Oleg Levinski - Academia.edu (original) (raw)

Papers by Oleg Levinski

Research paper thumbnail of An All-Movable Horizontal Tail Numerical Model for Free-play Prognostics

Research paper thumbnail of Aerodynamic loads estimation on a Twin Vertical-Tail configuration based on the Single-Step Lattice Boltzmann Method Simulation

Research paper thumbnail of Loads Estimation from Calibration Test with Machine Learning

AIAA SCITECH 2023 Forum, Jan 19, 2023

Research paper thumbnail of Generation of Generalised Aerodynamic Forces Through CFD Based Methods for Aeroelastic Stability Analysis

AIAA SCITECH 2023 Forum, Jan 19, 2023

Research paper thumbnail of Automatic Operational Modal Analysis of Flight Test Data Using a Modal Decomposition

AIAA Journal, Oct 1, 2021

This paper proposes a novel method for an automatic operational modal analysis. An available fini... more This paper proposes a novel method for an automatic operational modal analysis. An available finite element structural model is used together with sensor data to derive a linear representation of t...

Research paper thumbnail of A Dynamic Mode Decomposition Based Approach for Rapid Detection of Aeroelastic Modes from Flutter Flight Test Data

Research paper thumbnail of Transonic Shock Buffet Flowfield Assessment Using Various Dynamic Mode Decomposition Techniques

Journal of Aircraft

Dynamic mode decomposition (DMD) is a powerful data-driven modal decomposition technique that ext... more Dynamic mode decomposition (DMD) is a powerful data-driven modal decomposition technique that extracts spatiotemporal coherent structures: a useful process in flow diagnostics and future state estimation of complex nonlinear flow phenomena. Transonic shock buffet is a complicated phenomenon, and modal decomposition techniques such as DMD provide significant insight into its complicated flow physics; but, often, flowfield data are corrupted because of various sources of noise due to the presence of outliers or the absence of critical data components. Therefore, noise corruption renders the modal decomposition inaccurate, and thereby not useful. In this paper, two sources of noise have been considered: simple white noise, and complex salt-and-pepper-type spurious noise. Various DMD techniques including standard DMD, forward–backward DMD, total-least-squares DMD, higher-order DMD, and robust DMD have been implemented. Their effectiveness and limitations in countering noise corruption h...

Research paper thumbnail of A Data-Driven Approach to Control Surface Free-play Diagnostics with Actuator Load Responses

Research paper thumbnail of Unsteady Flow Simulation by Vortex Methods

The design of many modern devices calls for an aerodynamic prediction model for the analysis of m... more The design of many modern devices calls for an aerodynamic prediction model for the analysis of multi-component lifting bodies in unsteady motion. Moreover, the ability of the numerical model to deal with viscous separated flows is essential to obtain reliable prediction of aerodynamic characteristics. The work presented here considers unsteady flow analysis based on a vortex approach for simulation of two-dimensional Navier-Stokes equations. It can be used independently or in combination with inviscid vortex models to better suit the particular flow regime and in order to gain economies in computation. Some advantages and shortcomings of such vortex approaches are discussed. Unsteady load predictions from these vortex models are compared with the results of water channel experiments on an airfoil undergoing oscillatory motion over a range of angles of attack and frequencies.

Research paper thumbnail of Prediction of Aircraft Empennage Buffet Loads

The flow around an F/A-18 aircraft at high angle-of-attack is modelled with the aim of predicting... more The flow around an F/A-18 aircraft at high angle-of-attack is modelled with the aim of predicting the bending moments of the vertical tail resulting from differential buffet pressures on either side of the tail. In order to resolve the unsteady vortex breakdown and its subsequent interaction with the vertical tail, the method of detached-eddy simulation (DES) is used. The results obtained on a fairly coarse mesh show that the main flow dynamics of the vortex breakdown is well captured. A comparison of the fluctuating buffet pressure distribution on both sides of the tail with experimental data shows that reasonable agreement is obtained, although some detail around the leading edge of the tail is missing. However, the prediction of the bending moment is very encouraging considering the relatively coarse mesh used in the computation.

