Prediction Of Dynamic Response Research Articles

MBSNet: A deep learning model for multibody dynamics simulation and its application to a vehicle-track system

In multibody dynamics simulation (MBS) analysis, researchers usually face three challenges: high modeling difficulty, large calculation amount, and restricted solver. Therefore, it is of interest to explore a new technology that can solve or avoid one or more of these challenges. This article first explores how to use deep learning (DL) to realize dynamics simulation of a multibody system and contributes to the modeling and analysis of MBS in two meaningful aspects: (1) Based on the 3D convolutional neural network (3DCNN), long short-term memory(LSTM), and fully connected network (FCNN), we develop a DL network called MBSNet, which considers the relationship between system variables and disturbance variables of a general multibody system, to realize MBS analysis. (2) Based on the short-term MBS results of a vehicle-track vertically coupled dynamics model, we train MBSNet and apply it to long-term system dynamic predictions. The comparison between the MBSNet result and the MBS result shows that MBSNet has high robustness in the face of different track irregularities and can accurately and quickly achieve long-term predictions of low-frequency dynamic responses. Finally, the evidence from the vehicle-track case shows that MBSNet has the potential to be used in MBS analysis, but it faces some challenges, including inaccurate prediction of high-frequency impact components caused by the coupled motion of bodies in a multi-degree-of-freedom system, improper training strategy, insufficient working conditions and samples considered during training, etc.

Prediction of dynamic responses in a rectangular beam using the modal expansion method

Structures and equipment subject to dynamic loading are prone to a shortened life span due to excessive vibration levels, which can lead to fatigue failure of its components. Continuous monitoring of those systems can be a complicated and expensive task, due to the complexity and difficulty of accessibility to some locations, which makes it difficult to assess the structural integrity. One way to deal with this subject is to use finite element model, operational modal analysis, and the modal expansion method to predict dynamic responses in locations that have not been measured. In this work, a calibrated modal model of a rectangular aluminum beam suspended in air, in free–free boundary condition, was used to predict acceleration responses. The experimental modal matrix was obtained through impact tests and the system equivalent reduction expansion process (SEREP) technique was used to reduce the beam’s Finite Element model, thus ensuring the compatibility between numerical and experimental degrees of freedom. Model calibration was carried out using the local correspondence principle. Finally, by using the modal expansion method, the calibrated modal model was used to predict dynamic responses. Results showed high accuracy between the measured and the predicted acceleration signals.

Efficient characterization of dynamic response variation using multi-fidelity data fusion through composite neural network

Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.

Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs

Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house programDARwindand the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. SADA weights KDPs by DDPG algorithms' actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing theDARwindsimulation results and that of experimental data. Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligentDARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform's motions can be predicted by AI-basedDARwindwith higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learning method, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.

Residual learning of the dynamics model for feeding system modelling based on dynamic nonlinear correlate factor analysis

Feeding system modelling is the foundation for control strategy optimization, contour error compensation, etc., to improve the productivity and quality of a part. This paper proposes a novel residual learning approach for fitting the simulation error of the dynamics model of a machine tool feeding system. Then, the feeding system model consisting of the dynamics model and the residual model is constructed by integrating prior knowledge with statistical learning knowledge. The residual model is trained by using the training dataset generated from the dynamics model instead of using only the input and output data (i.e., the end-to-end data). In addition, a dynamic nonlinear correlate factor extraction method is proposed to extract the training dataset from the dynamics model and the reference data. Compared to the end-to-end data, the training dataset knows very well about the system’s nonlinear features owing to the internal prior knowledge of the dynamics model. Experiments conducted on a vertical milling centre confirm the effectiveness of the feeding system model in dynamic response prediction. Compared to the existing dynamics or data-driven modelling method, the proposed method can achieve higher prediction accuracy in nonlinear motion processes, such as the reverse process, and can obtain stable performance with respect to different feedrates owing to the residual learning approach.

