Browse Topic: Gears
Gears play a critical role in automotive transmission systems. During operation, frictional heat is generated in the intermeshing region due to loading. Effective lubrication and cooling are essential to minimize heat generation and ensure smooth operation. Lubrication failure can lead to a significant local temperature rise, potentially causing gear scuffing—a phenomenon where intermeshed gear teeth weld together and tear apart during rotation—resulting in severe damage and compromised transmission performance. To prevent this, gears are typically lubricated using splash or jet lubrication techniques. This study presents a Conjugate Heat Transfer (CHT) simulation of a jet-lubricated gear pair in an automotive transmission system to predict the local temperature rise due to frictional heating in the intermeshing region of the gears. The paper focuses on implementation of the frictional heat generation on the gear teeth and resultant transient temperature rise in the gear contact region. A commercial CFD tool, Simerics MP+ is used for the 3D CHT simulation. The methodology employs a multiphase Volume of Fluid (VOF) approach to capture the interaction between oil and air while utilizing a mixed timescale coupled approach for heat transfer analysis. The resulting local temperature distribution on the gear teeth is analyzed and validated with the test data.
Under the background of “dual carbon”, reducing the power consumption of electric vehicles (EVs) per 100 kilometers and improving their operating energy efficiency are the only way for the development of electric vehicles. This paper uses Yao’s theorem in the energy efficiency prediction theory of multi-unit systems to give the optimal control method for the operation energy efficiency of EVs with single motor drive and multiple gears. The optimal control method for the overall operating energy efficiency of EVs with single motor drive and multiple gears is to keep the power consumption per 100 kilometers equal before and after the gear switching, or to keep the output power of the battery equal before and after the gear switching.
An important characteristic of battery electric vehicles (BEVs) is their noise signature. Besides tire and wind noise, noise from auxiliaries as pumps, the electric drive unit (EDU) is one of the major contributors. The dynamic and acoustic behavior of EDUs can be significantly affected by production tolerances. The effects that lead to these scatter bands must be understood to be able to control them better and thus guarantee a consistently high quality of the products and a silent and pleasant drive. The paper discusses a simulation driven approach to investigate production tolerances and their effect on the NVH behavior of the EDU, using high precision transient multi-body dynamic analysis. This approach considers the main effects, influences, and the interaction from elastic structures of electric motor and transmission with accurate gear contact models in a fully coupled way. It serves as virtual end of line test, applicable in all steps of a new EDU development, by increasing front loading. Various parameters such as clearances, gear microgeometry, bearing deviation, misalignment, unbalance, electrical excitation, and control effects can be investigated for their sensitivity and impact on transfer and response. Such a model is applied for dynamic analyses of specific use cases and operating conditions. The important part of this paper is the demonstration of the applicability of such a fully physical and complex approach for large-scale DoE to investigate the required tolerance space for the defined parameters and the parameter combinations without the need of model simplification or transfer to frequency domain and by making use of high-performance computers in clusters. The derived data is further on used to train a surrogate data model to cover the whole parameter space. The effect of changes on specific NVH KPI’s like mechanical orders, resulting in gear whine, and their separation from electrical orders, as well as the specific root cause of a detected phenomenon can be analyzed.
For the team at SmartCap, building top-notch gear for outdoor adventurers isn’t just a business — it’s a passion driven by their own love for the wild. But as demand for their rugged, modular truck caps soared after their move to North America in 2022, they hit a snag: How do you ramp up production without sacrificing the meticulous quality you are known for, all while navigating a tough labor market? Their answer? A bold step into the world of intelligent automation, teaming up with GrayMatter Robotics, and employing the company’s innovative Scan&Sand™ system.