Research paper thumbnail of Experimental Investigation of the Unsteady Aerodynamics of a Symmetric Aerofoil at Low Reynolds Numbers

The unsteady behaviour of aerofoils has application to fixed wing aircraft encountering gusts or ... more The unsteady behaviour of aerofoils has application to fixed wing aircraft encountering gusts or wind sheer, super-manoeuvrable aircraft with articulated wings, helicopters and wind turbines as well as the marine use of hydrofoils. The aerodynamic characteristics of an unsteady aerofoil deviates substantially from the steady flow case. Many such devices will have aerofoils working at relative velocities low enough to introduce a Reynolds Number dependence. Aerofoils typically undergo a rapid degradation in performance at Reynolds numbers below approximately 1x10{6}. The paper introduces the experimental facility now running at the University of Melbourne designed to study the effect of Reynolds Number on an aerofoil in the unsteady condition. The apparatus described allows full flow visualisation and directly measures the lift, drag and pitching moment at 400Hz with Strouhal Numbers of up to 1.2 at a Reynolds Number of 1x10{6}. The configuration for phase linked heave (flap-wise) and surge (chord-wise) periodic motions will be described in detail with respect to previous work in the field. The current experimental program for Reynolds numbers from 1x10{6} down to 2.5x10{6} will also be described herein.

Research paper thumbnail of A Data-Driven Signal Processing Framework for Enhanced Freeplay Diagnostics in NextGen Structural Health Monitoring Systems

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Data-Driven Flight Load Prediction using Modal Decomposition Techniques

Research paper thumbnail of Investigation onto Deep Transonic Buffet Condition of a Supercritical Airfoil using Multiresolution Dynamic Mode Decomposition

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Recent Developments in the Implementation of a Bidirectional LSTM Deep Neural Network for Aircraft Operational Loads Monitoring

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning

Mechanical Systems and Signal Processing, 2022

Research paper thumbnail of A nonlinear signal processing framework for rapid identification and diagnosis of structural freeplay

Mechanical Systems and Signal Processing

Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occur... more Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occurs pseudo-routinely in modern aircraft, causing severe airframe vibration. This impacts fatigue life, and has serious implications for fleet management and Structural Health Monitoring (SHM). While the concepts which drive SHM for aircraft are traditionally based on reactive procedures, we are currently observing a major shift towards actionable and pro-active condition-based maintenance, known as Prognostics and Health Management (PHM), to significantly reduce fleet sustainment costs. Given this current paradigm shift, there is a demand for the development of novel strategies to address decades old SHM problems, where the adaptation of existing methods or the development of new and innovative techniques both play critical roles. In this paper a signal processing framework is presented, based upon well-established nonlinear system identification methods, to rapidly diagnose structural fre...

Research paper thumbnail of Prediction of Buffet Loads Using Artificial Neural Networks

Abstract : The use of artificial neural networks (ANN) for predicting the empennage buffet pressu... more Abstract : The use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN algorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.

Research paper thumbnail of Experimental Investigation of Vertical Tail Buffet

The paper describes development and testing of the Generic Buffet Model which consists of a sharp... more The paper describes development and testing of the Generic Buffet Model which consists of a sharp-edged, 76-degree leading edge sweep delta wing and swept back twin vertical tails. The tails are cantilevered on the upper surface of a trailing edge extension of the delta wing. The model has been tested at Reynolds numbers of up to 5x10{6} and at angles of attack up to 45 degrees in the 9'x 7' low-speed wind tunnel at the Defence Science and Technology Organisation (DSTO). The study aimed to investigate the process of leading edge vortex breakdown and its interaction with vertical tails. A new multi-channel Dynamic Pressure Measurement System (DPMS) has been employed for the measurement of unsteady differential buffet pressures arising on the vertical tails. The experimental results show that DPMS is able to accurately map the buffet pressures distribution over the vertical tail and provide quick assessment of their spatial and temporal characteristics. Generic Buffet Model equipped with the Dynamic Pressure Measurement System will be used as a test bed for experimental validation and further development of new and existing numerical and semi-empirical buffet prediction methods.

Research paper thumbnail of Use of Artificial Neural Networks for Buffet Loads Prediction

Lecture Notes in Computer Science, 2002

The use of Artificial Neural Networks (ANN) for predicting the empennage buffet pressures as a fu... more The use of Artificial Neural Networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN algorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.