A frequency and velocity-dependent impedance method for prediction of rail/foundation dynamics

This paper presents an efficient numerical tool for the prediction of railway dynamic response. A behavior calibration of the infinite Euler-Bernoulli beam resting on continuous viscoelastic foundation is proposed. Constitutive laws of the discrete elements are determined for a rectilinear ballasted track. A three-dimensional model coupled with an adaptive meshing scheme is employed to calibrate the beam model impedances by finding the similarity between the output signals using the genetic algorithm. The model shows an important performance with significant reduction in computational effort. This study emphasizes the major impact of the excitation characteristics on the parameters of the discrete models.

An equivalent model of a nonlinear bolted flange joint

The dynamic response of individual components in an assembled structure shows high accuracy compared to experimental measurements of the system response. However, when it comes to assemblies, the conventional linear approaches fail to deliver good accuracy, due to the uncertain linear and nonlinear mechanisms in the contact interface of the joints. Therefore, the inherent dynamics of the contact interfaces needs to be considered in modeling assembled structures. In this paper the prediction of the nonlinear dynamic response in a bolted flange joint was obtained in two ways. First, a 3D detailed finite element model capable of representing the micro-slip mechanism was made using a quasi-static time stepping analysis. The linear characteristics and nonlinear mechanisms developing in the contact interface of a bolted joint are investigated by using the 3D detailed model. Moreover, the natural frequencies of the assembled structure (representing the linear response) and the micro-slip behavior in terms of hysteresis loops (representing the nonlinear response) are obtained using the detailed model. Second, an equivalent model composed of beam elements and an appropriate joint model is then constructed for the assembled structure. An identification approach is proposed, and the parameters of the joint model are identified using both linear and nonlinear characteristics, i.e. natural frequencies and hysteresis loops. Comparing the hysteresis loops obtained from the detailed and equivalent models verifies the accuracy of the joint model used to represent the contact interface and the identification approach proposed for parameter quantification.

Realtime prediction of dynamic mooring lines responses with LSTM neural network model

A Long Short-Term Memory (LSTM) neural network model is developed to provide a real-time calculation tool for monitoring the mooring line responses under the operating condition by using the vessel motion as the only input. A feature extraction method based on first order moment and second order center moment is proposed in the data pre-processing to improve the characteristics of fluctuation information of input data. The effective judging standard of the scope of prediction accuracy regarding the proposed neural network model is defined. The impact of normalization method, neural number, and training sets length on the predicting accuracy are studied. The feasibility of the established LSTM neural network model is verified considering different data relevance between the training sets and validating sets. The results indicate that the mooring line responses can be predicted with high precision based on the vessel motion as input by using the established LSTM neural network model.

Theoretical predictions of dynamic responses of cylindrical sandwich filled with auxetic structures under impact loading

Sandwich structures have superior mechanical properties to be applied in civil, marine, mechanical and aerospace engineering fields. Auxetic structures with negative Poisson's ratio (NPR) can contract laterally when compressed axially to be excellent fillers for the sandwich structures to improve the energy absorption performance. In this work, a cylindrical sandwich filled with double arrowed auxetic structure is proposed. An analytical model is established to predict the dynamic responses of the sandwich structure under axial impact loading. The theoretical model is composed of three parts: energy dissipated due to the auxetic filler, the thin walled tubes, and their interaction. Numerical simulations are performed to validate the theoretical model and the results indicate that the theoretical model proposed is accurate to predict the crushing platform stress of the cylindrical sandwich under axial impact loading.

A new phenomenon in interference effect on catamaran dynamic response

Interference effect between catamaran demi-hulls has been considered as an adverse fluid-solid interaction; the narrower separation distance between the two hulls, the greater drag force acts upon the catamaran. However, this study is aimed at introducing a novel phenomenon contrasting outcome in semi-planing and planing regimes. As speed and accordingly the Froude number increase the catamaran transits across different modes, free surface profile becomes complicated and so does the interference effect as well as the prediction of catamaran dynamic response. The situation will be even more complex if demi-hull separation narrows where nonlinearity in interference effect is more pronounced. So, free-running tests in six degrees of freedom as well as obtaining fluid flow characteristics using computational fluid dynamics were conducted for a high-speed catamaran vehicle with asymmetric non-prismatic demi-hulls to tackle the problem. The results of catamaran dynamic response reveal that not only is drag reduced substantially up to 15%, but also trim angle diminishes by 30% as hulls separation distance decreases in semi-planing and planing modes. In other words, with increasing speed, and decreasing trim angle by 2.3o, the current method achieved the goal of consequently reducing the risk of porpoising instability, without the increase of delivered power.

