Browse Topic: Measurements

Items (1,517)
Oil consumption is a major concern for all engine manufacturers, both from an environmental and engine durability standpoint. Understanding how oil consumption is affected by key design parameters has traditionally been established during the validation phase of an engine development program using both steady-state and transient lube oil consumption (LOC) measurements. Cost and time pressures are driving this development to be performed virtually, where many more parameters can be assessed and understood prior to design verification testing. This paper presents a new analytical method that is capable of predicting transient phenomena of the ring pack that would not normally be captured by steady-state methods, providing a toolset that reduces engine development testing and cost and aid troubleshooting. Implemented in Realis1 RINGPAK, this new transient method has been validated against transient LOC measurements for a 2.0 L 4-cylinder GTDI engine. Different transient load/speed
Bell, DavidZhang, ShashaShen, CongTisch, DanBrezina, MichalHuang, Yun
Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge
Wang, ZiweiLi, ZhenglinGao, YangXuan, Fuzhen
This study investigates the effect of liquid-applied spray damping (LASD) thickness on the vibration and sound radiation of thin steel panels. Although LASD is widely used to enhance structural damping, its influence on radiated sound and the role of coating thickness have not been systematically studied. Five steel panels with varying LASD thicknesses were evaluated using two experimental approaches. An impact-based method in a hemi-anechoic chamber measured the structural mobility and noise transfer functions, while a reciprocal method in a reverberation chamber under acoustic excitation measured the radiated sound power transfer function. A thickness ratio was found beyond which additional LASD thickness yielded diminishing improvements in noise and vibration reductions. The effect of LASD thickness on radiation efficiency was also assessed in both narrowband and one-third octave bands.
Neihguk, DavidSuh, SamHerrin, David W.
In this experimental work, a detailed analysis of the wind tunnel measurements on scaled motorbike models equipped with different front wings was performed considering four wing configurations operating at different Reynolds numbers and roll angles. Global forces acting on the models were measured by a high-resolution dynamometric balance, while velocity fields in the wake were measured by means of the Particle Image Velocimetry technique. Throughout the paper, overall models’ performances are investigated, demonstrating similar behavior for drag coefficients and various trends for lift coefficients. The without- and single-wing configurations were shown to have positive sign, and conversely, the double- and closed-wing cases—with negative sign—generated downforce due to the presence of significant upward velocities, which in turn modified the wake shape. Furthermore, the improvements in closed-wing configuration compared to without- and single-wing ones were noticeable, while slight
Moscato, GiorgioRomano, Giovanni Paolo
The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their
D, AnithaD, SuchitraJain, UtsavMaity, SouvikDinda, Atish
Recent geopolitical events in Venezuela, Ukraine and other hot spots are a stark reminder that the long-term planning environment is fraught with challenges and opportunities that suppliers cannot control. The initiation of U.S. tariffs on its trading partners and various embargos also underscored that we have to be flexible in how we dole out capital and the risk we are assuming. The supply base is at the end of that chain. Any issues upstream will reverberate exponentially. It is obvious that the automotive world is re-regionalizing, and quickly. Why the concern? Some context. Until the '70s, every region essentially rowed its own boat. While there were some exports from one major region to another, there were regional OEMs that were sponsored by national governments due to job creation, tax base considerations and bragging rights. The U.S, France, Italy, Germany, Japan, South Korea and a host of others wanted to build national OEMs that could drive scale and become a global force.
The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.
Allard, Théophile
Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of
V Dhage, YogeshKolage, Vikas
Typically duty cycle development for any component carried out in the absence of direct acting load will be based on the measured strain from the RLDA and comparing it with the rig level testing. The biggest drawback from such an approach is the fact that the selected loads can be drastically different from the actual experienced peak load from the actual RLDA. This happens because the strain gauges will have sensitivity toward all three directional loads and sometimes due to complex structure design uni directional sensitive locations are difficult to find out or strain gauge. This will lead to over predicting the loads in some cases and the selected load may end up yielding the component in a single cycle itself. This paper discusses the method employed to calculate the loads at the input load location using the strain gauges which are cross sensitive and using that input loads to select the loads for the testing of the component. Also it talks about the constraints it faces while
Anandh, SudheepPrasad, SathishR S, Mahenthran
The Exhaust Emission Control is a vital part of automotive development aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies is a highly time-consuming process, which requires extensive vehicle testing under a variety of operating conditions. The frequent updates in emission legislation requires a high-efficiency process to achieve a faster time-to-market. The use of Machine Learning (ML) in the domain of emission calibration is the need of the hour to proactively improve the process efficiency and achieve a faster time-to-market. This paper attempts to explores emerging trend of Machine Learning (ML) based data analysis that have improved the overall process efficiency of emission control calibration. The data generated by automated programs could be used directly in data analysis with minimal or no need for data cleaning. The Machine Learning (ML) models could be trained by historical data from
Dhayanidhi, HukumdeenBalasubramanian, KarthickA, Akash
