Modelli ed esperimenti per la dinamica non lineare di dischi palettati con mistuning e contatti con attrito

L'abstract è presente nell'allegato / the abstract is in the attachmen

Aeroelastic modelling and control of very flexible air vehicles using a nonlinear modal formulation

We present the development of a nonlinear reduced-order formulation for the simulation of geometrically-nonlinear responses of flexible aircraft and other aeroelastic systems. The theoretical foundation of the formulation will be presented first, based on a modal projection of the intrinsic description for beams, coupled with a 2-D unsteady aerodynamic description. We will then investigate the preservation of conservation laws in the proposed method and develop the numerical details in a practical implementation of the method in MATLAB. In this work we also developed a method of obtaining coeffcients of the nonlinear modal beam equations by means of a condensation process, based on the direct application of Guyan reduction of a high-fidelity 3D FE model. Structural and aeroelastic simulations will be presented to verify the implementation of the method against theory and published results, as well as demonstrating the numerical properties of the approach. Static trim, stability analysis and open-loop nonlinear flight simulations using the framework will be demonstrated on a highly-flexible flying wing and compared with published results, as well as carrying out control design and closed-loop nonlinear simulations to demonstrate the capabilities of the proposed reduced-order method.Open Acces

The Twenty-First NASTRAN (R) Users' Colloquium

This publication contains the proceedings of the Twenty-First NASTRAN Users' Colloquium held in Tampa, FL, April 26 through April 30, 1993. It provides some comprehensive general papers on the application of finite elements in engineering, comparisons with other approaches, unique applications, pre-and postprocessing with other auxiliary programs and new methods of analysis with NASTRAN

