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The automotive industry is moving towards larger SUVs and also electrification is a need to meet the carbon neutrality target. As a result, we see an increase in overall gross vehicle weight (GVW), with the additional weight coming from the HV battery pack, electric powertrain, and other electrical systems. Tow-eye is an essential component that is provided with every vehicle to use for towing during an emergency vehicle breakdown. The tow-eye is usually connected to the retainer/sleeve available in the bumper system and towed using the recovery vehicle or other car with towing provision. Therefore, the tow-eye should meet the functional targets under standard operating conditions. This study is mainly for cars with bumper and tow-eye sleeves made of aluminum which is used in the most recent development of vehicles for weight-saving opportunities. Tow-eye systems in aluminum bumpers are designed to avoid any bending or buckling of the sleeve during towing for whatever the GVW loads. So that the vehicle doesn’t face any tow-eye system failure, which would prevent another car from towing the breakdown vehicle and would need special roadside assistance to take it for servicing. This paper uses the Design For Six Sigma (Taguchi Method) approach to identify the potential control factors (from the benchmarking study) to optimize the tow-eye sleeve to withstand higher GVW load without functional loss.Furthermore, this approach proved that the selected optimum design is less sensitive to noise factors such as aluminum property variation, bumper beam thickness variation, and load direction variation. The learnings from this paper will help to reduce the development time and cost by implementing the robust sleeve design in the early stage of the program.

Platooning has produced significant energy savings for vehicles in a controlled environment. However, the impact of real-world disturbances, such as grade and interactions with passenger vehicles, has not been sufficiently characterized. Follower vehicles in a platoon operate with both different aerodynamic drag and different velocity traces than while driving alone. While aerodynamic drag reduction usually dominates the change in energy consumption for platooning vehicles, the dynamics imposed on the follow vehicle by the lead vehicle and exogenous disturbances impacting the platoon can negate aerodynamic energy savings. In this paper, a methodology is proposed to link the change in longitudinal platooning dynamics with the energy consumption of a platoon follower in real time. This is accomplished by subtracting a predicted acceleration from measured longitudinal acceleration. The real-time consumption calculation methodology is evaluated using data from simulated and experimental platoons. The proposed methodology allows active deceleration losses to be calculated for a platoon follower in real time and is a development of the active deceleration theory presented by the authors in SAE Paper 2022-01-0526. In simulation, energy losses calculated by the method were within 5% of the true value and were robust to errors in modeled aerodynamic drag. As for the experimental results, the method agreed with the prior procedure of SAE Paper 2022-01-0526, which required extensive datasets and could only be completed as a post-processing routine. This novel methodology provides an important new feedback metric for platoon operators, and makes it possible to analyze real-time platooning benefit while the platoon is on the road.

Now for past many decades, automotive manufacturing is under perennial pressure owing to consumer aspirations, regulations, globalized competition and sustainability needs. Automotive manufactures are fiercely satisfying all of above constraints through innovative solutions. The resultant vehicles have many complex to very complex systems and subsystems to deliver the performance aspirations. In this game, one of the critical business parameter is rapid time to market. At the current state of the art, time to market directly conflicts with new product development time. Four wheeler new product development consumes upward of 48 months, while risking obsolescence of product features at the time of launch. Automotive manufacturers invest intensely in technologies to achieve this goal of short time to market. Digital technologies delivering high time efficiency have made deep in roads in the automotive product development arena. However yet physical validation is an inevitable step to get vehicle ready for launch. Meaningful validations require functional prototypes. Products being complex in nature, significant lead-time and investments are consumed in prototyping. Conventional part or tool manufacturing works as counterproductive for cost as well as time needs. Hence one more time shortening technique is to build functional prototype for a fixed purpose (validation intent). Built for purpose prototypes deliver extremely high time efficiency, but demand highly skilled prototype manufacturing engineers and technicians. Such rare teams work innovatively combining art and science to compress time lines. This technical paper focuses on smart use of quick manufacturing processes inclusive but not restricting to AM for advancing the product development. The achievement lies in using AM along with allied technologies to achieve the desired fit for purpose functional prototype rapidly. Innovated method would also yield a cost advantage as against the standard prototyping/manufacturing processes. Paper would also discuss on the possibilities of takes these learning for mass manufacturing.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