Research paper thumbnail of An All-Movable Horizontal Tail Numerical Model for Free-play Prognostics

Research paper thumbnail of Aerodynamic loads estimation on a Twin Vertical-Tail configuration based on the Single-Step Lattice Boltzmann Method Simulation

Research paper thumbnail of Loads Estimation from Calibration Test with Machine Learning

AIAA SCITECH 2023 Forum, Jan 19, 2023

Research paper thumbnail of Generation of Generalised Aerodynamic Forces Through CFD Based Methods for Aeroelastic Stability Analysis

AIAA SCITECH 2023 Forum, Jan 19, 2023

Research paper thumbnail of Automatic Operational Modal Analysis of Flight Test Data Using a Modal Decomposition

AIAA Journal, Oct 1, 2021

This paper proposes a novel method for an automatic operational modal analysis. An available fini... more This paper proposes a novel method for an automatic operational modal analysis. An available finite element structural model is used together with sensor data to derive a linear representation of t...

Research paper thumbnail of A Dynamic Mode Decomposition Based Approach for Rapid Detection of Aeroelastic Modes from Flutter Flight Test Data

Research paper thumbnail of Transonic Shock Buffet Flowfield Assessment Using Various Dynamic Mode Decomposition Techniques

Journal of Aircraft

Dynamic mode decomposition (DMD) is a powerful data-driven modal decomposition technique that ext... more Dynamic mode decomposition (DMD) is a powerful data-driven modal decomposition technique that extracts spatiotemporal coherent structures: a useful process in flow diagnostics and future state estimation of complex nonlinear flow phenomena. Transonic shock buffet is a complicated phenomenon, and modal decomposition techniques such as DMD provide significant insight into its complicated flow physics; but, often, flowfield data are corrupted because of various sources of noise due to the presence of outliers or the absence of critical data components. Therefore, noise corruption renders the modal decomposition inaccurate, and thereby not useful. In this paper, two sources of noise have been considered: simple white noise, and complex salt-and-pepper-type spurious noise. Various DMD techniques including standard DMD, forward–backward DMD, total-least-squares DMD, higher-order DMD, and robust DMD have been implemented. Their effectiveness and limitations in countering noise corruption h...

Research paper thumbnail of A Data-Driven Approach to Control Surface Free-play Diagnostics with Actuator Load Responses

Research paper thumbnail of Unsteady Flow Simulation by Vortex Methods

The design of many modern devices calls for an aerodynamic prediction model for the analysis of m... more The design of many modern devices calls for an aerodynamic prediction model for the analysis of multi-component lifting bodies in unsteady motion. Moreover, the ability of the numerical model to deal with viscous separated flows is essential to obtain reliable prediction of aerodynamic characteristics. The work presented here considers unsteady flow analysis based on a vortex approach for simulation of two-dimensional Navier-Stokes equations. It can be used independently or in combination with inviscid vortex models to better suit the particular flow regime and in order to gain economies in computation. Some advantages and shortcomings of such vortex approaches are discussed. Unsteady load predictions from these vortex models are compared with the results of water channel experiments on an airfoil undergoing oscillatory motion over a range of angles of attack and frequencies.

Research paper thumbnail of Prediction of Aircraft Empennage Buffet Loads

The flow around an F/A-18 aircraft at high angle-of-attack is modelled with the aim of predicting... more The flow around an F/A-18 aircraft at high angle-of-attack is modelled with the aim of predicting the bending moments of the vertical tail resulting from differential buffet pressures on either side of the tail. In order to resolve the unsteady vortex breakdown and its subsequent interaction with the vertical tail, the method of detached-eddy simulation (DES) is used. The results obtained on a fairly coarse mesh show that the main flow dynamics of the vortex breakdown is well captured. A comparison of the fluctuating buffet pressure distribution on both sides of the tail with experimental data shows that reasonable agreement is obtained, although some detail around the leading edge of the tail is missing. However, the prediction of the bending moment is very encouraging considering the relatively coarse mesh used in the computation.

