Zhang ◽  
Zhenyu Liu ◽  
Chan Qiu ◽  
Weifei Hu ◽  
Jianrong Tan
Keyword(s):  
High Efficiency ◽  
Prediction Method ◽  
Solution Space ◽  
Assembly Sequence Planning ◽  
Selection Strategy ◽  
Assembly Process ◽  
Assembly Sequence ◽  
Content Type ◽  
Sequence Planning ◽  
Precedence Graph

Purpose Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms. Design/methodology/approach A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated. Findings The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited. Practical implications The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications. Originality/value The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

scholarly journals The Effects of Icing on Aircraft Longitudinal Aerodynamic Characteristics

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1171
Author(s):  
Yihua Cao ◽  
Wenyuan Tan ◽  
Yuan Su ◽  
Zhongda Xu ◽  
Guo Zhong
Keyword(s):  
Prediction Method ◽  
Flight Test ◽  
Aerodynamic Characteristics ◽  
Identification System ◽  
Narrow Strip ◽  
Aerodynamic Derivatives ◽  
Strip Theory ◽  
Aerodynamic Parameters ◽  
Flight Dynamics Model ◽  
Derivatives Of

To study the effects of ice accretion on the longitudinal aerodynamic characteristics of an aircraft, a two-part method for predicting longitudinal aerodynamic derivatives of iced aircraft is proposed. For the aircraft with a flight test, a parameter identification system based on maximum likelihood criterion and a longitudinal nonlinear flight dynamics model is established. For the aircraft without a flight test, an engineering prediction method of aerodynamic derivatives based on an individual component CFD calculation and narrow strip theory is established. According to the flight test data of DHC-6 Twin Otter aircraft from NASA, the longitudinal aerodynamic parameters of both clean and artificially iced aircraft are obtained. Additionally, the longitudinal aerodynamic derivatives of the iced aircraft are calculated. Then, the correctness of the prediction method is verified by comparing the calculated results with the identification results. The comparison of these results shows that the prediction method is correct and accurate, and it can be used to calculate the effects of icing on the aircraft longitudinal aerodynamic parameters.

scholarly journals Sample-Specific Prediction Error Measures in Spectroscopy

2020 ◽  
Vol 74 (7) ◽  
pp. 791-798
Author(s):  
Carl Emil Eskildsen ◽  
Tormod Næs
Keyword(s):  
Chemical Reaction ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Predictive Performance ◽  
Calibration Model ◽  
Prediction Errors ◽  
Prediction Ability ◽  
Spectroscopic Measurements ◽  
Error Measures ◽  
Specific Prediction

In applied spectroscopy, the purpose of multivariate calibration is almost exclusively to relate analyte concentrations and spectroscopic measurements. The multivariate calibration model provides estimates of analyte concentrations based on the spectroscopic measurements. Predictive performance is often evaluated based on a mean squared error. While this average measure can be used in model selection, it is not satisfactory for evaluating the uncertainty of individual predictions. For a calibration, the uncertainties are sample specific. This is especially true for multivariate calibration, where interfering compounds may be present. Consider in-line spectroscopic measurements during a chemical reaction, production, etc. Here, reference values are not necessarily available. Hence, one should know the uncertainty of a given prediction in order to use that prediction for telling the state of the chemical reaction, adjusting the process, etc. In this paper, we discuss the influence of variance and bias on sample-specific prediction errors in multivariate calibration. We compare theoretical formulae with results obtained on experimental data. The results point towards the fact that bias contribution cannot necessarily be neglected when assessing sample-specific prediction ability in practice.

scholarly journals Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

Engineering Design Based on Hammersley Sequences Sampling Method and SVR

2012 ◽  
Vol 544 ◽  
pp. 206-211 ◽  
Author(s):  
Li Ke ◽  
Hao Bo Qiu ◽  
Zhen Zhong Chen ◽  
Li Chi
Keyword(s):  
Engineering Design ◽  
Surrogate Model ◽  
Sampling Method ◽  
Simulation Analysis ◽  
Design Method ◽  
Computational Cost ◽  
Simulation Design ◽  
Fem Method ◽  
Computationally Expensive ◽  
Sample Method

Nowadays, computer-based engineering design is becoming widely used in the design of products. In the field of engineering, CAD model, FEM method and surrogate model are used to reduce the time and computational cost comparing with the traditional engineering design. But when facing with computationally expensive tasks, such design method mentioned above seems unable to deal with such tasks. In that case, surrogate model is gradually used and shows great potential in dealing with the computationally complex tasks. Although computing power and speed are rapidly growing, the use of the computer simulation analysis is limited in doing engineering design and some other analysis such as reliability analysis for complex product, so that it limits the use of metamodeling techniques. In that case, we use space-filling DOE sample method to support the construction of surrogate model. In this paper, we consider both Hammersley sequences and SVM as sampling method and surrogate model to construct the simulation design model, aiming at reduce computational costs. SVR achieves more accuracy and shows great potential in application in the design of complex and computationally expensive tasks.

scholarly journals Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

2017 ◽  
Vol 45 (1) ◽  
pp. 22-27 ◽  
Author(s):  
Bunyamin DEMIR ◽  
Ikbal ESKI ◽  
Zeynel A. KUS ◽  
Sezai ERCISLI
Keyword(s):  
Neural Network ◽  
Physical Properties ◽  
Intelligent Systems ◽  
Mean Squared Error ◽  
Simulation Analysis ◽  
Geometric Mean ◽  
Design Parameters ◽  
Physical Parameters ◽  
Pumpkin Seeds ◽  
Agricultural Materials

The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods.

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