Predicting On-axis Rotorcraft Dynamic Responses Using Machine Learning Techniques

Physical-law-based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Owing to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to errors in the model's predictions of the real vehicle's response. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions. Machine learning was facilitated using a Gaussian process (GP) nonlinear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate, and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large datasets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the model input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the sparse GP model predictions.

An integrated macroelement formulation for the dynamic response of inelastic deformable rocking bodies

SummaryThis paper presents a macroelement formulation for the prediction of the planar dynamic response of inelastic deformable rocking bodies. The formulation is based on a previous macroelement developed by the authors able to describe the cyclic response of inelastic rocking bodies, which takes into account the deformability both along the height of the member, as well as near the rocking end. Modifications of this formulation to account for other motion modes of rocking members during their dynamic response, namely, sliding and upthrow, as well as modifications to account for damping in a uniform manner during the whole motion, including impacts, are introduced. The dynamic response predicted by the macroelement for free‐standing rigid and deformable rocking bodies is presented and compared with existing theoretical solutions, and the effect of deformability, damping, inelasticity, and friction on the response is discussed.

Generalized Modal Amplitude Stability Analysis for the prediction of the nonlinear dynamic response of mechanical systems subjected to friction-induced vibrations

The numerical prediction of the dynamic behaviour of mechanical systems subjected to friction-induced vibrations is still a tedious problem. Different methodologies exist nowadays to study it. The first one is the complex eigenvalue analysis, which is widely used by the scientists and the industrials to predict the appearance of instabilities despite its disadvantages. Other methodologies, namely temporal integration and frequential approaches, have been developed to determine the transient and/or the steady-state response to assess the history of the dynamic response, and so to identify the unstable modes involved in the nonlinear dynamic response as well as the vibration levels. However, because of their complex implementation, their high numerical cost and sometimes the strong assumptions made on the form of the solutions, these methods are not widely and currently used in industry. To cope with the limitations of the CEA, namely the over- or under-predictability and the lack of information about modal participations in the nonlinear dynamic response, developing complementary tools is necessary. Thus, this paper is devoted to the extension and generalization of a nonlinear approach, called the modal amplitude stability analysis, to the multi-instability case. The method, called the Generalized Modal Amplitude Stability Analysis (GMASA), allows to identify the evolutions and contributions of unstable modes involved in the nonlinear self-sustaining vibration response and to estimate the limit cycles. The method is applied on a phenomenological system for which it is easy to provide an understanding of the unstable mode(s) contribution to the nonlinear dynamic response of the system and for which the calculations can be performed with reasonable computational times. Thus, the efficiency and validity of the GMASA approach are investigated by comparing the GMASA results with those of the reference results based on temporal approach.

Dynamic Response and Frequency Split Predictions for Cylindrical Fused Silica Resonators

An investigation into the dynamic response and frequency split behavior of a cylindrical fused silica resonator is performed via simulations and experiments. For this purpose, a structure for the cylindrical resonators with appropriate dimensions has been designed and fabricated. A laser Doppler vibrometer is utilized for the experimental quantification of the physical characteristics of the resonator. Natural frequencies of n = 2 modes, frequency split and quality factor have been extracted from the measured frequency sweep. Then the influence of mass change on the frequency split is investigated by chemical trimming. Time response predictions utilizing a ring model is employed to predict the resonant amplitudes as well as the influence of driving force and input angular velocity variations. The numerical predictions of the relationship between the mass mismatch and the frequency split are found to be consistent with the exper imental results. Predictions of frequency split of resonators via the simulation model are envisaged to greatly increase the efficacy of the chemical trimming process.

