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Metamaterials have recently emerged and shown great potential for noise and vibration reduction in specific frequency ranges, called stop bands. To predict stop bands, their often periodic nature is exploited and dispersion curves are calculated based on a single representative unit cell, typically modeled using the finite element method. Since their sub-wavelength nature and often intricate design can lead to large unit cell models, model reduction methods such as the Generalized Bloch Mode Synthesis have been proposed to greatly accelerate dispersion curve calculations. In order to calculate forced vibro-acoustic responses of finite periodic elastic metamaterial plates composed of an assembly of unit cells, however, full order finite element models rapidly become computationally unaffordable. Therefore, in this work the Generalized Bloch Mode Synthesis is incorporated in a sub-structuring approach, which enables fast forced vibration response calculations of finite elastic metamaterial plates based on a single reduced order unit cell model. The main advantage as compared to a regular Craig-Bampton approach is the additional local reduction of unit cell boundary degrees of freedom, whereby a compatible basis for the identical neighboring unit cells is incorporated. In addition, by combining this Generalized Bloch Mode Synthesis based sub-structuring approach with the Elementary Radiator Approach, efficient sound transmission loss computations of finite periodic metamaterial plates are enabled. The performance of the proposed approach for fast vibro-acoustic response predictions is demonstrated for different cases.

Over the years, metamaterials have shown their potential in a wide range of different disciplines, e.g. optics, electromagnetics, dynamics etc. Metamaterials are, often periodic, engineered structures made of conventional materials but which exhibit properties not encountered in nature. In the field of noise and vibration, metamaterials have received increasing interest since they can obtain frequency ranges of high noise and vibration attenuation, called stop bands. Their performance is often investigated by means of dispersion curves, which are calculated based on a single unit cell and assuming a structure of infinite periodic extent. Nowadays, the attenuation of acoustic and structural waves is commonly tackled as two separate problems, whereby either acoustic or structural dispersion curves are used. Recently, vibro-acoustic unit cell designs have come to the fore which can exhibit appealing characteristics, such as simultaneous structural and acoustic stop bands. To consider the vibro-acoustic coupling in these unit cell designs during the performance predictions, vibro-acoustic dispersion curve calculations are thus required. However, these computations are typically cumbersome to perform due to the associated high computational cost and therefore, often, uncoupled dispersion curves are used during the performance assessment. Although several unit cell model order reduction approaches have recently been proposed to accelerate the dispersion curve computations, such as the Bloch mode synthesis (BMS) and Generalized Bloch mode synthesis (GBMS), they are not readily applicable to vibro-acoustic unit cells. To accelerate vibro-acoustic dispersion curve calculations, this work extends the BMS and GBMS techniques towards 2D and 3D periodic vibro-acoustic systems. To balance accuracy versus speed, the extended BMS reduction basis is constructed using a split set of vibro-acoustic coupled modes, while the extended GBMS reduction basis uses the uncoupled modes. Several verification cases demonstrate that strongly accelerated vibro-acoustic dispersion curve computations are achieved whereby the vibro-acoustic coupling inside the unit cell is accurately accounted for.

Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).

Objective: The level of primary implant fixation in cementless total hip arthroplasty is a key factor for the longevity of the implant. Vibration-based methods show promise for providing quantitative information to help surgeons monitor implant fixation intraoperatively. A thorough understanding of what is driving these changes in vibrational behavior is important for further development and improvement of these methods. Additionally, an instrument must be designed to enable surgeons to leverage these methods. This study addresses both of these issues. Method: An augmented system approach was used to develop an instrument that improves the sensitivity of the vibrational method and enables the implementation of the necessary excitation and measurement equipment. The augmented system approach took into account the dynamics of the existing bone-implant system and its interaction with the added instrument. Results: Two instrument designs are proposed, accompanied by a convergence-based method to determine the insertion endpoint. The modal strain energy density distribution was shown to affect the vibrational sensitivity to contact changes in certain areas. Conclusion: The augmented system approach led to an instrument design that improved the sensitivity to changes in the proximal region of the combined bone-implant-instrument system. This fact was confirmed both in silico and in vitro. Clinical Impact: The presented method and instruments address practical intraoperative challenges and provide perspective to objectively support the surgeon’s decision-making process, which will ensure optimal patient treatment.

The Digital Twin (DT) is a promising concept which aims at creating a virtual replica of an individual system, that can provide information otherwise not available. This paper presents a DT of a vertical transportation system, focusing on how to model this DT, and using it to evaluate the system condition and potential corrective solutions. Exploiting the advantages of object-oriented modeling, this DT has been built ensuring that it may be dynamically adapted to different monitoring scenarios. Moreover, its adaptability is tested showing that reduced models behave similarly. The resulting reduced models are used to estimate installation parameters in a bottom-up way, using the measurements of a scaled test bench. The estimated parameters are used to update a high-fidelity model and simulate the effect of corrective actions.

