bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Digital Shearography for Dual Sensitivity Simultaneous Measurement Using Carrier Frequency Spatial Phase Shift Technology
Authors: Zheng Xiaowan , Guo Bicheng , Fang Siyuan , Sia Bernard , Yang Lianxiang
Published: April 2023

Digital shearography has many advantages, such as full-field, non-contact, high sensitivity, and good robustness. It was widely used to measure the deformation and strain of materials, also to the application of nondestructive testing (NDT). However, most digital sherography applications can only work in one field of view per measurement, and some small defects may not be detected as a result. Multiple measurements of different fields of view are needed to solve this issue, which will increase the measurement time and cost. The difficulty in performing multiple measurements may also increase for cases where the loading is not repeatable. Therefore, a system capable of measuring dual fields of view at the same time is necessary. The carrier frequency spatial phase shift method may be a good candidate to reach this goal because it can simultaneously record phase information of multiple images, e.g. two speckle interferograms with different fields of view. It then obtains the phase information of each interferogram by separating them from the spectrum using the Fourier Transform (FT) method. The challenge of using this method is that the phase information can partially overlap on the spectrogram, resulting in a bad phase map. This paper presents a new idea for separating the phase information on the spectrogram. The new idea adjusts the shearing directions, leading to complete separation of the spectrums of the two images, e.g. one in the horizontal direction and the other in the vertical direction. The phase information of each interferogram can then be excavated by windowing the corresponding spectrum and taking the inverse Fourier Transform. In digital shearography, the phase information is directly related to gradient of surface deformation; thus, gradient of surface deformation with different fields of view, also called dual sensitivity, can be obtained. The principle of this method will be described and demonstrated by experiment results.
A Subdomain Approach for Uncertainty Quantification of Long Time Horizon Random Processes
Authors: Mande Onkar , Mourelatos Zissimos , Papadimitriou Dimitrios
Published: April 2023

This paper addresses the uncertainty quantification of time-dependent problems excited by random processes represented by Karhunen Loeve (KL) expansion. The latter expresses a random process as a series of terms involving the dominant eigenvalues and eigenfunctions of the process covariance matrix weighted by samples of uncorrelated standard normal random variables. For many engineering appli bn vb nmcations, such as random vibrations, durability or fatigue, a long-time horizon is required for meaningful results. In this case however, a large number of KL terms is needed resulting in a very high computational effort for uncertainty propagation. This paper presents a new approach to generate time trajectories (sample functions) of a random process using KL expansion, if the time horizon (duration) is much larger than the process correlation length. Because the numerical cost of KL expansion increases drastically with the size of time horizon, we partition it into multiple subdomains of equal length (time), perform a KL expansion for only the first subdomain and then extend it to the remaining subdomains by imposing a correlation between the KLE coefficients of adjacent subdomains. Additionally, to ensure continuity at the junction between subdomains, a cubic spline interpolation is implemented. The proposed approach is demonstrated using two examples.
The Potential of Data-Driven Engineering Models: An Analysis Across Domains in the Automotive Development Process
Authors: Knödler Julian , Könen Christian , Muhl Philip , Rudolf Thomas , Sax Eric , Reuss Hans-Christian , Eckstein Lutz , Hohmann Sören
Published: April 2023

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains. In consequence, we show the necessities and benefits of adopting such models by analyzing the current methods used in industry. We derive commonalities in approaches and applications across domains to subsequently provide detailed case studies along the development cycle. Here, we highlight essential data acquisition concepts and suggest promising approaches for four different engineering use-cases, while pointing out limitations and pitfalls in application. Conclusively, we present our perspective on further challenges and opportunities in the evolution of the automotive industry in terms of data-driven system models for technical use-cases.
Model Based Systems Engineering Application in Automotive Industry
Authors: Alonso Liliana , Alvarez Ezequiel , Tolentino Cesare
Published: April 2023

Auto industry has faced constant challenges in the economic, technology and global trend in the recent years. This is changing the corporative mindset to find creative and innovative processes and methods to evolve the product development system to adjust and deliver competitive products that satisfy customers expectations.

Integrating the work from different teams in an organization has been moving from simple roles and responsibilities definition with effective communication channels to a new vision where teamwork progresses in harmony and embraces change to satisfy customers as part of the process.