The Sikorsky Boeing SB>1 DEFIANT is a technology demonstrator aircraft that was built under the Joint Multi-Role Technology Demonstrator (JMR TD) program to address the next generation performance requirements of the US Army Future Vertical Lift (FVL) initiative. During the development of the SB>1 DEFIANT technology demonstrator aircraft several manufacturing lots of gears were produced with a core hardness that was 10-30% below the minimum engineering requirement. The defect was not detected until a large population of gears was near completion. To prevent significant program cost and schedule impacts, a safe load capacity for the discrepant gears was determined via test. Dynamically loaded ground test articles for SB>1 DEFIANT technology demonstrator aircraft began qualification testing with the low hardness gears. The low hardness issue, root cause, and test method to establish a safe operating load limit are discussed.
The Main Gearbox of a helicopter is a crucial component that delivers the desired performance and ensures the highest possible level of safety of the aircraft; it includes several gears and bearings, which require to be continuously lubricated by a pressurized oil flow. Undesired circumstances may cause the oil to leak from the main circuit, hence reducing its pressure and consequently the oil flow rate targeted towards the rotating components; this modifies their friction coefficient, and subsequently leads to an overheating of the parts with the risk of degenerating in a catastrophic failure. During the design of a helicopter drive system, engineers need to take proper precautions and make sure that the MGB is fully equipped with the proper features to cope with a loss of lubrication event; specifically, the drive system is supposed to be able to run at least 30 minutes after the oil pressure drops to zero. A lot of effort has been put over the years at Leonardo Helicopters to find robust solutions to attain the longest performance of the drive system in no-oil conditions: the most important result is the certification of the AW189 for a 50-minutes “run dry” capability. Nevertheless, the dynamic environment typical of the rotorcraft industry pushes towards continuous innovation, and in the last few years the Transmissions Systems Design department of LH has been asked to investigate suitable ways to further augment the no-oil capabilities of the MGB: the main steps followed and entailed results are presented in this paper. The first part of the manuscript discusses the “state of the art” auxiliary lubrication system, currently flying on the AW189 drive system. The second part tackles the approach adopted to meet the novel requirements, unveiling both the methodology and the final design choice: the latter includes a metering element, able to tune the oil flow rate headed towards the component deemed the most critical in order to satisfy the requirement of longer no-oil performance. Numerical and experimental tools are exploited as complementary tools to properly crystallize the obtained results and corroborate the solution.
For electric vehicles, it is critical to develop drive units that produce a minimal amount of noise while meeting efficiency needs for a given application. Modern computational resources and accumulated experience allow for engineers to evaluate gear noise early in the development process and influence the design of the drive unit. This paper documents a high-fidelity virtual engineering approach to evaluate gear noise in a concept parallel axis drive unit and provide learnings to influence the design of external structures to improve NVH performance. By using the latest simulation tools to calculate and visualize the noise and vibration characteristics of the drive unit, designers and developers can implement design changes in optimization iterations to reduce noise and vibration. Gear harmonic response is firstly analyzed through a system model which considers structural deflection and misalignment, then a FE housing model is incorporated which is used for noise radiation evaluation and correlation. Through these procedures, different vibrational modes of the system can be examined. By identifying both problematic internal harmonics and the noisy surfaces they excite, new external housing designs can be achieved with much lower noise. Verification analysis of this design illustrates local improvement at problematic frequencies and informs on future work to further improve gear performance.
Gear whine has emerged as a significant challenge for electric vehicles (EVs) in the absence of engine masking noise. The demand from customers for premium EVs with high speed and high torque density introduces additional NVH risks. Conventional gear design strategies to reduce the pitch-line velocity and increase contact ratio may impact EV torque capacitor or its efficiency. Furthermore, microgeometry optimization has limited design space to reduce gear noise over a wide range of torque loads. This paper presents a comprehensive investigation into the optimization of transfer gear blanks in a single-speed two-stage FDW electric drive unit (EDU) with the objective of reducing both mass and noise. A detailed multi-body dynamics (MBD) model is constructed for the entire EDU system using a finite-element-based time-domain solver. This investigation focuses on the analysis and optimization of asymmetric gear blank design features with three-slot patterns. A design-of-experiment (DOE) methodology is employed to identify pivotal gear blank design parameters, including the blank thickness and slot angle. The radiated sound power and mount vibration responses from the EDU are predicted and correlated with test data. The time-varying stiffness at the meshing point gives rise to sidebands around the transfer gear orders, which are accurately captured using the MBD time-domain solver. The asymmetric gear blank stiffness changes the torsional vibration transfer path, necessitating microgeometry re-optimization to fully capture the NVH benefits. A case study is conducted based on the Ultium EDU, focusing on transfer gear blank design. It is demonstrated that a selected three-slotted gear blank design, in conjunction with optimized microgeometry, results in reduced mass and up to 10 dB lower gear noise for the electric drive unit system.
