Pre-Test of a Light-Weight CFRP Wing Segment of a High Altitude Platform for In-flight Load Measurements Based on Strains

For the validation of loads and aeroelastic simulations of a high altitude platform (HAP) in-flight, load measurements are very helpful. One technique to obtain load measurements is to apply strain gages to the primary structure. A calibration procedure enables the reconstruction of the loads from the measured strains. To reduce the risks, gain confidence and establish a feasible measurement set-up, a pre-test based on a CFRP wing segment is performed. This paper presents the theoretical background and the set-up of the measurement equipment. The results of the calibration from three different test cases are presented, showing an excellent agreement between applied and reconstructed loading with a deviation of < +/- 1.0 N and < +/- 2.0 cm for the location. Finally, the strains measured in the experiment are compared with the simulation, showing that a prediction of the strains is possible within a range of < +/- 8%

Design and wind tunnel test of an actively controlled flexible wing

The reduction of flight loads has the potential to reduce structural mass in wing design and to improve passenger comfort. In the DLR project KonTeKst, an actively controlled wing was designed, manufactured and tested in a wind tunnel experiment. The motivation of the activity was to validate methods for the design of combined passive and active load alleviation techniques, including aeroelastic tailoring and active load control. In addition, the experimental activities provided an opportunity to improve and validate modal identification techniques. The wind tunnel model is a flexible composite wing of 1.6 meter (half-) span, with three flaps used for load control. The control design uses H2 optimal blending techniques for input and output. The control strategy is capable of accounting for failure of one flap without critically losing performance. The wind tunnel experiment was performed in a wind tunnel with a maximum speed of 50 m/s. The paper will give an overview of the numerical design activities for wing and load control, of wing design and manufacturing, including the selection and installation of sensors and actuators, and of the wind tunnel set up including the actuation of the wing, data acquisition and online analysis, as well as selected results

System identification and modal tracking on ship structures

Thesis (PhD)--Stellenbosch University, 2018.ENGLISH SUMMARY: are currently based mainly on dynamic response feedback. Navigators decide on how to operate the vessel based on how they feel it pitching, heaving, rolling and vibrating. The aim of this thesis is to investigate the idea of using system identification and modal tracking on polar vessels towards the development of a decision aiding system. System identification provides a powerful tool for building mathematical models of dynamic systems. An open source toolbox (openSID) for system identification using Stochastic Subspace Identification (SSI) was developed as a research and learning tool. Full scale measurements were performed on the research vessel Polarstern during an expedition to the Arctic. This is the first comprehensive data set including vibration responses and environmental parameters to span the entire operational profile of a research voyage to the Arctic. System identification successfully identified seven global modes in the bandwidth 2 - 10 Hz. Comparisons between different methods were used to cross validate results. A modal tracking algorithm was developed and relationships between identified modes and system inputs were observed. A novel method is developed to improve the uncertainty and sensitivity of system identification and tracking, based on a data driven statistical model and a Kalman filter. A key objective is to make experimental data maximally informative by using additional system inputs. The model was found to accurately re-create the training data set and was used to make predictions based on future system inputs. The Kalman filter estimates were observed to produce balanced and consistent results. These results demonstrate the potential of an ice force estimation and structural health monitoring system.AFRIKAANSE OPSOMMING: Kritieke besluite in terme van die veilige en doelgerigte bedryf van skepe in ys is tans hoofsaaklik gebasseer op dinamiese hanterings terugvoer. Besluite oor hoe om die vaartuig te navigeer word toegelig deur hoe seevaarders die skip voel duik, heg, rol en vibreer. Die doel van hierdie tesis is om die idee van stelselidentifikasie en modale naspeuring op poolskepe te ondersoek ten einde die ontwikkeling van ’n besluitnemingstelsel. Stelselidentifikasie bied ’n kragtige metode vir die bou van wiskundige modelle van dinamiese stelsels. Oopbron gereedskap algoritme (openSID) vir stelselidentifikasie, met die gebruik van Stochastiese Subspasie Identifikasie (SSI) is ontwikkel as ’n navorsings en leer instrument. Volskaal metings is uitgevoer op die navorsing skip Polarstern tydens ’n ekspedisie na die Arktiese gebied. Dit is die eerste omvattende datastel wat vibrasierespons en omgewingsparameters insluit om die hele operasionele profiel van ’n navorsingsreis na die Arktiese omgewing te dek. Stelselidentifikasie het sewe globale modes in die bandwydte 2 - 10 Hz geïdentifiseer. Vergelykings tussen twee metodes is gebruik om resultate te bekragtig. Modale naspeuringsalgoritme is ontwikkel en verhoudings tussen geïdentifiseerde modusse en stelselinsette is waargeneem. Nuwe metode is ontwikkel om die onsekerheid en sensitiwiteit van stelselidentifikasie en naspeuring te verbeter, gebasseer op ’n data gedrewe statistiese model en ’n Kalman filter. ’n Hoof doelwit is om eksperimentele data maksimaal insiggewend te maak deur addisionele stelsel insette te gebruik. Dit is gevind dat die model die opleidingsdatastel akkuraat naboots. Hierna is dit gebruik om voorspellings te maak gebasseer op toekomstige stelselinsette. Beraming met die Kalman filter is waargeneem om gebalanseerde en konsekwente resultate te lewer. Hierdie resultate demonstreer die potensiaal van ’n besluitnemingsstelsel om ys kragte af te skat en strukturele integriteit te monitor