The path to evolve work in engineering that relies on several computational tools continues. In this article, it is presented an integration of different tools to manage vehicle program changes using model-based systems engineering, the present work improves the reaction capabilities of the teams and enables to adjust to changes in the development of a vehicle.
Development of a New PVC-Gel Actuator for Implementing Emotional Vibration
Authors: Chang Kyoung-Jin , Kyung Ki-Uk , Kim Hyunwoo , Hong Sangjin , Park Dong Chul
Published: April 2023

Electro-hydraulic actuators, a type of soft actuators, can provide soft-touch vibrations due to their structural characteristics, but some problems need to be improved to apply them to vehicles. That is, it is necessary to increase excitation force, expand frequency band, lower driving voltage, and increase durability. This research aims to design a new type based on electro-hydraulic actuator and improve problems with its performance to develop a product that generates emotional vibration in vehicles. First, a new mechanism and design of an electro-hydraulic actuator called a PVC-gel film actuator are proposed. This actuator uses PVC-gel as a film which covers a dielectric liquid and uses carbon nanotube as a cathode material. In addition, a method of manufacturing an actuator with improved performance has been proposed by creating and testing prototypes with different sizes and material properties. It has been verified that the proposed actuator improves excitation force, frequency band, driving voltage, and durability compared to the existing actuator. Next, as a result of investigating the object that this actuator can exert the most effect on a vehicle based on body sensitivity, a device has been developed that built into a neck pillow in front of the vehicle seat and linked to the music played on the vehicle to generate a comfortable vibration in the neck. In addition, four vibration scenarios have been constructed to generate vibrations according to various genres of music. Finally, as a result of testing on a vehicle with a large number of people, it has been verified that the vibration generating device developed in this study can provide a new and emotional vibration experience for users.
Thermodynamic Modeling of Military Relevant Diesel Engines with 1-D Finite Element Piston Temperature Estimation
Authors: Gohn James , Gingrich Eric , Tess Michael , Korivi Vamshi , Yan Ziming , Gainey Brian , Filipi Zoran , Lawler Benjamin
Published: April 2023

In military applications, diesel engines are required to achieve high power outputs and therefore must operate at high loads. This high load operation leads to high piston component temperatures and heat rejection rates limiting the packaged power density of the powertrain. To help predict and understand these constraints, as well as their effects on performance, a thermodynamic engine model coupled to a finite element heat conduction solver is proposed and validated in this work. The finite element solver is used to calculate crank angle resolved, spatially averaged piston temperatures from in-cylinder heat transfer calculations. The calculated piston temperatures refine the heat transfer predictions as well requiring iteration between the thermodynamic model and finite element solver. Both the thermodynamics and the piston temperature predictions are validated against experimental data obtained from a heavy-duty single cylinder research engine equipped with a wireless telemetry system and piston surface thermocouples to measure piston surface temperatures. The piston backside conditions are critical to the performance of the temperature solver, therefore the tuning of piston backside conditions to match experimental data is considered and assessed. The validated model is then used to analyze the performance of the heat transfer correlations developed by Woschni and Hohenberg. The piston temperatures predicted by each of the correlations are compared to those measured in the experiment both in terms of the piston temperature swing and its sensitivity to injection timing. Finally, the capabilities of the coupled model are demonstrated by analyzing the effects of engine geometry on engine performance relative to critical limitations for military engines.
Containerization Approach for High-Fidelity Terramechanics Simulations
Authors: Jadhav Sanskruti Deepak , Salvi Ameya , Kosaraju Krishna Chaitanya , Smereka Jonathon , Brudnak Mark , Krovi Venkat N , Gorsich David
Published: April 2023

Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high.