Dynamic Characteristics of an Offshore Wind Turbine with Tripod Suction Buckets via Full-Scale Testing

The dynamic characteristics of an offshore wind turbine with tripod suction buckets are investigated through finite element analysis and full-scale experiments. In finite element analysis, an integrated framework is suggested to create a simple yet accurate high fidelity model. The integrated framework accounts for not only the strain dependency of the soil but also for all dynamics in the seabed, including those of the soil, suction bucket skirt, and cap. Hence, the model accurately describes the coupling effect of translational and rotational motions of the seabed. The prediction results are compared to the experimental results obtained via full-scale testing in four stages during construction and in several operational conditions. The comparison shows that the stiffness of the suction bucket cap and strain dependency of the soil play a significant role in predicting natural frequency, suggesting that these two factors should be considered in finite element analysis for the accurate prediction of dynamic responses of an offshore wind conversion system. Moreover, dynamic analysis of the strain and acceleration measured during operational conditions shows that strain is more robust than acceleration with regard to the characterization of the overall dynamics of an offshore wind conversion system because the natural frequency of an offshore wind turbine is very low. It can be inferred that the measurement of strain is a more effective way to monitor the long-term evolution of dynamic characteristics. The suggested integrated framework and measurement campaign are useful not only to avoid conservatism that may incur additional costs during load calculation and design phases but also to establish an intelligent operation and maintenance strategy with a novel sensing technique.

Dynamic Response Prediction of Underwater Explosion Vessels

In order to ensure the safety of the underwater explosive vessel in service, it is necessary to analyse the dynamic response of the underwater explosive vessel. The dynamic response model based on Classification and Regression Tree is established to simulate the mapping relationship between load and container strain. A reliable and stable model is obtained by cross validation. At the same time, compared with Multi-Layer Perceptron model, CART model is faster and more accurate in training, and the prediction effect is obviously better than Multi-Layer Perceptron model, which further explains the validity of CART model and provides a better method for dynamic response prediction of container.

Strain Gage Ramifications on Mistuning in As-Manufactured Models and Experimental Testing

Abstract Blade-mounted strain gages are vital during rig and engine development to ensure safe engine operation. However, they also enable a change in dynamics of integrally bladed rotors (IBRs). State-of-the-art IBR dynamic response predictions are accomplished using as-manufactured models (AMMs) generated via optical topography measurements and mesh morphing. Two AMM finite element models (FEMs) are created of a 20-bladed IBR. One FEM has no strain gages present, where the second FEM includes strain gages on six blades. Traditionally, strain gages and lead wires are treated as the same material property as the IBR itself. It will be shown that the inclusion of strain gages in AMM's using this method changes the IBR's predicted mistuning. An alternative AMM approach is developed that changes the material properties of the finite elements attributed to the strain gages. The predicted mistuning for each AMM is accomplished using the fundamental mistuning model identification (FMM ID), where the predicted mistuning will be compared to both traveling wave excitation (TWE) experiments and a rotating, single stage compressor rig. Findings show mistuning predictions of the nonstrain gaged AMM compare far better to the experiments compared to the inclusion of the strain gages in the AMM. Additionally, altering material properties of the strain gages in the AMM improve mistuning prediction compared to treating the strain gages as the parent IBR material. Therefore, AMM should be acquired using clean, nonstrain gaged rotors or the material properties of strain gaged elements need to be altered to more accurately model the component.

Abstract OT1-03-02: Combining MRI and liquid biopsies for prediction of response to neoadjuvant chemotherapy in early stage breast cancer: The LIMA study

