Search

One major challenge of today’s product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled.

Abstract This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear.

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively.

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.

The present study aims to efficiently predict the wear volume of a journal bearing under start–stop operating conditions. For this purpose, the wear data generated with coupled mixed-elasto-hydrodynamic lubrication (mixed-EHL) and a wear simulation model of a journal bearing are used to develop a neural network (NN)-based surrogate model that is able to predict the wear volume based on the operational parameters. The suitability of different time series forecasting NN architectures, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Nonlinear Autoregressive with Exogenous Inputs (NARX), is studied. The highest accuracy is achieved using the NARX network architectures.

Abstract This contribution summarizes the current state of research regarding so‐called sensor‐integrating machine elements as an enabler of digitalization in mechanical engineering and——if available—their application in industry. The focus is on the methodical aspects of the development of these machine elements in general as well as specific sensor‐integrating machine elements that are either already in use or currently under development. Developmental aspects include the robust design of initially evaluated concepts for sensor‐integrating machine elements as well as their modularization. Smart materials with sensory functions are included in the analysis as well as the differentiation with regard to add‐on sensors. The aim of the authors interlinked by a special research program funded by the German Research Foundation (DFG) is to facilitate the exchange with other researchers with the help of the comprehensive overview given in this contribution. The contribution concludes with a brief discussion of open challenges, such as the energy supply and data transfer in rotating systems and also data security.

Wind energy is an important renewable energy source. Rotor main bearings are critical components of wind turbines since a faulty main bearing leads to downtime and high repair costs. Operational expenditures amount to 32% of wind energy costs. The use of plain bearings as main bearings can potentially reduce these costs. Plain bearings with segmented sliding elements can be repaired up-tower without dismantling the drivetrain, as damaged segments can be exchanged individually. One such segmented plain bearing design is the conical plain bearing design called FlexPad. For the FlexPad, proof of concept was achieved for the 1 MW range during previous studies. Modern wind turbines—especially for offshore deployment—have increased in size significantly compared with their predecessors. The goal of current studies is to transfer the FlexPad design towards a main bearing unit at a market relevant scale of 8.5 MW. In this work, the identified scaling challenges are presented. A FlexPad model scaled to the 8.5 MW range is presented to illustrate the challenges. The bearing load components, such as radial forces and torque, increase on different scales with increasing rotor size leading to changed load characteristics with increasing size. Increased rotor weight and bearing diameters result in an increase in the breakaway torque required to start turbine rotation. This breakaway torque can exceed the torque generated by the turbine at starting wind speeds. The generally increased loads necessitate stiffer sliding segments leading to the increased weight of the segments, which hampers the ability to easily exchange segments.

Offshore crane winches are equipment installed on sea vessels and designed for accurate lifting and lowering of payloads at subsea levels in all conditions and are equipped with Active Heave Compensation (AHC) capabilities which keep the handled payload stable relative to the seabed. Due to logistical challenges and short maintenance windows imposed by sea conditions, maintenance of offshore crane winches in open sea is costly and time consuming, and its reliability is therefore of the highest importance. In recent times, electric drives for the actuation of the winch drum have seen a surge in popularity in favour of hydraulic drives owing to advances in control technologies of AC motors, as well as higher efficiency, lower noise and lack of an oil reservoir. In light of these changes and the importance of reliability, a study on the effect of the electrification (ie. switching from hydraulic to electric actuation) of offshore crane winches from a reliability viewpoint is necessary. In previous studies, it has been established that the different inherent properties of electric and hydraulic drives lead to torque ripples of different magnitudes and frequencies which may induce additional mechanical stresses in the drivetrain. The effects of these ripples on a system level have so far only been studied from a Noise-Vibration-Harshness (NVH) viewpoint in applications where driver comfort takes precedence in design (ex. electric vehicles, tractors). In this paper, these drive-induced ripples are simulated for common electric and hydraulic drive configurations used in offshore crane winches, and their effect on the reliability of the driven gearbox, as the most critical component in the drivetrain, is analysed. It was found that electric drives produce in general ripples of smaller magnitude (between 0.44% and 1.07% of static torque for electric compared to between 3.12% and 3.30% for hydraulic), which enhances the reliability of the driven gearbox (1.25% to 3.44% reduction in lifetime due to electric ripples, and 9.29% to 9.67% due to hydraulic ripples in comparison to an ideal torque source with no ripples). However, depending on the design and number of pumps in use, the size of the hydraulic torque ripple can have a wider range of between 1.31% and 7.69% and the subsequent reduction in lifetime compared to the ideal case ranging between 4.07% and 21.37%.