구조 축소 기법과 인공신경 회로망을 이용한 유한요소 구조물의 모델 갱신 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계공학과, 2020. 8. 조맹효.Model updating methods for structural systems have been introduced in various numerical processes. To improve the updating method, the process must require an accurate analysis and minimized experimental uncertainties. Finite element model was employed to describe structural system. Structural vibration behavior of a plate model is expressed as a combination of the initial state behavior of the structure and its associated perturbations. The dynamic behavior obtained from a limited number of accessible nodes and their associated degrees of freedom is employed to detect structural changes that are consistent with the perturbations. The equilibrium model is described in terms of the measured and unmeasured modal data. Unmeasured information is estimated using an iterated improved reduction scheme. Because the identification problem depends on the measured information, the quality of the measured data determines the accuracy of the identified model and the convergence of the identification problem. The accuracy of the identification depends on the measurement/sensor location. We propose a more accurate identification method using the optimal sensor location selection method. Experimental examples are adopted to examine the convergence and accuracy of the proposed method applied to an inverse problem of system identification. Model updating methods for structural systems have been introduced in various fields. Model updating processes are important for improving a models accuracy by considering experimental data. Structural system identification was achieved here by applying the degree of freedom-based reduction method and the inverse perturbation method. Experimental data were obtained using the specific sensor location selection method. Experimental vibration data were restored to a full finite element model using the reduction method to compare and update the numerical model. Applied iteratively, the improved reduced system method boosts model accuracy during full model restoration; however, iterative processes are time-consuming. The calculation efficiency was improved using the system equivalent reduction-expansion process in concert with the proper orthogonal decomposition. A convolutional neural network was trained and applied to the updating process. We propose the use of an efficient model updating method using a convolutional neural network to reduce calculation time. Experimental and numerical examples were adopted to examine the efficiency and accuracy of the model updating method using a convolutional neural network. A more complex model is applied for model updating method and validated with proposed methods. A bolt assembly modeling is introduced and simplified with verified methodologies.구조 시스템에 대한 모델 갱신 방법이 다양한 해석에 도입되고 있습니다. 갱신 방법을 개선하려면 프로세스에 정확한 분석과 최소화된 실험적 불확실성이 필요합니다. 유한 요소 모델을 사용하여 구조 시스템을 구현했습니다. 평판 모델의 구조적 진동 거동은 구조의 초기 상태 거동과 그와 관련된 섭동의 조합으로 표현됩니다. 제한된 수의 가능한 위치와 그에 해당하는 자유도에서 얻은 동적 거동은 섭동과 일치하는 구조적 변화를 감지하는 데 사용됩니다. 등가 모델은 측정 및 측정되지 않은 모드 데이터의 관점에서 설명됩니다. 측정되지 않은 정보는 반복적 인 개선된 축소 기법을 사용하여 추정됩니다. 시스템 식별 문제는 측정된 정보에 의존하기 때문에 측정된 데이터의 정확도는 식별된 모델의 정확성과 식별 문제의 수렴성을 결정합니다. 시스템 식별의 정확성은 측정 및 센서의 위치에 따라 달라집니다. 최적의 센서 위치를 선정하는 방법을 사용하여, 보다 정확한 식별 방법을 제안합니다. 실험 예제는 시스템 식별의 역 해석 문제에 적용된 제안된 방법의 수렴성과 정확성을 조사하기 위해 선정되었습니다. 실험 데이터를 고려하여 모델의 정확성을 높이려면 모델 갱신 방법이 중요합니다. 여기서 자유도 기반 축소 기법과 역 섭동 방법을 적용하여 구조 시스템 식별을 수행했습니다. 센서 위치 선정 방법을 사용하여 양질의 실험 데이터를 얻을 수 있었습니다. 실험 모델과 해석 모델을 비교하고 갱신하기 위해 실험 데이터와 축소 기법의 변환행렬을 사용하여 전체 유한 요소 모델로 복원되었습니다. 반복적으로 적용되는 개선된 축소 기법은 전체 모델 복원 과정에서 모델의 정확도를 높여줍니다. 그러나 반복 계산으로 인해 시간이 많이 걸립니다. 적합 직교 분해와 함께 반복 계산이 필요 없는 자유도 축소 기법의 변환행렬을 사용하여 계산 효율을 향상시켰습니다. 합성 곱 인공 신경 회로망을 학습하여 모델 갱신 방법에 적용되었습니다. 본 연구를 통해 계산 시간을 줄일 수 있는 합성 곱 인공 신경 회로망을 사용하는 효율적인 모델 갱신 방법의 사용을 제안합니다. 합성 곱 인공 신경 회로망을 사용하는 모델 갱신 방법의 효율성과 정확성을 조사하기 위해 실험 및 수치 예제를 선정하고 검증했습니다. 또한 제안된 방법의 검증을 위해 보다 복잡한 모델이 모델 갱신 방법에 적용되었습니다. 검증된 방법을 볼트 결합 모델링에 도입하고 실험을 통한 모델 갱신으로 더욱 단순화된 모델링을 제안합니다.Chapter 1. Introduction 1 1.1 Frequency model updating method . 1 1.2 Reduction methods . 3 1.2.1 Degree of freedom-based reduction method 3 1.2.2 Iterated improved reduced system 4 1.2.3 Proper orthogonal decomposition 8 1.2.4 System equivalent reduction-expansion process 9 1.3 Structural system identification . 11 1.3.1 Balance equation for system identification . 15 1.3.2 Inverse perturbation method . 16 1.4 Machine learning in identification process . 20 Chapter 2. Sensor location selection method 21 2.1 Vibration test setup . 21 2.1.1 Vibration test setup for system identification 21 2.1.2 Vibration data rebuilt for in-house code . 22 2.2 Nodal point consideration . 26 2.2.1 Sequential elimination method 26 2.2.2 Energy method 27 2.2.3 Nodal point consideration 28 2.2.4 Numerical examples . 28 2.3 Sensor location selection method 32 Chapter 3. Residual error equation for identificataion process 36 3.1 Parameter optimizing equation setup 36 3.2 Convergence criterion . 38 3.3 Weighting factor for parameter evaluation 39 3.4 Identification examples 42 Chapter 4. Convolutional neural networks-based system identification method 54 4.1 Introduction . 54 4.2 The balance equation of the model updating method . 57 4.2.1 The IPM method 58 4.2.2 The DOF-based reduction method 59 4.2.3 Experimental data for the model updating method 63 4.3 Convolutional neural network-based identification 67 4.3.1 The SEREP and POD . 67 4.3.2 The 2D-CNN 72 4.4 Experimental examples 77 Chapter 5. A model updating of complex models 94 5.1 The model updating and digital twin . 94 5.2 A complex model example 95 5.2.1 The tank bracket model 95 5.2.2 The sensor location selection 98 5.3 The bolt joint assembly simplification . 102 Chapter 6. Conclusion 109 Appendix A. Structural design of soft robotics using a joint structure of photo responsive polymers 113 A.1 Overview 113 A.2 Structural desing of soft robotics . 114 A.3 Experimental setup 117 A.3.1 Systhesis process 117 A.3.2 Sample preparation 118 A.3.3 Spectrometer characterization 118 A.4 Structural modeling . 121 A.4.1 Multiscale mechanincs 121 A.4.2 Nonlinear FEM with a co-rotational formulation 123 A.5 Results and discussion 128 A.6 Summary of Appendix A 142 Bibliography 145 Abstract in Korean 158Docto

