scholarly journals Neuro-Fuzzy Network-Based Reduced-Order Modeling of Transonic Aileron Buzz

Aerospace ◽  
2020 ◽  
Vol 7 (11) ◽  
pp. 162
Author(s):  
Rebecca Zahn ◽  
Christian Breitsamter
Keyword(s):  
Fuzzy Model ◽  
Reduced Order Modeling ◽  
Training Data ◽  
Navier Stokes ◽  
Training Procedure ◽  
Data Set ◽  
Reduced Order ◽  
Forced Motion ◽  
Neuro Fuzzy ◽  
Fuzzy Network

In the present work, a reduced-order modeling (ROM) framework based on a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is applied for the computation of transonic aileron buzz. The training data set for the specified ROM is obtained by performing forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulations. Further, a Monte Carlo-based training procedure is applied in order to estimate statistical errors. In order to demonstrate the method’s fidelity, a two-dimensional aeroelastic model based on the NACA651213 airfoil is investigated at different flow conditions, while the aileron deflection and the hinge moment are considered in particular. The aileron is integrated in the wing section without a gap and is modeled as rigid. The dynamic equations of the rigid aileron rotation are coupled with the URANS-based flow model. For ROM training purposes, the aileron is excited via a forced motion and the respective aerodynamic and aeroelastic response is computed using a computational fluid dynamics (CFD) solver. A comparison with the high-fidelity reference CFD solutions shows that the essential characteristics of the nonlinear buzz phenomenon are captured by the selected ROM method.

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

scholarly journals Investigation of the Effects of Length to Depth Ratio on Open Supersonic Cavities Using CFD and Proper Orthogonal Decomposition

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ibrahim Yilmaz ◽  
Ece Ayli ◽  
Selin Aradag
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Stokes Equations ◽  
Reduced Order Modeling ◽  
Orthogonal Decomposition ◽  
Navier Stokes ◽  
Reduced Order ◽  
Modeling Approach ◽  
Depth Ratio ◽  
Minimum Number ◽  
Proper Orthogonal

Simulations of supersonic turbulent flow over an open rectangular cavity are performed to observe the effects of length to depth ratio (L/D) of the cavity on the flow structure. Two-dimensional compressible time-dependent Reynolds-averaged Navier-Stokes equations with k-ωturbulence model are solved. A reduced order modeling approach, Proper Orthogonal Decomposition (POD) method, is used to further analyze the flow. Results are obtained for cavities with severalL/Dratios at a Mach number of 1.5. Mostly, sound pressure levels (SPL) are used for comparison. After a reduced order modeling approach, the number of modes necessary to represent the systems is observed for each case. The necessary minimum number of modes to define the system increases as the flow becomes more complex with the increase in theL/Dratio. This study provides a basis for the control of flow over supersonic open cavities by providing a reduced order model for flow control, and it also gives an insight to cavity flow physics by comparing several simulation results with different length to depth ratios.

scholarly journals Improvement of Urban Flood Alert Criteria Prediction Model based on Neuro-Fuzzy Initial Function and Training Data

2020 ◽  
Vol 20 (1) ◽  
pp. 327-337
Author(s):  
Hoseon Kang ◽  
Jaewoong Cho ◽  
Hanseung Lee ◽  
Jeonggeun Hwang
Keyword(s):  
Fuzzy Model ◽  
Training Data ◽  
Average Error ◽  
Urban Flood ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Flood Alert ◽  
Improved Model ◽  
Steep Slopes ◽  
And Training

In Korean metropolitan areas, the high density of residential and commercial buildings, highly impervious surfaces, and steep slopes contribute to floods that can occur within a short duration of heavy rainfall. To prepare for this, advance warning measures based on accurate flood alert criteria are needed. In our previous study, we demonstrated the applications of a Neuro-Fuzzy model that considersthe characteristics of the basin to predict flood alert criteria in areas with no damage. However, as the number of learning materials are low, at 27, the evaluation and verification of the model has not been sufficiently accomplished, and its application is limited. Therefore, in this study, we propose an improved model based on the initializing function of the Neuro-Fuzzy algorithm, the construction of training data, and preprocessing. Compared to the existing model, the improved model reduced the average error by 48.1%~65.4% and the RMSE by 50.7%~60.1%. The new model, when applied to actual floods, showed an improvement of 0.7%~19.1% in accuracy.

scholarly journals A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

2014 ◽  
Vol 7 (5) ◽  
pp. 2091-2105 ◽  
Author(s):  
G. S. H. Pau ◽  
G. Bisht ◽  
W. J. Riley
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Approximation Error ◽  
Reduced Order Modeling ◽  
Data Set ◽  
Reduced Order ◽  
Coarse Resolution ◽  
Wide Range ◽  
Study Sites

Abstract. Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO2, CH4) exchanges with the atmosphere range from the molecular scale (pore-scale O2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 103) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

Liquid Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Steady-State and Transient Operations

2020 ◽  
Author(s):  
Christopher MacDonald ◽  
Michael Yang ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Artificial Intelligence ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Transient Model ◽  
Rule Based ◽  
Detection Systems ◽  
Pump Station ◽  
Neuro Fuzzy

Abstract There are several challenges associated with existing rupture detection systems such as their inability to accurately detect during transient (such as pump dynamics) conditions, delayed responses and their inability to transfer models to different pipeline configurations easily. To address these challenges, we employ multiple Artificial Intelligence (AI) classifiers that rely on pattern recognitions instead of traditional operator-set thresholds. AI techniques, consisting of two-dimensional (2D) Convolutional Neural Networks (CNN) and Adaptive Neuro Fuzzy Interface Systems (ANFIS), are used to mimic processes performed by operators during a rupture event. This includes both visualization (using CNN) and rule-based decision making (using ANFIS). The system provides a level of reasoning to an operator through the use of the rule-based AI system. Pump station sensor data is non-dimensionalized prior to AI processing, enabling application to pipeline configurations outside of the training data set. AI algorithms undergo testing and training using two data sets: laboratory-collected data that mimics transient pump-station operations and real operator data that includes Real Time Transient Model (RTTM) simulated ruptures. The use of non-dimensional sensor data enables the system to detect ruptures from pipeline data not used in the training process.

scholarly journals Real-Time Transient Soot and NOx Virtual Sensors for Diesel Engine Using Neuro-Fuzzy Model Tree and Orthogonal Least Squares

2012 ◽  
Vol 134 (9) ◽  
Author(s):  
Rajit Johri ◽  
Ashwin Salvi ◽  
Zoran Filipi
Keyword(s):  
Diesel Engine ◽  
Fuzzy Model ◽  
Complex Problem ◽  
Training Data ◽  
Divide And Conquer ◽  
Exhaust Emission ◽  
Virtual Sensors ◽  
Model Tree ◽  
Neuro Fuzzy ◽  
Highly Nonlinear

Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely the “neuro-fuzzy model tree.” The approach is based on divide-and-conquer strategy, i.e., to divide a complex problem into multiple simpler subproblems, which can then be identified using a simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOx provide instantaneous engine-out emissions. Finally, the engine-in-the-loop is used to validate the models for predicting transient particulate mass and NOx.

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