Wind turbines (WT) are complex multidisciplinary systems containing a large number of mechanical, control, and electrical components. Model-Based Systems Engineering (MBSE) provides an approach for cross-discipline development to address the system complexity and focuses on creating and utilizing domain models as the primary means of information exchange. The domain models predict system behaviors and can support system validation through virtual testing at an early stage of system development. However, the further the WT development proceeds, the more system parameters are set, and the more domain models and virtual tests are involved. Therefore, it is necessary to design a framework of virtual testing workflows of WTs to support virtual validation processes as well as to automate those workflows. To achieve this goal, this contribution shows how standardized virtual testing workflows can be designed and linked to hierarchical and functional system architectures modeled in the Systems Modeling Language (SysML). The virtual testing workflows enable to trigger simulations of domain models and handle system parameters participating in the simulations, thus ensuring data consistency. Furthermore, to facilitate modular management and reuse of domain models, the domain models are classified according to model purposes, model fidelities, and system scopes. The virtual testing workflows are structured corresponding to the classification of the domain model, thereby forming a nested framework. To verify the feasibility of the proposed workflows, a virtual testing process of WT components (i.e., bearings) inside the system context with different model purposes and different model fidelities is demonstrated. It is shown that virtual testing workflows are systematically organized so that engineers can easily virtually (re-)validate the systems.

To increase the power density of the electromechanical drive train of wind turbines, journal bearings can be used as planetary gear bearings instead of rolling bearings. This technological change presents new challenges. For example, wind turbine drive systems are subject to dynamic and low-speed operating conditions, which can lead to accelerated abrasive wear of the journal bearings. In addition, oil supply failure or peak loads due to wind gusts and grid and power converter faults could potentially result in catastrophic failure due to adhesive wear in a very short time. Such operating characteristics are, therefore, critical regarding the journal bearing wear lifetime and must be considered in the design. The successful implementation of journal bearings in wind turbines depends on a reliable estimation of adhesive and abrasive wear. In this paper, five different models for the wear calculation of journal bearings are evaluated regarding their suitability for the wear calculation of planetary gear bearings in wind turbines. For this purpose, the following evaluation criteria were defined: parameter uncertainty, parametrization effort, in particular number of parameters, parameterization method, load case dependency of parameters and calculation effort. In order to be able to evaluate the wear models, the wear models are numerically implemented, and the wear of a test journal bearing is exemplarily calculated under load conditions, which are comparable to load conditions in a wind turbine. Relevant influences from the wind turbine system, such as lubricant, material and manufacturing-dependent surface influences, such as roughness and hardness, are considered. The wear models are evaluated with respect to their fulfillment of the defined criteria. The resulting evaluation allows the selection of a wear model that can be used to calculate the wear of planetary gear journal bearings in wind turbines, considering the available input variables.

Innovative Model-Based Systems Engineering (MBSE) applies function-oriented hierarchical system architecture and utilizes Systems Modeling Language (SysML) for virtual testing. However, for complex systems, the relevant virtual tests are scattered at different hierarchy levels. Manually performing these virtual tests requires a lot of effort and leads to the potential risk of errors due to the overlooking of some tests and functions. In order to solve these problems, it is necessary to develop automated virtual validation workflows for the function-oriented system architecture. This contribution proposes a standardized virtual validation workflow design framework corresponding to the hierarchical functional architecture to organize virtual tests. The virtual tests are also modeled in workflows consisting of a set of simulation activities that can execute domain models to simulate system behaviors. The simulation activities are developed modularly, corresponding to the classification of the domain models. The resulting workflows are implemented in a wind turbine (WT) system. It demonstrates that the workflows enable automated validation at all hierarchy levels and early detection of technical system failure risks. The design framework allows for the synchronous creation of validation workflows as functions are added or decomposed. The standardized design ensures easy redesign and function reuse across different systems. Modular design, based on model classification, enhances design agility and adaptability in various system contexts. The proposed virtual testing workflows automatically execute corresponding simulation activities sequentially.