Response Transmissibility for Load Identification Improved By Optimal Sensor Locations

A knowledge of loads acting on a structure is important for analysis and design. There are many applications in which it is difficult to measure directly the dynamic loads acting on a component. In such situations, it may be possible to estimate the imposed loads through a measurement of the system output response. Load identification through output response measurement is an inverse problem that is not only ill-conditioned, but in general leads to multiple solutions. Therefore, additional information, such as number and locations of the imposed loads must be provided ahead of time in order to allow for a unique solution. This dissertation focuses on cases where such information is not readily accessible and presents a method for identification of loads applied to a structure using the concept of response transmissibility. The solution approach is divided into two phases that involve finding the number and location of forces first followed by a reconstruction of the load vector. To achieve the first phase, a complete description of the structure in terms of degrees of freedom needs to be specified and a numerical model, usually a finite element model is built. In order to determine the number of forces and their locations, the proposed algorithm combines the dynamic responses measured experimentally along with the transmissibility matrices obtained from the numerical model. Once the number of loads and their locations are known, a regeneration of the load vector is achieved during the second phase by combining the measured dynamic responses with the transmissibility matrix from the numerical model. In this dissertation, identification of loads through measurement of structural response at a finite number of optimally selected locations is also investigated. Optimum sensor locations are identified using the D-optimal design algorithm. Two different types of measurements are considered, acceleration measurements using accelerometers and the strain measurements using strain gages. A series of simulated results on multi-degree of freedom (MDOF) discrete and continuous systems are presented to illustrate the load identification technique based on response transmissibility. One of the factors that affects the accuracy of load reconstruction is the number of vibration modes included in the analysis, which can be a large number. Improvements using model order reduction, not only help reconstruct the input forces accurately, but it also reduces the computational burden significantly. The developed algorithms are implemented using the finite element tool ANSYS in conjunction with MATLAB software. Numerical sensitivity analysis is also implemented to examine the effect of presence of uncertainties (noise) in experimental data. The results obtained confirm that the techniques presented are robust even in the presence of simulated noise; it is seen that the applied loads are recovered accurately

Identification and synthesis of components for uncertainty propagation

For automotive structures, built-up of hundreds of components with property spread, knowing the effects of component variability and its propagation through the system assembly is important in order to mitigate noise and vibration problems. To increase the understanding of how the spread propagates into variability in built-up structures, both experimental and computational aspects are considered in this thesis.In the first part of the thesis, methods to identify models from experimental data are developed. Physical insight is often required for accurate experimental models. To this end, two-phase state-space system identification algorithms are developed where physically motivated residual states are included and physically motivated constraints are enforced. The developed identification algorithms are used together with finite element model updating to investigate the variability in dynamical properties between nominally identical components. Furthermore, the accurate and physical experimental models are used in synthesis with the updated finite element models. It is shown that experimental-analytical synthesis of complex and modally dense structures is possible, and that the component variability can be predicted in such assemblies.In the second part of the thesis, methods to reduce the computational cost of variability analysis are developed. An efficient multifidelity interface reduction method is developed for component synthesis. It is also shown that modal truncation augmentation vectors can be computed efficiently from the multifidelity interface reduction basis. Lastly, an efficient uncertainty propagation method is developed, based on a second-order modal model. Utilising several approximations, it is shown that industrial-sized models can be handled with small loss in accuracy compared to a purely Monte Carlo based approach

Data-driven system identification of strongly nonlinear modal interactions and model updating of nonlinear dynamical systems