Abstract Background The response to neoadjuvant chemotherapy (NAC) in early stage breast cancer has important prognostic implications1-3. Early, dynamic prediction of response allows for adaption of the treatment plan before completion, or even before the start of treatment. This strategy can help prevent overtreatment and related toxicity and correct for undertreatment with an ineffective regimen. We hypothesize that accurate dynamic response prediction may be reached by combining multi-parametric MRI with liquid biopsies prior to, during and after NAC, in addition to conventional clinical and pathological information. Magnetic resonance imaging (MRI) is non-invasive and is typically used for response evaluation in current clinical practice. It shows the size and perfusion of the tumor as they change during treatment. However, tumor size on MRI has limited predictive value for response to therapy4. Multi-parametric MRI uses different imaging protocols in one session to measure more functional items than perfusion alone, addressing different aspects of tumor biology, and possibly improving predictive value. With this improvement, imaging still only visualizes macroscopic disease. Therefore, in the LIMA study, MRI will be combined with liquid biopsies containing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), which have both shown prognostic and predictive values in early stage breast cancer5-7. Since the ctDNA may originate from cells in every part of the tumor, it may capture tumor heterogeneity. Liquid biopsies are minimally invasive and provide insight into microscopic tumor load and the tumor’s genetic picture. There is an abundance of clinical studies investigating the predictive value of liquid biopsies and MRI. On breast cancer however, no studies have been published that combine these two modalities. Aim show proof of concept for combining multi-parametric MRI with liquid biopsies in addition to conventional clinical and pathologic information, to accurately predict response to NAC at different time points. Trial design A multicenter prospective observational cohort study. Multi-parametric MRI will be performed prior to NAC, halfway and after completion of NAC. Liquid biopsies will be obtained before start of treatment, every 2 weeks during treatment and after completion of NAC. Statistical methods The primary endpoint is Residual Cancer Burden (RCB) in the surgical resection specimen after NAC. Collected data will be analyzed with efficient statistical methods dedicated to sparse datasets primarily using penalized regression techniques. Eligibility criteria Non-pregnant, non-lactating women ≥18 years of age with non-metastatic, histologically proven invasive breast cancer who are planned to be treated with NAC (and in case of a HER2-positive tumor: addition of trastuzumab and/or pertuzumab) who have: - no luminal A breast cancer - no inflammatory breast cancer - no other active malignant disease in the past 5 years (excluding squamous cell or basal cell carcinoma of the skin) -no contra-indications for MRI or gadolinium-based contrast agents Target accrual 100 patients (present: 0) References 1von Minckwitz 2012 J Clin Oncol; 2Cortazar 2014 Lancet; 3Symmans 2007 J Clin Oncol; 4Gu 2017 Clin Breast Cancer; 5Bidard 2018 J Natl Cancer Inst; 6Garcia-Murillas 2015 Sci Transl Med 7Magbanua 2018 SABCS p. Abstract No. PD2-01 Acknowledgements Funding from the European Union Horizon 2020 research and innovation program under grant agreement no. 755333 (LIMA). In collaboration with Philips, INSERM, ICM, Agena Bioscience, ANGLE, Stilla Technologies, DiaDx and ALS Automated Lab Solutions. Citation Format: Liselore Maria Janssen, Britt Berendine Maria Suelmann, Markus Hendrik Adriaan Janse, Sjoerd Geert Elias, Paul J van Diest, Wouter Bernard Veldhuis, Elsken van der Wall, Kenneth George Antonius Gilhuijs. Combining MRI and liquid biopsies for prediction of response to neoadjuvant chemotherapy in early stage breast cancer: The LIMA study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT1-03-02.

Dynamic response prediction of water medium explosion container based on convolutional neural network

The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel.

Prediction of dynamic responses of FSRU-LNGC side-by-side mooring system

Floating Storage and Regasification Unit (FSRU) becomes one of the most popular equipment in the industry for providing clean energy because of its technical, economic and environmental features. The interaction between the FSRU and Liquified Natural Gas Carrier (LNGC) under the combined loads from wind, wave and current is quite complex to model. In this paper, a configuration for the offloading operation of the FSRU-LNGC side-by-side mooring system is proposed to predict the motion responses, forces on the cables and fenders of the multi-floating mooring system. The damping lid method is adopted to improve the overestimated hydrodynamic coefficients calculated from conventional potential flow theory in the frequency domain. The dynamic response of the side-by-side mooring system including six degrees of freedom motion, relative motions, cable tensions and fender forces are provided and analyzed. The numerical results are validated using the experimental data. The proposed coupled analysis model and the numerical analysis can properly predict the dynamic response of the multi-floating mooring. The sensitivity analysis of pretension of the connecting cables on the dynamic responses of the two vessels are provided. Moreover, the non-dimensional damping parameters can be acted as a good reference to the dynamic response analysis of similar multi-floating mooring systems.