The applications are too numerous to list in their entirety. Coffee grounds. Eggshell waste. Pomegranates and pineapples. Manure and paper mill sludge. Tobacco. These are just a few of the materials that require dewatering, a process that — as its name suggests — separates fluids from solids, often converting what would otherwise go down the drain or end up in a landfill into saleable products.
In Electric vehicle Drive Unit Gears, high mesh misalignments result in shift in load distribution of a gear pair that can increase contact and bending stresses. It can move the peak bending and contact stresses to the edge of the face width and increase gear noise as well. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank, which in turn helps in reducing noise and improving durability of drive unit. This paper delineates Prescriptive Analytics method that combines virtual simulations, Machine learning (ML) and optimization techniques to minimize different gear misalignments for the electric vehicle drive units. Generally, the manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is time consuming and does not account for interactions between the components. In this study, firstly, Machine learning models are developed based on design of experiments (DOE) simulations. These ML models are used as surrogates for actual simulations in generic algorithms (Differential Evolution) based optimization techniques. It finally prescribes changes in stiffness of different components to get optimum misalignment value.
Gear shifting effort or force especially in manual transmission has been one of the key factors for subjective assessment in passenger vehicle segment. An optimum effort to shift into the gears creates a big difference in overall assessment of the vehicle. The gear shifting effort travels through the transmission shifting system that helps driver to shift between the different available gears as per the torque and speed demand. The shifting system is further divided into two sub-systems. 1. Peripheral system [Gear Shift Lever with knob and shift Cable Assembly] and Shift system inside the transmission [Shift Tower Assembly, Shift Forks, Hub and sleeve Assembly with keys, Gear Cones and Synchronizer Rings etc.] [1]. Both the systems have their own role in overall gear shifting effort. There has been work already done on evaluation of the transmission shifting system as whole for gear shifting effort with typical test bench layouts. Also, work has been on assessment of life of the synchronizer ring as standalone. Current paper explains the work done on the development of a methodology to evaluate the synchronizer ring assembly on a test bench that accommodates only a Synchronizer ring and Gear cone set for evaluation, and the results of which can be correlated with bench testing of the complete synchronizer ring evaluation along-with Transmission shifting system.
For a couple of decades, virtually every global original equipment manufacturer spent significant capital and attention raising their sales/production profile in China. It became the world's largest light vehicle market by 2010 and has not looked back. Forming new joint ventures to expand their portfolios through the extension of global offerings, several OEMs even took the opportunity to design China-specific variants. Western OEMs followed these JVs, and scores of European, North American, Japanese and Korean Tier 1 and 2 suppliers followed their OEMs, creating a local supply of global components as China became an integral cog in the machine. A presence in China is core to success for many industry players. China produced about 28 million light vehicles in 2023, based on S&P Global Mobility's estimates. China is not only key for Western OEM profitability, from a volume perspective it is the largest single market (about 31% of the world in 2023) with the highest growth profile. It also resides between Europe and the U.S. from a content and vehicle segment profile. Additionally, global unibody platforms from virtually every global OEM count on China for significant contributions. As recently as 2019, non-Chinese OEMs accounted for 13 million units (53%) of China's light-vehicle output.
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