High-fidelity simulators coupled with cloud-based containers can help ease the challenge of data ‘quality’ and ‘quantity’. Project Chrono is a multi-physics simulation engine that provides high-fidelity simulation capabilities with emphasis on flow and terrain modeling. With a host of libraries and APIs for industry accepted tools like MATLAB, Simulink and TensorFlow, Project Chrono proves to be a powerful research bed for data-driven modeling and control development for off-road navigation. Containers are lightweight virtual machines that take away repetitive configurations by setting up a computational environment, including all necessary dependencies and libraries. Docker encapsulates an end-to-end platform solution for heavy computation challenges of deep learning applications and allows fast development and testing. The synergy between the high-fidelity simulator and the compute outsourcing capabilities of cloud-based containers proves to be extremely beneficial for continuous integration and continuous deployment (CI/CD) for data driven modeling and control tasks. In the following work, we containerize a high-fidelity simulator (Project Chrono) to develop and validate data driven modeling and control algorithms for off-road autonomous navigation.
Military Unmanned Ground Vehicle Maneuver: A Review and Formulation
Authors: Whitson Jordan A. , Gorsich David , Vantsevich Vladimir V. , Letherwood Michael , Sapunkov Oleg , Moradi Lee
Published: April 2023

A state-of-the-art review of the technical meaning and application of the term ‘maneuver’, used by the U.S. Army and ground vehicle engineering communities, was performed with regard to various military activities, including modeling and simulation (M&S), to focus on the value and applicability of the term to military vehicle dynamics. As shown, U.S. military doctrine has built through history and experience a unique concept of maneuver-in-general and its application in U.S. Army unified land operations. Yet, the term ‘maneuver’ needs further technical categorization and characterization for the purpose of dynamics of military unmanned ground vehicles (UGVs) and vehicle design for maneuver. While the NHTSA and SAE standards and definitions provide solid foundations for M&S of cars and trucks to enhance the safety of those vehicles (manned and autonomous), occupants, and pedestrians on roads, the standards cannot address all needs of military vehicles in maneuver. Military UGVs are designed to operate in hyper-dynamic battlefield and tactical conditions on severe terrains where manned systems cannot operate. These operational conditions require a different approach to modeling, simulation, and real-time UGV-self-assessment of its dynamic behavior to be technically capable to fulfill autonomous missions and tasks for the sake of the safety of warfighters and the UGV itself. In the paper, a technical definition for a military vehicle maneuver is presented with the purpose of encompassing vehicle agile movements with extended safety due to controllable instability and also unsafe movements on a need basis. Sub-element definitions of a vehicle maneuver and new ideation of agile movement is proposed to narrow the scope to vehicle military tasks in austere environments. Along with formulation, a graphical interpretation is provided to illustrate advantages of the proposed approach for planning UGV motion using geometric and kinematics characteristics. The contextual application is shown in an operation study to illustrate where the terms can improve M&S.
Control Strategy for All-Wheel Cooperative Steering of Multi-Axle Vehicle
Authors: Li Jiaqi , Liu Yahui , Qu Pengshuai
Published: April 2023

Applications in commercial and military fields created high demands on the steering performance of multi-axle vehicle. With the characteristic of more degrees of freedom (DOF), all-wheel cooperative steering is more conducive to improve the steering performance of multi-axle vehicle. This paper studies multi-axle vehicle assembled with steer-by-wire system, and proposes a control strategy to achieve all-wheel cooperative steering to improve the low-speed steering flexibility and high-speed steering stability of multi-axle vehicle. Based on the ideal steering performance at low-speed and high-speed, the steady-state gain of multi-axle vehicles at different speeds is reshaped. Also, the corresponding vehicle reference model is constructed to provide the ideal vehicle state as a reference. The precision of the vehicle reference model is verified by an all-wheel independent steering platform. Accordingly, the state feedback control module which contains a sliding mode controller and a disturbance observer is designed to make the actual state of the vehicle track the designed idea state, which is verified under a variety of path tracking conditions in the simulation environment. Simulation results show that the control strategy proposed for multi-axle vehicle can achieve all-wheel cooperative steering, improving the low-speed steering flexibility and high-speed steering stability of multi-axle vehicle.
Data-driven Estimation of Tire Cornering Stiffness: A Dynamic Mode Decomposition Approach
Authors: Chen Hao , Lv Chen
Published: April 2023