As wind energy is paving the way for the energy transition from fossil to renewable energy sources, the ongoing trend of increasing the rated power of wind turbines aims to reduce the overall cost of wind energy. The resulting increase in drivetrain loads motivates the need for wind turbine (WT) drivetrain testing in the development phase of critical components such as the WT main gearbox (GB). While several WT system test benches allow for the application of emulated rotor loads in six degrees of freedom (6-DOF), the drivetrain input loads can significantly differ from the GB 6-DOF input loads due to the design of the drivetrain under test. However, currently available load measurement solutions are not capable of sensing GB input loads in 6-DOF. Thus, this work aims to develop a methodology for converging signals from a purposely designed sensor setup and turbine specific design parameters to compute the GB 6-DOF input loads during WT testing. Strain gauges (SG) and accelerometers have been installed on the low-speed shaft (LSS) of a WT drivetrain under test at the 4MW WT system test bench at the Center for Wind Power Drives. Using the data of the aforementioned sensors, a methodology for computing the GB input loads is developed. The methodology is validated through comparison to the applied loads data provided by the aforementioned test bench. The results demonstrate the high promise of the proposed method for estimating the GB input loads during WT drivetrain testing.

Adequately designed and positioned surface textures are recognized as a promising way to increase load-carrying capacity and reduce frictional losses of journal bearings. The aim of this work is to analyze the local lubrication mechanisms of textures in journal bearings from microflow perspective, while considering the interactions between textures and the film formation in the whole bearing. For this purpose, hydrodynamic lubrication models of textured journal bearings are built. The results show that placing textures downstream of the high-pressure region leads to a reduced friction force, with a less severe loss of load-carrying capacity. The effects of textures on the load-carrying capacity include the positive micro-hydrodynamic pressure effect and the negative effect caused by the discontinuity of the high-pressure region. The micro-hydrodynamic pressure of textures can be generated on one hand by limiting pressure drop (cavitation) in the divergent gap and on the other hand by the inertia effect. For the friction, the vortex inside textures affects the friction force by influencing the maximum shear stress at the minimum oil film. In turn the vortex is influenced by the bearing lubrication film. The research provides the fundamental reference and theoretical basis for the design and optimization of textured journal bearings.

To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development.

The trend of increasing the power output and nominal load capacities of wind turbines (WT) over time has been driving the construction of testing facilities with increasing load capacities for testing WT drivetrain components prior to field deployment. Due to the high investment and operational costs of such facilities, a need exists to design accelerated tests that cover load situations corresponding to expected field conditions while maintaining high time-efficiency. This investigation addresses this need by presenting a methodology to achieve the following goals. Firstly, identifying ranges and combinations of WT 6-degree of freedom (6-DOF) rotor loads is to be expected in the field. This is achieved using aeroelastic multibody simulations (MBS) of an MBS WT model being subjected to simulated wind fields covering the design load cases outlined in the IEC 61400-1 standard and by analyzing the simulated time-series data to design accelerated tests that efficiently and realistically cover the design space of the variables, e.g., 6-DOF rotor loads, to be applied during WT drivetrain testing. The designed tests are to take place on a purpose-built test rig that allows for the application and control of the 6-DOF drivetrain input loads and rotational speed. Using the proposed method, accelerated tests were designed that efficiently cover load combinations within the realistic regions of the design space. A comparison with a full factorial design of experiments shows a significant (95+ %) reduction in total test time as well as the ability of the proposed method to help to avoid unsustainable and unrealistic load conditions within the design space that could result in costly, unintended drivetrain failures during testing.

Model-based systems engineering (MBSE) is an auspicious approach to the virtual development of cyber-physical systems. The behavior of the system’s elements is thus represented by specialized simulation models that are integrated into the descriptive SysML-based system model. Although many simulation models have been developed in research for the common system elements for various purposes and fidelities, their integration remains a major challenge: the parameter interfaces of the simulation models must be coupled with each other and with the parameters of the system elements in such a way that they are correctly parameterized. So far, this coupling can only be carried out by model experts in a time-consuming and error-prone manner. Therefore, in this paper, we first propose a concept that structures the system element parameters for targeted use in validation and design cases. Second, we propose a model signature for simulation models that differentiates its parameters by input, internal, output, and model parameters and specifies them with spatial and temporal dimensions as well as admissible ranges, among others. Based on the two contributions, domain models can be validly and automatable coupled and used for the virtual development of system elements in model-based systems engineering.