Experimental measurements are fundamental for the calibration and validation of computational models. When a model fails to reproduce measurements, engineers must identify and incorporate the unmodeled and/or uncertain dynamics to reconcile theoretical prediction and experimental observation. While linear identification tools are well-established, practicing engineers face significant barriers when identifying and constructing reduced-order models of the dynamics of nonlinear dynamical systems; the reason is that, typically, nonlinearities introduce new dynamical phenomena that have no counterparts in linear settings. This dissertation focuses on a recently developed, data-driven nonlinear system identification and reduced-order modeling methodology that enables one to detect, characterize and model system nonlinearity using existing computational models and experimental data. This task necessitates the synergistic implementation of diverse theoretical, computational and experimental techniques such as multiple-scale and averaging approximations, resonance capture analyses, empirical mode decomposition, wavelet and Hilbert transforms, and experimental modal analysis. The first portion of this dissertation concerns the development of an advanced signal decomposition procedure, termed wavelet-bounded empirical mode decomposition, and considers applications to the detection of strongly nonlinear modal interactions that populate the dynamics of a cantilever beam with local stiffness nonlinearity and that of a linear oscillator coupled to a vibro-impact nonlinear energy sink (i.e., a strongly nonlinear broadband absorber). The second portion examines the physical underpinnings of the proposed methodology for detecting (even strongly) nonlinear interactions in the form of internal resonances in the measured time series caused by nonlinear modal energy exchanges. Using as an example the dynamics of a cantilever beam supported by a local smooth nonlinearity, the theoretical predictions are validated by experimental measurements and post-processing analysis. The third portion focuses on the identification of a nonlinear energy sink connected to a model airplane wing; the respective theoretical and computational models are updated to accurately capture the nonlinear effects, as indicated by comparison with the measured data. The final portion considers the global effects induced by local lightweight nonlinear attachments by examining the dynamics of a model airplane with a nonlinear stores installed on each wing. The stores are found to induce significant changes in the global dynamics of the plane even though they are local attachments, including strongly nonlinear energy exchanges facilitated by internal resonances between the modes of the plane

Nonlinear modal analysis for blisks with friction dampers

Dynamic analyses of nonlinear systems have become an important topic in the field of turbomachinery. In industrial turbomachinery, the friction damping is regarded as the major damping source compared to the aerodynamic damping and material damping. Contact with friction is a type of common non-conservative and non-smooth nonlinearities. The existence of various friction joints in industrial turbomachinery makes the dynamic analysis complicated. The definition of the damped Nonlinear Normal Modes (dNNMs) and various numerical approaches facilitate the infrastructure of nonlinear modal analysis for structures with friction dampers. However, the relations between dNNMs and resonant solutions in forced responses cannot be directly addressed. In this context, the purpose of the present thesis is (i) to evaluate the performance of the existing numerical approaches for computing the dNNMs of systems with non-conservative nonlinearities; (ii) to develop a new method to directly relate the dNNMs to the resonant solutions in forced response for such system; and (iii) to study the geometric influence of the friction ring dampers on their dynamic responses and damping performances. The first contribution of the present thesis is to provide a comprehensive comparison of the existing numerical approaches, including Complex Nonlinear Mode (CNM) and Extended Periodic Motion Concept (EPMC). EPMC with artificial hysteretic damping is firstly attempted and compared with the original EPMC and CNM. The advantages and limitations of each method are critically discussed. The second contribution is the development of the Extended Energy Balance Method (E-EBM), which is a numerical method used to efficiently predict the resonances using the dNNMs of a non-conservative nonlinear system. This E-EBM can be simply applied to both CNM and EPMC approaches. The third contribution is the investigation the damping performance of friction ring dampers for blisk structures, especially the geometric effects of the ring dampers. The geometric design of the ring dampers is achieved by using kriging meta-modelling to predict the dNNMs. Useful advice is proposed for the future design of friction ring dampers.Open Acces

Identification of Blade-root Joint Dynamics in Turbine Disks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Sixth NASTRAN (R) Users' Colloquium

Papers are presented on NASTRAN programming, and substructuring methods, as well as on fluids and thermal applications. Specific applications and capabilities of NASTRAN were also delineated along with general auxiliary programs