In cementless Total Hip Arthroplasty (THA), achieving high primary implant fixation is crucial for the long-term survivorship of the femoral stem. While orthopedic surgeons traditionally assess fixation based on their subjective judgement, novel vibration-analysis fixation-monitoring techniques show promising potential in providing the surgeon with objective and quantifiable fixation measurements. This study presents a dynamic response measurement protocol for implant endpoint insertion and evaluates this protocol in the presence of artificial soft tissue. After the artificial femur was prepared in accordance with the THA protocol, the implant was inserted and progressively hammered into the cavity. The Pearson Correlation Coefficient (PCC) and Frequency Response Assurance Criterion (FRAC) corresponding to each insertion hammer hit were derived from the Frequency Response Functions (FRF) corresponding to each insertion step. The protocol was repeated with the artificial femur submerged in artificial soft tissue to imitate the influence of anatomical soft tissue. The FRAC appeared overall more sensitive than the PCC. In the presence of the artificial soft tissue the technique yielded higher PCC and FRAC values earlier in the insertion process. The measurements with artificial soft tissue produced FRFs with fewer peaks, lower resonance frequencies, and overall higher damping factors. The soft tissue appears to limit the fixation-change detection capabilities of the system and a promising potential remedy to this limitation is suggested.

Sparsity-based methods have recently come to the foreground of damage detection applications posing a robust and efficient alternative for traditional approaches. At the same time, low-frequency inspection is known to enable global monitoring with waves propagating over large distances. In this paper, a single sensor complex Group Lasso methodology for the problem of structural defect localization by means of compressive sensing and complex low-frequency response functions is presented. The complex Group Lasso methodology is evaluated on composite plates with induced scatterers. An adaptive setting of the methodology is also proposed to further enhance resolution. Results from both approaches are compared with a full-array, super-resolution MUSIC technique of the same signal model. Both algorithms are shown to demonstrate high and competitive performance.

Torsional vibrations play a critical role in the design and operation of a mechanical or mechatronic drivetrain due to their impact on lifetime, performance, and cost. A magnetic spring allows one to reduce these vibrations and improve the actuator performance yet introduces additional challenges on the identification. As a direct torque measurement is generally not favourable because of its intrusive nature, this paper proposes a nonintrusive approach to identify torsional load profiles. The approach combines a physics-based lumped parameter model of the torsional dynamics of the drivetrain with measurements coming from a motor encoder and two MEMS accelerometers in a combined state/input estimation, using an augmented extended Kalman filter (A-EKF). In order to allow a generic magnetic spring torque estimation, a random walk input model is used, where additionally the angle-dependent behaviour is exploited by constructing an angle-dependent estimate and variance map. Experimental validation leads to a significant reduction in bias in the load torque estimation for this approach, compared to conventional estimators. Moreover, this newly proposed approach significantly reduces the variance on the estimated states by exploiting the angle dependency. The proposed approach provides knowledge of the torsional vibrations in a nonintrusive way, without the need for an extensive magnetic spring torque identification. Further, the approach is applicable on any drivetrain with angle-dependent input torques.

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.

Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.

Replicate bones are widely used as an alternative for cadaveric bones for in vitro testing. These composite bone models are more easily available and show low inter-specimen variability compared to cadaveric bone models. The combination of in vitro testing with in silico models can provide further insights in the evaluation of the mechanical behavior of orthopedic implants. An accurate numerical representation of the experimental model is important to draw meaningful conclusions from the numerical predictions. This study aims to determine the elastic material constants of a commonly used composite clavicle model by combining acoustic experimental and numerical modal analysis. The difference between the experimental and finite element (FE) predicted natural frequencies was minimized by updating the elastic material constants of the transversely isotropic cortical bone analogue that are provided by the manufacturer. The longitudinal Young’s modulus was reduced from 16.00 GPa to 12.88 GPa and the shear modulus was increased from 3.30 GPa to 4.53 GPa. These updated material properties resulted in an average natural frequency difference of 0.49% and a maximum difference of 1.73% between the FE predictions and the experimental results. The presented updated model aims to improve future research that focuses on mechanical simulations with clavicle composite bone models.

The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.