Damping materials exhibit advantageous mechanical and acoustic characteristics that enhance the structural integrity of systems. These materials find extensive applications across various industries, including automotive, aerospace, and building acoustics, and are widely employed in the development of soundproofing materials. The damping characteristics of materials primarily pertain to the dissipation of vibrational energy, the reduction of oscillations, and the controlling and subsequent attenuation of vibration-induced noise emanating from structures. To improve both structural integrity and acoustic performance, it is crucial to accurately assess the damping properties of these materials. The Oberst bar test method is a standard method used in the automotive, railway and building industry for initial optimization of damping material However, questions have arisen about the degree to which the outcomes of the Oberst test truly reflect real-world applications. Numerous experimental
Kamble, Prashant PrakashJoshi, ManasiJain, SachinkumarHarishchandra Walke, Nagesh
The area of electric vehicles (EV) has fully arrived with almost every OEM enhancing electric vehicles in their portfolio. However, regarding its business potential numerous challenging engineering questions have risen. Especially vehicle NVH development needs to be rethought as masking noise from classical internal combustion engines (ICE) are gone. At the same time the frequency content of electric engines falls in the best human audible range, creating high potential for annoying tonal acoustic issues. With NVH design requirements now pushed up into the kilohertz range, many classic development strategies fail or lack efficiency. VIBES Technology’s answer to this challenge is what we call Hybrid Modular Modelling (HMM). This modelling strategy combines test-based and numerical simulation throughout the vehicle development cycle. Using best of both worlds, HMM allows accurate virtual (part / system) design and optimization on full vehicle level. Here HMM is based on the latest
Kohlhofer, DanielPingle, Pawan Sharadde Klerk, Dennis
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system
Uthaman, Sreekumar
As the electric mobility landscape evolves, there is a growing emphasis on addressing the Noise, Vibration, and Harshness (NVH) challenges associated with electric drivetrains. The absence of an IC engine in EVs shifts the focus to other noise contributors such as gear meshing, electric machine operation, and structural vibrations. Despite the known influence of micro-geometry on gear dynamics, current optimization practices often rely on empirical adjustments or standard guidelines without fully utilizing advanced computational methods to predict and optimize NVH performance. There exists a pressing need for a systematic approach to analyze and optimize gear micro-geometry to reduce noise and vibration in high-speed e-axle applications. This research aims to bridge that gap by investigating the relationship between micro-geometry optimization and NVH characteristics of an e-axle. Through detailed modelling and optimization techniques, this research aims to identify optimal gear micro
Ankit, PriyadarshiKulkarni, KrishnaMomin, Vaseem
This paper presents the design of a cost-effective fuel injector driver designed for accelerated testing of injectors. The driver simulates injection patterns across a wide range of vehicle operating conditions and can be programmed with injection maps for different engines, test cycles based on drawing specifications, pre-defined engine running profiles, and manual control, where the user defines PWM frequency and duty cycle. It also enables remote operation through a Wi Fi access point. An injector driver-based test setup was developed to study wear and evaluate leakage tendency in an injector design. To simulate extended field usage in a short timeframe, an accelerated operating cycle was derived using telematics data. Injector samples were tested with periodic leak rate measurements. Conducting such tests at vehicle level or on engine test bench would involve significant time and cost. This setup is an effective tool for rapid comparative analysis across supplier design, enabling
Bhatt, PanchamAgrawal, AdheeshKuchhal, Abhinav
In the initial stages of a vehicle development program, the sizing of various components is a critical deliverable. The steering system, in particular, requires a precise estimation of the rack load for the appropriate sizing of the rack and assists units. Accurately predicting the load on the system during the early stages of development is challenging, especially in the absence of benchmark or legacy data. Commonly used processes for estimating parking steering effort often employ simplistic approaches that may fail to account for parameters such as tire size, vertical stiffness, and steering geometry, leading to reduced accuracy. This paper introduces an advanced methodology for predicting steering rack loads, which incorporates considerations such as contact patch size and pressure variation, as well as the tire jacking effect. The methodology involves mathematical modeling of the contact patch using mesh-grids, utilizing common inputs available in the early stages of vehicle
Shirke, UmeshDabholkar, AniruddhBardia, VivekSrivastava, HarshitPrasad, Tej Pratap
The automotive industry is undergoing a significant technological transformation, which is continually impacting the methods used to test the functionalities, delivered to end consumer. This includes the ever-growing need to embed software-based functions to support more and more end user functionality, while at the same time retaining existing and well-established functions, all within short development timelines. This presents both opportunities and challenges, with greater potential for reuse or leverage of test assets, although the actual percentage of leverage on real world projects is practically less than anticipated for a multitude of reasons. This paper collates the various factors which effect the practical leverage of test assets from one project to another, including various workflows and the interaction across components amongst applications lifecycle management systems. Alongside, it describes the current practices of basis analysis in isolation in combination with
Venkata, ParameswaranKulkarni, ApoorvaRAJARAM, SaravananGanesh, Chamarthi