Accurate information about tire cornering stiffness is essential for the implementation of advanced vehicular control systems. Data-driven modelling method leverages the availability of high-quality measurement data alone, without vehicle parameters, which provides a tutorial to reconstruct the system dynamics and estimate tire cornering stiffness. As such, we collect the states and inputs of the vehicle to build its state space using the dynamic mode decomposition (DMD) method. Then, based on the entries of the system and input matrix, the tire cornering stiffness can be further identified by solving the linear equations via orthogonal regression with considering the measurement noise. The sufficient and necessary rank condition for the DMD execution is also analyzed. Additionally, we introduce two alternative ways to update the system and input matrices - recursive least squares (RLS) and sliding window (SW). Finally, the simulation tests are conducted by CarSim-Simulink to compare the proposed data-driven solution to the extended Kalman filter (EKF). The validation results indicate that the DMD method is capable of reconstructing vehicle lateral dynamics in linear regions and achieves comparable performance to the EKF without requiring prior tire forces. Specifically, the SW offers the best performance for tracking the time-varying system and input matrices. The RLS, in comparison, demonstrates a slow response with satisfactory numerical stability.
Identification of the Nonlinear Dynamic Behavior of Magnetorheological Fluid Dampers using Adaptive Neuro-Fuzzy Inference System
Authors: Abd Elwahed Amr , Metered Hassan , Monieb Hany
Published: April 2023

Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance. Validation results with several data sets indicate that the proposed direct and inverse identification models using ANFIS can be used to predict the nonlinear dynamic performance of MR fluid dampers accurately and can work as a damper controller.
Application of Model Predictive Control to Cabin Climate Control Leading to Increased Electric Vehicle Range
Authors: Fussey Peter , Ma He , Dutta Nilabza
Published: April 2023

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range.

To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems. Furthermore, the controller must satisfy different control requirements by leveraging multiple control actuators whilst simultaneously respecting multiple control and system constraints. A Model Predictive Controller has been used to reduce the control complexity of a conventional controller that requires many heuristic control laws and significant calibration effort. The control solution has been realised for an industrialized application, respecting typical embedded control hardware solutions, minimizing computational effort and memory requirements.

This paper introduces a development approach utilizing Adaptive Model Predictive Control (AMPC) to address the challenges above, leading to the demonstration in a vehicle (Jaguar I-PACE) over the UDDS cycle on a climatic chassis dynamometer. The structured development approach improves the efficiency and flexibility of setting-up advanced control solutions.
Energy Consumption of Titanium Dioxide & Ultraviolet Germicidal (UV-C) Photocatalytic Air Purification System for Automotive Cabins
Authors: Mathur Gursaran
Published: April 2023

The author has been conducting research on UV based photocatalytic air purifier systems for the past 5 years to eliminate living organic germs, bacteria, pathogens, etc. from the cabin air. An HVAC system has been developed by using a filter impregnated by titanium di-oxide (TiO2) with UV lights to improve and maintain cabin air quality. The author has designed and constructed a 3rd generation HVAC unit for cabin air purification for automobiles that is based on UV photocatalytic process by using UV-C LEDs to eliminate viruses that typically exist in conditioned space.

The author has conducted tests with HVAC unit to determine power consumptions of air purification systems.

An HVAC unit that employs a HEPA (high efficiency particulate air filter) filter is compared with the same HVAC unit with UV & titanium dioxide based photocatalytic system. The pressure drops of the HEPA, particulate and TiO2 filters have been investigated that contribute to the overall energy consumption. The energy consumption of the UV-C & UV-A LEDs are also taken into account for the overall energy consumption analysis and comparison with the base system. Tests were conducted in the laboratory to determine pressure drops of the above two systems to compute energy consumption.

Based on the testing, the measured power consumption of an HVAC unit with a HEPA filter is on average higher by 57% (27~71% over the airflow range) over an UV based photocatalysis system. Detailed test data and analysis is presented in the paper.

The designed system can be used for conventional vehicles, EVs, ride sharing and for autonomous vehicles.
Predictive Model Development Using Machine Learning for Engine Cranktrain System
Authors: S Aravamuthan , Sharma Ayush , K Karthikeyan , S Kangde Suhas
Published: April 2023