Vibration response of the polar supply and research vessel the S. A. Agulhas II in Antarctica and the Southern ocean

Thesis (MEng) -- Stellenbosch University, 2014.ENGLISH ABSTRACT: Full scale measurements were conducted on the polar supply and research vessel the S.A. Agulhas II during a 78 day voyage from Cape Town to Antarctica in 2013/2014. Investigations were conducted into the effect of vibration on human comfort and the structural dynamic response of the vessel. Vibration measured in the bridge of the vessel is found to have little effect on human comfort for a standing person and is classified as not uncomfortable according to BS ISO 2631-1. Structural fatigue as a result of vibration is found to reach levels where damage is possible in the stern and where damage is probable in the bow during open water navigation, according to ship vibration guidelines by Germanischer Lloyd. Multivariate statistical analyses are performed to investigate the relationships between multiple predictor variables and vibration response. Factor analysis revealed data structure from which specific physical phenomena could be identified. Multivariable linear regression models are developed to predict vibration response and are found to provide more accurate predictions in open water than in ice. The 2-node, 3-node and 4-node normal bending modes of the structure are identified using operational modal analysis while the vessel was moored in the harbour. The natural frequencies, damping ratios and mode shapes are estimated and compared using LMS Operational PolyMAX and ARTeMIS CCSSI. A comparison of operational modal analysis results to the STX Finland finite element model show that the vessel’s modes occur at lower frequencies than numerically predicted. Clear potential is identified to further investigate structural vibration and operational modal analysis algorithm development in future research.AFRIKAANSE OPSOMMING: Volskaal metings was op die poolvoorsienings en navorsingskip die S.A. Agulhas II uitgevoer tydens ’n 78 dae reis van Kaapstad tot Antarktika in 2013/2014. Ondersoeke is uitgevoer met betrekking tot die effek van vibrasie op menslike gemak en die strukturele dinamiese reaksie van die vaartuig. Vibrasie wat in die brug van die skip gemeet is, het min invloed op menslike gemak vir ’n staande persoon en word geklassifiseer as nie ongemaklik volgens BS ISO 2631-1. Strukturele vermoeidheid as gevolg van vibrasie bereik vlakke waar skade moontlik is in die spieël en waar skade waarskynlik is in die boog tydens navigasie in oop water, volgens skip vibrasie riglyne deur Germanischer Lloyd. Meerveranderlike statistiese ontledings is uitgevoer om die verhoudings tussen verskeie voorspellerveranderlikes en vibrasieterugvoer te ondersoek. Faktorontleding het data struktuur openbaar waaruit spesifieke fisiese verskynsels geïdentifiseer kan word. Multi-veranderlike lineêre regressiemodelle was ontwikkel om vibrasie reaksie te voorspel en lewer meer akkurate voorspellings in oop water as in ys. Die 2-nodus, 3-nodus en 4-nodus normale buig modes van die struktuur is geïdentifiseer met behulp van operasionele modale analise terwyl die skip vasgemeer in die hawe is. Die natuurlike frekwensie, demping verhoudings en mode vorms is beraam en vergelyk met behulp van LMS operasionele Polymax en ARTeMIS CCSSI. ’n vergelyking van operasionele modale analise resultate en ’n STX Finland eindige element model toon dat die vaartuig se modusse voorkom by laer frekwensies as wat numeries voorspel word. Duidelike potensiaal is geïdentifiseer om strukturele vibrasie en die ontwikkeling van operasionele modale analise algoritmes te ondersoek in toekomstige navorsing

Taxi vibration testing: A new and time efficient procedure for the identification of modal parameters on aircrafts

In order for aircraft prototypes to perform first flight, a flutter clearance to guarantee aeroelastic stability has a fundamental role in the required certification process. The Ground Vibration Test (GVT) is the standard means to determine the dynamic characteristics of the structure which are subsequently used to update numerical models for the flutter clearance. The GVT must be performed at a time critical phase of an aircraft’s development and has therefore been significantly improved and optimized over the last 20 years. In order to achieve further reduction of testing time a completely new philosophy for testing was needed. This resulted in the Taxi Vibration Test (TVT) which uses advanced methods to process output-only data during aircraft taxi to identify modal parameters. The method was developed at DLR Göttingen and has gone through a process of maturation including model and full scale investigations. In this work the final maturation of the method as a viable alternative for efficient certification is demonstrated on data from the A340-600 Research GVT performed in cooperation with Airbus and ONERA in the year 2011. A comprehensive set of modal parameters were identified using the Stochastic Subspace Identification (SSI) method. The influence of the measurement duration on the uncertainty of the modal parameters was investigated statistically. In a TVT, the landing gears are involved in the vibration. These are known to be non-linear, mainly because of the slip-stick type of friction non-linearity in the shock absorbers. Therefore, the importance of the relationship between the taxi speed, which is related to the unknown excitation force, and the modal parameters was identified. The modal acceleration spectra were used as a proxy to quantify this non-linearity in order to provide a meaning comparison between GVT and TVT. Trends of decreasing frequency and increasing damping with increasing speed were observed, which is in agreement with friction type non-linearity. The influence of the landing gear to increase damping and reduce frequency of specific modes was also identified and discussed. The comparison between GVT and TVT confirm this force dependency of the modal parameters. The modal parameters identified from SSI show significantly improved damping estimates and trends, increasing the confidence in the TVT method as a viable alternative for optimal aircraft certification