The first step in designing or analyzing any structure is to understand “right” set of loads. Typically, off-road vehicles have many access doors for service or getting into cab etc. Design of these doors and their latches involve a knowledge of the loads arising when the door is shut which usually involves an impact of varying magnitudes. In scenarios of these impact events, where there is sudden change of velocity within few milliseconds, produces high magnitude of loads on structures. One common way of estimating these loads using hand calculations involves evaluating the rate-of-change-of-momentum. However, this calculation needs “duration of impact”, and it is seldom known/difficult to estimate. Failing to capture duration of impact event will change load magnitudes drastically, e.g. load gets doubled if time-of-impact gets reduced from 0.2 to 0.1 seconds and subsequently fatigue life of the components in “Door-closing-event” gets reduce by ~8 times. For these problems, structures
Valkunde, SangramGhate, AmitGagare, Kiran
This paper presents the virtual prototyping of traction motor in commercial EV to make an early prediction of the performance parameters of the machine without spending an enormous cost in building a physical structure. A 48/8 slot-pole configuration of IPMSM is used to demonstrate the electromagnetic and thermal co-simulation in ANSYS MotorCad. The core dimensions were determined using permanent-magnet field theory. From those, a two-dimensional finite-element (2D FEM) model of the interior permanent magnet (IPM) motor was simulated using Ansys Motor-CAD electromagnetic simulation tool. The influence of geometrical parameters on the performances of traction motor are evaluated based on FEM. The temperature distribution have been analyzed under steady and transient operating conditions. Alongside, the effects of saturation, demagnetization analysis, and the impact of PM flux linkage on inductances are also considered in this paper. At last, the simulation and analytical results of the
Murty, V. ShirishRathod, SagarkumarGandhi, NikitaTendulkar, SwatiKumar, KundanThakar, DhruvSethy, Amanraj
Overloading in vehicles, particularly trucks and city buses, poses a critical challenge in India, contributing to increased traffic accidents, economic losses, and infrastructural damage. This issue stems from excessive loads that compromise vehicle stability, reduce braking efficiency, accelerate tire wear, and heighten the risk of catastrophic failures. To address this, we propose an intelligent overloading control and warning system that integrates load-sensing technology with real-time corrective measures. The system employs precision load sensors (e.g., air below deflection monitoring via pressure sensors) to measure vehicle weight dynamically. When the load exceeds predefined thresholds, the system triggers a multi-stage response: 1 Visual/Audio Warning – Alerts the driver to take corrective action. 2 Braking Intervention – If ignored, the braking applied, immobilizing the vehicle until the load is reduced. Experimental validation involved ten iterative tests to map deflection-to
Raj, AmriteshPujari, SachinLondhe, MaheshShirke, SumeetShinde, Akshay
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both longitudinal and vertical simulations at the front and rear of the vehicle. The first aspect involves the development of an instrumentation strategy for the two-wheeler, with careful identification of directionally sensitive
Ganju, ShubhamV, VijayamirtharajPrasad, SathishR S, Mahenthran
Asian countries capture a significant share of global two-wheeler usage, with India consistently ranking among the top three countries. 2 wheelers are a significant portion of road traffic and contribute heavily to the national burden of road fatalities. Despite regulatory mandates, helmet non-compliance remains widespread due to limited enforcement reach and behavioural inertia. The current strategies for enforcement, such as traffic policing or external camera-based surveillance, are reactive, infrastructure-dependent, are ineffective at scale. To address these limitations, we propose system that will detect if the user is wearing the helmet. The system is designed and packaged to be integrated into the 2-wheeler directly and then execute functions in real-time for helmet noncompliance. The software algorithm is an AI-powered, vision-based system that leverages deep learning techniques for helmet detection. This model is enforced with a custombuilt dataset accommodating cultural and
Kandimalla, Om MahalakshmiShah, RavindraKarle, Ujjwala
Final design choices are frequently made early in the product development cycle in the fiercely competitive automotive sector. However, because of manufacturing tolerances design tolerances stiffness element fitment and other noise factors physical prototypes might show variations from nominal specifications. Significant performance differences (correlation gaps) between the digital twin representation produced during the design phase and real-world performance may result from these deviations. Measuring every system parameter repeatedly to take these variations into account can be expensive and impractical. The goal of this study is to identify important system parameters from system characteristic data produced by controlled dynamic testing to close the gap between digital and physical models. Dynamic load cases are carried out with a 4-poster test rig where vehicle responses are captured under controlled circumstances at different suspension locations. An ideal set of digital model
Verma, Rahul RanjanGoli, Naga Aswani KumarPrasad, Tej Pratap
Accurately determining the loads acting on a structure is critical for simulation tasks, especially in fatigue analysis. However, current methods for determining component loads using load cascade techniques and multi-body dynamics (MBD) simulation models have intrinsic accuracy constraints because of approximations and measurement uncertainties. Moreover, constructing precise MBD models is a time-consuming process, resulting in long turnaround times. Consequently, there is a pressing need for a more direct and precise approach to component load estimation that reduces efforts and time while enhancing accuracy. A novel solution has emerged to tackle these requirements by leveraging the structure itself as a load transducer [1]. Previous efforts in this direction faced challenges associated with cross-talk issues, but those obstacles have been overcome with the introduction of the "pseudo-inverse" concept. By combining the pseudo-inverse technique with the D-optimal algorithm
Pratap, RajatApte, Sr., AmolBabar, Ranjit
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