Highly competitive automotive market demands shorter product development cycle while maintaining higher standards of performance in terms of durability and Noise Vibration & Harness (NVH). Engine cranktrain system is one of the major vibration sources in engine and first torsional mode frequency is a key parameter which influences vibration characteristics. Current CAE (Computer Aided Engineering) workflow for evaluating cranktrain system performance is time-consuming and takes around 55 Hrs. It involves crankshaft geometry cleanup, stiffness calculation, 1D model building and post processing. Over the time, significant historical data has been created while performing this virtual simulation during the product development cycle. Having a trained Machine Learning (ML) model based on this historical data, which can predict first torsional mode frequency accelerates the virtual validation. In this paper, prediction of first torsional frequency of cranktrain system using ML is presented. Altair’s Knowledge Studio is used for ML model building, and predictions are compared with CAE results. Based on domain expertise, feature selection and data cleanup are performed. Predictions with linear regression and deep learning (DL) algorithms are compared and hyperparameters are manually optimized for obtaining right balance between a reliable model and its predictions within acceptable error. The ML model based on DL algorithm delivered more than 90% variance-explained on training and test database. Also, more than 85% predictions on test database predicted performance within 15% error. Torsional Vibration Damper (TVD) ring inertia, pulley inertia and crankpin mass per conrod are found to be the most influencing parameters for the first torsional mode. Prediction using DL model is in good agreement (< 10 % error) with CAE results. Overall, the developed predictive model using DL algorithm helped in achieving up to 70%-time reduction with respect to current CAE cycle.
Prediction of Buckling and Maximum Displacement of Hood Oilcanning Using Machine Learning
Authors: S Aravamuthan , S Kangde Suhas
Published: April 2023

Modern day automotive market demands shorter time to market. Traditional product development involves design, virtual simulation, testing and launch. Considerable amount of time being spent on virtual validation phase of product development cycle can be saved by implementing machine learning based predictive models for key performance predictions instead of traditional CAE. Durability oil canning loadcase for vehicle hood which impacts outer styling and involves time consuming CAE workflow takes around 11 days to complete analysis at all locations. Historical oil canning CAE results can be used to build ML model and predict key oil canning performances. This enables faster decision making and first-time right design.

In this paper, prediction of buckling behaviour and maximum displacement of vehicle hood using ML based predictive model are presented. Key results from past CAE analysis are used for training and validating the predictive model. Commercially available tool is used, and predictions are compared with CAE results. Based on domain expertise, features are selected and cleaned up to make it suitable for training the predictive model. Different algorithms based on ROM (Reduced Order Modelling) and POD (Proper Orthogonal Decomposition) are used for prediction and the best performing algorithm and it’s hyperparameters are selected based on loss function (R2) and acceptable error.

Prediction using Neural Network consists of multi quadratic radial basis function (RBF) which is in good agreement (< 20 % error) with CAE predictions, and it can be improved further by adding more data into the training database. With this predictive model, maximum displacement and buckling can be predicted within 30 mins which resulted in 99% turnaround time savings when compared to existing CAE workflow.
Virtual Validation of Automotive Electronic Products for Climatic Test Conditions
Authors: Kumar Vinay , Viswanathan Swaminathan
Published: April 2023

In modern automobiles a complex network of electronic sensors and controls is being integrated for increased comfort, convenience, and safety. All of these needs to be designed for the stringent environmental condition requirements. Environmental tests used for validation of product primarily consists of combination of Vibration load, Temperature and Humidity. Failures induced by vibration Load and temperature cycling are fairly well understood and often simulation can help design team to understand weakness in design and evaluate design options to mitigate it. However, Humidity and temperature (cyclic or constant) are critical as well referred as Climatic tests. The purpose of climatic tests are to assess the ability of a product to operate reliably under condensing conditions. Unlike other environmental test where there are visual clues of something broken, these test could lead to failure without any visual clues. Failures are intermittent in nature as they are driven (among other things) by presence of water on Printed Circuit Board (PCB). With moisture or water present on board they might malfunction but fault will disappear with evaporation of water. Examples of these failures are presence of dendrites due to Electrochemical migration (ECM), aqueous corrosion or sudden malfunction of intermittent nature. These are primarily resolved after actual validation test and almost no simulation is performed for up-front prediction of possible design issues. Hence there is a fundamental need to understand simulation of climatic test and predicting the regions of condensation on PCB. The objective of this paper is to demonstrate simulation methodology to predict the moisture condensation on PCB during combined temperature humidity test. The scope of the study focuses on qualitative correlation of the simulation predicted condensation location and experimentally observed results to build confidence in the simulation approach. The design sensitivity study also has been conducted to investigate the variation in response due to change in environmental and design conditions like device power numbers, device switch on and off frequency, Thermal cycling ramp rate and ambient humidity.
High-Fidelity CAE Simulation of 4-Cylinder 4-Stroke Hollow Assembled Camshaft under Multi Axial Load
Authors: K Karthikeyan , S Aravamuthan , Nair Akhilsen , Dharan R Bharani , Yadav Vivek
Published: April 2023