Laser-based plastic–metal joints have high potential to enable cost-efficient lightweight structures in multi-material design. By an appropriate load-optimized positioning of the microstructure on the joining zone, cost- and strength-optimized joints can be realized. However, there are no design methods and models to efficiently develop these tailored microstructures. Currently, time-consuming experiments are necessary to find the optimum microstructure concepts. These experiments must be repeated when requirements change, e.g., dimensions of the components. To provide a simple and efficient design tool, this paper presents an automated numerical method for the development of cost- and strength-optimized microstructure concepts for laser-based joining zones. The basis for the approach is a new numerical model which generates concepts for microstructures automatically based only on the stress tensor in the joining zone. A new finite element cohesive zone model (CZM) was developed to estimate the joint strength. The CZM parameters were efficiently derived from a finite element model of a single cavity. To determine the costs, a new model is presented that calculates the production time and the cost for any given microstructure. The models were interconnected in a combined optimization procedure and a genetic algorithm was used to determine cost- and strength-optimized microstructure concepts. The approach was applied to a demonstration example where the laser costs were reduced by up to 67% compared with benchmarks with surface-covering parallel linear cavities. The approach shows high potential for the efficient design of cost- and strength-optimal laser-based hybrid joints since it is fully based on simulation models and iterative experiments in the design stage are eliminated.

Rolling bearings face different damaging effects: Besides mechanical effects, current-induced bearing damage occurs in electrical drive systems. Therefore, it is of increasing interest to understand the differences leading to known electrical damage patterns. It is of utmost importance not to consider the harmful current passage in the machine element as an isolated phenomenon but to take into account the whole drive system consisting of the machine elements, the electric motor and the connected power electronics. This publication works toward providing an overview of the state-of-the-art of research regarding electrical bearing currents.

Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.

The ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intensification of the load situation within the WT gearbox motivates the need to monitor WT transmission input loads. However, due to the high costs of direct measurement solutions, more economical solutions, such as virtual sensing of transmission input loads using stationary sensors mounted on the gearbox housing or other drivetrain locations, are of interest. As the number, type, and location of sensors needed for a virtual sensing solutions can vary considerably in cost, in this investigation, we aimed to identify optimal sensor locations for virtually sensing WT 6-degree of freedom (6-DOF) transmission input loads. Random forest (RF) models were designed and applied to a dataset containing simulated operational data of a Vestas V52 WT multibody simulation model undergoing simulated wind fields. The dataset contained the 6-DOF transmission input loads and signals from potential sensor locations covering deformations, misalignments, and rotational speeds at various drivetrain locations. The RF models were used to identify the sensor locations with the highest impact on accuracy of virtual load sensing following a known statistical test in order to prioritize and reduce the number of needed input signals. The performance of the models was assessed before and after reducing the number of input signals required. By allowing for a screening of sensors prior to real-world tests, the results demonstrate the high promise of the proposed method for optimizing the cost of future virtual WT transmission load sensors.

Electrical faults can lead to transient and dynamic excitations of the electromagnetic generator torque in wind turbines. The fast changes in the generator torque lead to load oscillations and rapid changes in the speed of rotation. The combination of dynamic load reversals and changing rotational speeds can be detrimental to gearbox components. This paper shows, via simulation, that the smearing risk increases due to the electrical faults for cylindrical roller bearings on the high speed shaft of a wind turbine research nacelle. A grid fault was examined for the research nacelle with a doubly fed induction generator concept. Furthermore, a converter fault was analyzed for the full size converter concept. Both wind turbine grid connection concepts used the same mechanical drive train. Thus, the mechanical component loading was comparable. During the grid fault, the risk of smearing increased momentarily by a maximum of around 1.8 times. During the converter fault, the risk of smearing increased by around 4.9 times. Subsequently, electrical faults increased the risk of damage to the wind turbine gearbox bearings, especially on the high speed stage.

In the face of the rapid growth in the scale and complexity of multidisciplinary systems, being able to develop reliable systems under ever-faster changing and more individual market requirements is becoming more and more challenging. The Model-Based Systems Engineering (MBSE) approach has already been researched heavily, and started to be introduced for the management of complexity, maintaining consistency, and reducing development costs and the time-to-market. However, a major drawback of the current MBSE methodologies is the lack of capability to integrate with domain-specific simulation models to investigate design concepts in the early phases of the development process. In order to address this issue, we propose a holistic system modeling approach that allows system engineers to link descriptive system models with domain-specific simulation models. In this paper, the Systems Modeling Language (SysML) is used as the standard architecture modeling language. A system modeling approach in SysML based on the system’s functional architecture for system design and validation is defined. The approach was developed to integrate domain-specific models into the system model using a SysML modeler with the capability of running and reusing simulation tasks via the coupling of external tools, which helps to bridge the existing gap between models on the system level and detail level. The feasibility of the proposed approach will be evaluated based on the case study of a wind turbine (WT) system. The study shows that our approach has the potential to enable the consistent, parameter-based interlinkage of domain-specific models based on always-up-to-date data, and to assist engineers in making design decisions to meet the system requirements accurately and rapidly in different engineering fields.