The total number of total hip arthroplasties is increasing every year, and approximately 10% of these surgeries are revisions. New implant design and surgical techniques are evolving quickly and demand accurate preclinical evaluation. The initial stability of cementless implants is one of the main concerns of these preclinical evaluations. A broad range of initial stability test methods is currently used, which can be categorized into two main groups: Load-to-failure tests and relative micromotion measurements. Measuring relative micromotion between implant and bone is recognized as the golden standard for implant stability testing as this micromotion is directly linked to the long-term fixation of cementless implants. However, specific custom-made set-ups are required to measure this micromotion, with the result that numerous studies opt to perform more straightforward load-to-failure tests. A custom-made micromotion test set-up for artificial acetabular bone models was developed and used to compare load-to-failure (implant push-out test) with micromotion and to assess the influence of bone material properties and press-fit on the implant stability. The results showed a high degree of correlation between micromotion and load-to-failure stability metrics, which indicates that load-to-failure stability tests can be an appropriate estimator of the primary stability of acetabular implants. Nevertheless, micromotions still apply as the golden standard and are preferred when high accuracy is necessary. Higher bone density resulted in an increase in implant stability. An increase of press-fit from 0.7 mm to 1.2 mm did not significantly increase implant stability.

Turbomachine rotors, supported by little damped rolling element bearings, are generally sensitive to unbalance excitation. Accordingly, most machines incorporate squeeze film damper technology to dissipate mechanical energy caused by rotor vibrations and to ensure stable operation. When developing a novel geared turbomachine able to cover a large power range, a uniform mechanical drivetrain needs to perform well over the large operational loading range. Especially, the rotor support, containing a squeeze film damper and cylindrical roller bearing in series, is of vital importance in this respect. Thus, the direct objective of this research project was to map the performance of the envisioned rotor support by estimating the damping ratio based on the simulated and measured vibration response during run-up. An academic test rig was developed to provide an in-depth analysis on the key components in a more controlled setting. Both the numerical simulation and measurement results exposed severe vibration problems for an insufficiently radial loaded bearing due to a pronounced anisotropic bearing stiffness. As a result, a split first whirl mode arose with its backward component heavily triggered by the synchronous unbalance excitation. Hence, the proposed SFD does not function properly in the lower radial loading range. Increasing the static load on the bearing or providing a modified rotor support for the lower power variants will help mitigating the vibration issues.

Magnetic springs are a fatigue-free alternative to mechanical springs that could enable compliant actuation concepts in highly dynamic industrial applications. The goals of this article are: (1) to develop and validate a methodology for the optimal design of a magnetic spring and (2) to benchmark the magnetic springs at the component level against conventional solutions, namely, mechanical springs and highly dynamic servo motors. We present an extensive exploration of the magnetic spring design space both with respect to topology and geometry sizing, using a 2D finite element magnetostatics software combined with a multi-objective genetic algorithm, as a part of a MagOpt design environment. The resulting Pareto-optima are used for benchmarking rotational magnetic springs back-to-back with classical industrial solutions. The design methodology has been extensively validated using a combination of one physical prototype and multiple virtual designs. The findings show that magnetic springs possess an energy density 50% higher than that of state-of-the-art reported mechanical springs for the gigacycle regime and accordingly a torque density significantly higher than that of state-of-the-practice permanently magnetic synchronous motors.

The demands for improvement in sound quality and reduction of noise generated by vehicles are constantly increasing, as well as the penalties for space and weight of the control solutions. A promising approach to cope with this challenge is the use of active structural-acoustic control. Usually, the low frequency noise is transmitted into the vehicle's cabin through structural paths, which raises the necessity of dealing with vibro-acoustic models. This kind of models should allow the inclusion of sensors and actuators models, if accurate performance indexes are to be accessed. The challenge thus resides in deriving reasonable sized models that integrate structural, acoustic, electrical components and the controller algorithm. The advantages of adequate active control simulation strategies relies on the cost and time reduction in the development phase. Therefore, the aim of this paper is to present a methodology for simulating vibro-acoustic systems including this coupled model in a closed loop control simulation framework that also takes into account the interaction between the system and the control sensors/actuators. It is shown that neglecting the sensor/actuator dynamics can lead to inaccurate performance predictions.

The goal of this study is twofold. The first one is to assess the applicability of approaches based on dynamicmechanical analysis to investigate the viscoelastic properties of a self-adhesive synthetic rubber. The second goal is to identify the parameters of a viscoelastic model which accurately represents the frequency-dependent mechanical properties. For that purpose, the time-temperature superposition principle is successfully applied to build the master curves of the material up to 1 MHz. The thickness of the samples and the thermal expansion effects are found to have a negligible influence on the mechanical properties measured by dynamic-mechanical analysis. The parameters of a generalized Maxwell model and a fractional derivative model are identified from the obtained master curves and lead to an accurate representation of the frequency-dependent mechanical properties of the rubber.