The major area in which the automotive manufacturers are working is to produce high-performance vehicles with lighter weight, higher fuel economy and lower emissions. In this regard, hollow camshafts are widely used in modern diesel and gasoline engines due to their inherent advantages of less rotational inertia, less friction, less weight and better design flexibility. However, the dynamic loads of chain system, valve train and fuel injection pump (if applicable) makes it challenging to design over-head hollow camshafts with the required factor of safety (FOS).

In the present work, high-fidelity FE model of a hollow camshaft assembly is simulated to evaluate the structural performance for assembly loads, valve train operating loads, fuel injection pump loads and chain system loads. The investigation is carried out in a high power-density (70 kW/lit) 4-cylinder in-line diesel engine. The camshaft is used for operating the intake valves which induce varying stresses in-line with the engine firing order. Moreover, the camshaft is also used to drive the high-pressure fuel injection pump (FIP) at the rear-end which can add significant torsional stresses. Furthermore, the stresses induced by the hub-loads of timing chain is found to be having a significant effect on the bending behavior of the front-end of the camshaft. In addition to these operating stresses, the camshaft is subjected to different kinds of mean stresses induced by the bolt (used to fasten the drive-sprocket) and interference fit of the camshaft child parts (cam and front plug). Hence, the authors propose a robust and reliable evaluation methodology to evaluate the structural performance and factor of safety (FOS). The dynamic bending behavior of the camshaft under press-fit loads of cam lobes and front plug is discussed. The present work also covers the load-path and multi axial stress state induced on the hollow camshaft under varying load conditions apart from estimating the fatigue life. Moreover, the investigation includes the assessment of different parameters influencing the stress multi-axiality on the camshaft to arrive at potential improvements in the camshaft design. Overall, the results arrived using this methodology is found to be having a good correlation with the parts used for durability testing. Thus, the proposed methodology can be used for evaluating hollow camshafts of modern engines subjected to complex and highly dynamic loads.
Perspectives on the Transition from Hardware-Based Validation and Product Evaluation to Virtual Processes
Authors: Schudt Joseph , Schell Brian
Published: April 2023

Accelerating product development cycles and incentives to reduce costs in product development are strong motivators to move to virtual development and validation processes. Challenges to moving to a virtual paradigm include a wealth of historical data and context for hardware tests, uncertainty over dependencies, and a lack of a clear path of transition to virtual methods. In this paper we will discuss approaches to understanding the value created by hardware tests and aligning that value to virtual processes. We will also discuss the need for a virtual context to be added to SAE J1739 [1] (DFMEA detection criteria), and how to create paths to maximize the value of virtual assessments. Finally, we will also discuss the cultural and organizational changes required to support.
Characterization of Vertical Dynamics of a Multi-Purpose Tractor with Static and Dynamic Experimental Tests
Authors: Belloni Mattia , Vignati Michele , Sabbioni Edoardo , Cutini Maurizio
Published: April 2023

Multi-purpose agricultural tractors are vehicles that are usually used in rough paths and on off-road situations characterized by strong slope variations. The main feature of this kind of vehicles is the stability in working conditions to avoid overturning while it is on duty. This characteristic is given by the interaction between the suspension system and the vehicle frame. Due to the limited size of this kind of vehicle, the stability feature could be given by chassis deformation or using a two-piece frame connected by a spherical joint. This paper presents the validation of a numerical lumped-parameters model able to reproduce the vertical dynamics of a multi-purpose tractor featured by a yielding chassis. The unknown model parameters have been estimated firstly with static tests to study the vertical tire and suspension stiffnesses. The dynamic tests using a four-post-test rig have been performed to tune the unknown dynamic parameters. Finally, the model has been validated with some in-field acquisition to reproduce the real vehicle working conditions.
Zero-dimensional Modeling of Flame Propagation During Combustion of Natural Gas/Hydrogen Mixtures
Authors: Oppl Thomas , Pirker Gerhard , Wimmer Andreas , Wohlthan Michael
Published: April 2023