Automatic modal parameter selection using a statistical model and a Kalman filter

The automation of system identification is important for processing large amounts of data without expert user interaction. Automation is also important to maintain consistency in estimates, especially when investigating trends in data which could be masked by variations of mathematical parameters. This research presents a novel idea to obtain automatic modal parameter estimates based on a data driven statistical model and a Kalman filter. A key objective was to make observed data maximally informative. This lead to the development of a sliding predictive model using an optimized linear regression method to use system inputs which are not included in standard system identification. The method was first demonstrated on a numerical data set where it was found to improve system predictions. The method was then tested on full scale data from the German research vessel Polarstern during a voyage to the Arctic. The automatic Kalman estimates showed improved estimates using the combination of statistical model and modal parameters

Experimental and operational modal analysis: Automated system identification for safety-critical applications

Safety-critical applications like the evaluation of aeroelastic stability during aircraft flight require modal parameters identified with high accuracy. Promising methods of automated modal identification exist. Nevertheless, these methods are not yet chosen for safety-critical applicaions. The reason is either insufficient accuracy of modal parameters or significant adaptions for each individual application. In this work, a new method is presented that not only enables fully automated modal analysis, but also learns an optimal way to analyze the data in a supervised manner. Based on the result of a single manual modal analysis, the self-learning method finds optimal parameters for the automated analysis. In an iterative process, new analysis parameters are chosen by Bayesian Optimization with a Gaussian Process as surrogate model and Expected Improvement as the acquisition function. With these parameters, the method can analyze additional datasets as accurately as a manual expert. The presented method is evaluated on ground vibration test data (i.e., experimental modal analysis) as well as flight vibration data (i.e., operational modal analysis) of an aircraft structure. In contrast to previous methods, the presented method can be easily used for various modal tests, since it can learn by itself to perform optimally with respect to a specific target function like for example the one provided in this work. Due to its robustness, the method is promising also for industrial test cases and safety-critical applications

Optimization of time and frequency domain methods for real-time modal parameter identification of aircraft

Lightweight structures such as aircraft are prone to vibration. In changing flight conditions, the vibration damping may change due to the aerodynamic-structure-interaction. To predict and prevent unstable aeroelastic conditions such as flutter, real-time operational modal analysis can be applied. Due to high noise levels, application to parameter-varying systems and short time data, the uncertainty of such identification is high. In this study, a new method is presented that optimizes the results provided by time and frequency domain methods (Stochasic Subspace Identification, SSI and Least Squares Complex Frequency, LSCF) for real-time modal parameter identification using Bayesian Optimization. Combining the results of both methods decreases the uncertainties and biases of identified modal parameters. The method has been tested on a laboratory aircraft structure which can change its mass dynamically. The whole process of automated SSI and LSCF, their combination and tracking runs in less than two seconds and therefore can be used for continuous real-time modal parameter identification

System identification and tracking using a statistical model and a Kalman filter

The sensitivity of system identification estimates to changing environmental and operational parameters is important for structural health monitoring and inverse force estimation. Damage to a structure can be misidentified or masked by modal shifts as a result of environmental parameters. In this paper a novel approach to reduce the uncertainties and improve the sensitivity of system identification and tracking is presented based on a data driven statistical model and a Kalman filter. A key objective is to make experimental data maximally informative by using additional system inputs. The method is first demonstrated on numerical data where it was found to improve accuracy and identify underlying trends. Investigations were then conducted on full scale data from the research vessel Polarstern. Model training led to the development of a sliding predictive model using an optimized linear regression method. The model was found to accurately re-create the training data set and was used to make predictions based on future system inputs. Since both the model prediction and the system identification estimates contain different uncertainties the Kalman filter was used to combine both estimates in an optimal way. The Kalman filter estimates were observed to produce balanced and consistent results. The Kalman estimates were also not overly or consistently biased by the SSI estimates or the model predictions

Evolutionary based approach to modal parameter identification

An important task in modal analysis is the optimal selection of physical poles of a system. In this paper an Artificial Intelligence (AI) evolutionary algorithm is used to optimize the modal parameters from Output Only Modal Analysis (OMA). The evolutionary algorithm begins by generating a population selected randomly from mode clusters. The fitness of each individual is then evaluated based on the difference between the measured and synthesized spectra. The best individuals are then reproduced using a crossover strategy. A random mutation allows the exploration of the larger solution space in order to avoid convergence in local minima. The algorithm is tested on simulated data as well as the DLR AIRMOD laboratory structure, and is found to provide accurate and robust heuristic solutions to large and complex solution spaces