Research paper thumbnail of Experimental Investigation of the Unsteady Aerodynamics of a Symmetric Aerofoil at Low Reynolds Numbers

The unsteady behaviour of aerofoils has application to fixed wing aircraft encountering gusts or ... more The unsteady behaviour of aerofoils has application to fixed wing aircraft encountering gusts or wind sheer, super-manoeuvrable aircraft with articulated wings, helicopters and wind turbines as well as the marine use of hydrofoils. The aerodynamic characteristics of an unsteady aerofoil deviates substantially from the steady flow case. Many such devices will have aerofoils working at relative velocities low enough to introduce a Reynolds Number dependence. Aerofoils typically undergo a rapid degradation in performance at Reynolds numbers below approximately 1x10{6}. The paper introduces the experimental facility now running at the University of Melbourne designed to study the effect of Reynolds Number on an aerofoil in the unsteady condition. The apparatus described allows full flow visualisation and directly measures the lift, drag and pitching moment at 400Hz with Strouhal Numbers of up to 1.2 at a Reynolds Number of 1x10{6}. The configuration for phase linked heave (flap-wise) and surge (chord-wise) periodic motions will be described in detail with respect to previous work in the field. The current experimental program for Reynolds numbers from 1x10{6} down to 2.5x10{6} will also be described herein.

Research paper thumbnail of A Data-Driven Signal Processing Framework for Enhanced Freeplay Diagnostics in NextGen Structural Health Monitoring Systems

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Data-Driven Flight Load Prediction using Modal Decomposition Techniques

Research paper thumbnail of Investigation onto Deep Transonic Buffet Condition of a Supercritical Airfoil using Multiresolution Dynamic Mode Decomposition

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Recent Developments in the Implementation of a Bidirectional LSTM Deep Neural Network for Aircraft Operational Loads Monitoring

AIAA SCITECH 2022 Forum, 2022

Research paper thumbnail of Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning

Mechanical Systems and Signal Processing, 2022

Research paper thumbnail of A nonlinear signal processing framework for rapid identification and diagnosis of structural freeplay

Mechanical Systems and Signal Processing

Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occur... more Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occurs pseudo-routinely in modern aircraft, causing severe airframe vibration. This impacts fatigue life, and has serious implications for fleet management and Structural Health Monitoring (SHM). While the concepts which drive SHM for aircraft are traditionally based on reactive procedures, we are currently observing a major shift towards actionable and pro-active condition-based maintenance, known as Prognostics and Health Management (PHM), to significantly reduce fleet sustainment costs. Given this current paradigm shift, there is a demand for the development of novel strategies to address decades old SHM problems, where the adaptation of existing methods or the development of new and innovative techniques both play critical roles. In this paper a signal processing framework is presented, based upon well-established nonlinear system identification methods, to rapidly diagnose structural fre...

Research paper thumbnail of Prediction of Buffet Loads Using Artificial Neural Networks

Abstract : The use of artificial neural networks (ANN) for predicting the empennage buffet pressu... more Abstract : The use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN algorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.

Research paper thumbnail of Experimental Investigation of Vertical Tail Buffet

The paper describes development and testing of the Generic Buffet Model which consists of a sharp... more The paper describes development and testing of the Generic Buffet Model which consists of a sharp-edged, 76-degree leading edge sweep delta wing and swept back twin vertical tails. The tails are cantilevered on the upper surface of a trailing edge extension of the delta wing. The model has been tested at Reynolds numbers of up to 5x10{6} and at angles of attack up to 45 degrees in the 9'x 7' low-speed wind tunnel at the Defence Science and Technology Organisation (DSTO). The study aimed to investigate the process of leading edge vortex breakdown and its interaction with vertical tails. A new multi-channel Dynamic Pressure Measurement System (DPMS) has been employed for the measurement of unsteady differential buffet pressures arising on the vertical tails. The experimental results show that DPMS is able to accurately map the buffet pressures distribution over the vertical tail and provide quick assessment of their spatial and temporal characteristics. Generic Buffet Model equipped with the Dynamic Pressure Measurement System will be used as a test bed for experimental validation and further development of new and existing numerical and semi-empirical buffet prediction methods.

Research paper thumbnail of Use of Artificial Neural Networks for Buffet Loads Prediction

Lecture Notes in Computer Science, 2002

The use of Artificial Neural Networks (ANN) for predicting the empennage buffet pressures as a fu... more The use of Artificial Neural Networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN algorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.