To achieve global climate goals, greenhouse gas emissions must be drastically reduced. The energy and transportation sectors are responsible for about one third of the greenhouse gases emitted worldwide, and they often use internal combustion engines (ICE). One effective way to decarbonize ICEs may be to replace carbon-containing fossil fuels such as natural gas entirely, or at least partially, with hydrogen. Cost-effective development of sustainable combustion concepts for hydrogen and natural gas/hydrogen mixtures in ICEs requires the intensive use of fast and robust simulation tools for prediction. The key challenge is appropriate modeling of flame front propagation.

This paper evaluates and applies different approaches to modeling laminar flame speeds from the literature. Both appropriate models and reaction kinetic calculations are considered. The most promising approaches are then combined with various approaches for turbulent combustion as the basis for determining zero-dimensional burn rates. In developing these approaches, one particular challenge is to make the models able to correctly represent flame front propagation with a 0-100% share of hydrogen in the mixture as well as consider the resulting effects. In particular, the destabilizing effects of an increasing share of hydrogen, which can be described by the Lewis number not equal to one, are examined.

Finally, validation of the model results involved not only classic comparison of analyzed cylinder pressure curves and other engine performance parameters but also direct comparison of measured and modeled flame front propagation based on experimental measurement data from ion current probes. The measurements were made on a single-cylinder research engine with a cylinder head equipped with ion current probes attached to the spark plug sleeve around the spark plug.
Combined Physical and ANN-Based Engine Model of a Turbo-Charged DI Gasoline Engine with Variable Valve Timing
Authors: Wei Jingsi , Liu Mingjia , Angerbauer Michael , Yang Qirui , Xu Hanjun , Grill Michael , Kulzer André , Chen Ceyuan
Published: April 2023

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode. The data set covers wide variation ranges of boundary and operating conditions in the combustion chamber including variable valve timings. A special neural network structure was configurated, which consists of five interconnected gated recurrent unit (GRU) sub-networks for calculating mass fuel burned, pressure values, the peak pressure position, NO emissions as well as knock condition. To form a whole working cycle simulation within GT-SUITE, the neural networks were converted into a functional mock-up unit (FMU), which is connected with the physics-based low-pressure part through FKFS RapidCylinder®. A performance evaluation of the hybrid engine model shows that, the mean deviations of brake torque, MFB50 and NO emissions compared to the physics-based reference mode are respectively 0.529%, 0.048°CA and 32.67ppm over the whole engine characteristic map, indicating an equal calculation quality. While maintaining the calculation accuracy, the neural networks with FMU connections realize a combustion chamber calculation 5- to 10-fold faster than real-time and an acceleration of the whole engine calculation of up to 80% compared to the completely physics-based simulation.
Modelling of a Solenoid Injector for Internal Combustion Engines and Simulation Using the Bond Graph Methodology
Authors: Gaucherand Jessica , Pedersen Eilif , Netzer Corinna , Lovas Terese
Published: April 2023

In internal combustion engines, fuel injection timing, injection rate and pressure is optimized to ensure suitable combustion and reduce emissions. Injectors are complex systems where mechanical, electromagnetic, and fluid dynamics interact together. Numerical model development of injectors allows for investigating different conditions, as well as optimization of the system in a safe manner. In this study, a 1-dimensional (1D) mathematical model of a direct gasoline injector (GDI) is presented, supported by computational fluid dynamics (CFD) in-nozzle flow simulations. The described system is a commercially available injector where the internal geometry was captured using silicone molds of the nozzle. The model includes the representation and interaction of the different components across several domains using the bond graph methodology. In the injector, the needle is magnetic and is lifted when an electromagnetic field is activated. When the magnetic force becomes stronger than the spring force and the fuel’s pressure pushing the needle downward, the needle lifts and fuel flows through the nozzle and out of the injector. These motions are described by the mathematical model, for which the magnetic signal serves as input. The model output provides the needle lift and information on the fluid flow, including the pressure, temperature, and mass flow rate. Gaseous methane and liquid ammonia injection are investigated in separate models where the thermofluid part of the model is changed to account for a compressible gas, which in turn follows the perfect gas law in one case, and an incompressible liquid in the other case. Comparison with literature shows that the model captures the dynamics of the needle lift well, and the mass flow is accurately predicted.
An Updated Comprehensive Chemical Kinetic Mechanism for Ammonia and its Blends with Hydrogen, Methanol, and
N
-Heptane
Authors: Hamdy Mohamed , Nadiri Solmaz , Mohamed Ahmed , Dong Shijun , Wu Yuxiang , Fernandes Ravi , Zhou Chongwen , Liu Shuaishuai , Senecal Kelly , Zhang Kuiwen , Curran Henry
Published: April 2023

Ammonia is a promising carbon-free alternative fuel for use in combustion systems. The main associated challenges are its relatively low reactivity and high NOx emissions compared to conventional fuels. Therefore, the combustion behaviour of ammonia and ammonia blends still needs to be better understood over a wide range of conditions. To this end, a comprehensive chemical kinetic mechanism C3MechV3.4, which is an update of C3MechV3.3, has been developed for improved predictions of the combustion of ammonia and ammonia blends. C3MechV3.4 has been validated using a wide range of experimental results for pure ammonia and ammonia/hydrogen, ammonia/methanol and ammonia/n-heptane blends. These validations target different data sets including ignition delay times, species profiles measured as a function of time, and/or temperature and laminar flame speeds over a wide range of conditions. The updated developed mechanism gives good predictions for pure ammonia and its blends with hydrogen, methanol and n-heptane. The most important reactions affecting predictions in different regimes for the various ammonia mixtures are discussed.
Feature Extraction from a Crankshaft Instantaneous Speed Signal of an Automotive Power Unit using Cepstrum Analysis
Authors: El Yacoubi Ismail , Samuel Stephen
Published: April 2023

Internal combustion (IC) engines are the most common power unit technology found in road vehicles. The process of combustion within IC engines is linked to the output torque and overall powertrain performance. This work presents a method of analysing the parameters of cylinder pressure and crankshaft instantaneous speed signals obtained from a turbocharged, 4-stroke, 4-cylinder, 1.6 Litre, spark ignition, gasoline direct injection engine at various speed and load operating conditions. Whereas cepstrum analysis is used in the present work to extract critical features characterising the combustion process. Cepstrum analysis showed that the location of maximum heat release can be directly obtained from the quefrency of the instantaneous crank speed. This paper presents a systematic scheme for applying cepstrum for obtaining combustion features from the instantaneous crank speed signal.
Evaluation of Longitudinal ADAS Functions for Fuel Economy Improvement of Class 8 Long Haul Trucks
Authors: Paul Sumit , Goyal Vasu , Joshi Satyum , Franke Michael , Tomazic Dean , Zeman Jonathan
Published: April 2023

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost.

With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication. Upcoming road grade, traffic lights and lead vehicle speeds can be utilized to optimize vehicle speed profile, energy management and thermal management strategies. While many studies have been conducted in the past to evaluate control strategy changes based on longer time horizon, limited studies have been conducted to evaluate shorter time horizon strategies that dynamically adjust vehicle speed (or suggest vehicle speed) for fuel efficiency. In this study, FEV North America, Inc. has applied a model-based approach to evaluate the fuel economy improvement potential of a connected electrified Class 8 long-haul truck. A system-level 1-D propulsion and thermal system model of an electrified Class 8 truck was simulated in real-world conditions including traffic lights, multiple lead vehicle and varying road grades using GT-SUITE. The look-ahead information on road grade, traffic light schedule and lead vehicle speeds were assumed to be available through GPS, V2X communication and long-range radar sensors. A system-level 1-D propulsion model of a Class 8 truck was developed and simulated in real-world driving conditions including traffic lights, multiple lead vehicles, and varying road grade using GT-SUITE. The look-ahead information on road grade, traffic light schedule, and lead vehicle states were assumed to be available through GPS, V2X communication, and long-range radar sensors. The connectivity information was used to implement ADAS features like Predictive Cruise Control (PCC), Advanced Adaptive Cruise Control (AACC), and Eco-Approach (EA) to optimize the vehicle target speed and evaluate their combined fuel economy benefit on a real-world drive cycle.