Transport electrification is pushing the automotive and aerospace industries to enhance the power density of their powertrains further and further. One of the technologies currently pursued by some companies is high-speed electric motors. For instance, the new Model S Plaid motor by Tesla has a carbon-fiber wrapped IPM (Interior Permanent Magnet) rotor which can exceed 20,000rpm. The SPX88-120 made by Helix company shows a power density of about 18kW/kg at 50,000rpm. However, such high rotating speeds result is huge mechanical stresses in the entire rotating assembly, thus making the structural design of these parts extremely challenging. The primary goal of this paper is to provide a scientific rationale for the effective Finite Element Modeling (FEM) and integration strategies to maximize the rotating assembly durability of a 150kW radial flux SPMSM (surface-mounted permanent magnet synchronous motor) considered as a case-study. A non-linear simulation requires the input of a stress-strain curve and modified power law hardening study is conducted. The secondary goal of the paper is to analyze the thermal stress risers for multiphysics optimization of components. An analytical methodology to estimate the fatigue life for fully reverse cyclic loading is expressed. An extensive study on the eigen mode shape and frequency was performed to understand the dominant frequency of the system. A comparative performance study is conducted on shaft critical speeds, modal analysis, and stiffness interaction between components. Multiphysics optimization of topology is undertaken, the principal stresses in significant load-bearing components are reduced by 10 to 33%.

The reactionless drive is an internal momentum engine which until recently has been deemed impossible under the laws of physics. In this paper, the authors will extend the equation for reaction less propulsion =F=−μq2/6πcmr2v×dBdt+B×dvdtand derive an additional equation, which we call “The Sektet Equation” governing the system of motion,FSek=−μq2/6πcmr2∗2B∗dBdt.The results of the paper show that significant thrusts can be generated on relatively low voltages and energy inputs. It applies this equation to explain how NASA’s EM drive likely produces thrust via the “Sektet Equation” using a three circuit analysis of the Sektet Force.

The development of Digital Twin (DT) has become popular. A dominant description of DT is that it is a software representation that mimics a physical object to portray its real-world performance and operating conditions of an asset. It uses near real-time data captured from the asset and enables proactive optimal operation decisions. There are many other definitions of DT, but not many explicit evaluations of DT performance found in literature. The authors have an interest to investigate and evaluate the quality and stability of appropriate DT techniques in real world aircraft Maintenance, Repair, and overhaul (MRO) activities. This paper reviews the origin of DT concept, the evolution and development of recent DT technologies. Examples of DTs in aircraft systems and transferable knowledge in related vehicle industries are collated. The paper contrasts the benefits and bottlenecks of the two categories of DT methods, Data-Driven (DDDT) and Model-Based (MBDT) models. The paper evaluates the applicability of the two models to represent vehicle system management. The authors present their methodological approach on Predictive Maintenance (PM) development basing on reliable DT models for vehicle systems. This paper contributes to design, operation, and support of aircraft/vehicle systems.

The Safety Assessment Process defined by SAE ARP4761 [1] and associated regulatory guidance and the system development process defined by SAE ARP4754 [2] are built on an understanding of the functions performed by a system or systems. SAE Technical Paper 2022-01-0008 [3] proposes a process to assist the system or product developer with identifying and describing functions at each level of abstraction used in describing the architecture.This paper walks through the process described in SAE Technical Paper 2022-01-0008, examining some of the issues and considerations encountered using this approach, and resulting in an example function list for a passenger aircraft. The example aircraft is typical except that an autonomous operating mode is included.

There has been an ongoing need to increase the application of industrial robots to complete high-accuracy aerospace manufacturing and assembly tasks. However, the success of this is dependent on the ability of robotic systems to meet the tolerance requirements of the sector. Machine learning (ML) robot error compensation models have the potential to address this challenge. Artificial neural networks (ANNs) have been successful in increasing the accuracy of industrial robots. However, they have not always brought robotic accuracy within typical aerospace tolerances. Methods that have not yet been investigated to further optimize the use ANNs used in ML robot error compensation methods are presented in this paper. The focus of ML compensation methods has dominantly surrounded ANNs; there have been little to no investigations into other types of ML algorithms for their suitability as robot error compensation models. The success of ANNs to date proves the capability of ML algorithms for this task, and therefore other ML algorithms should be investigated to determine their capability to potentially improve industrial robot accuracy. This paper takes a novel approach by investigating the Support Vector Regression (SVR) ML algorithm to compensate for robot error. The ML models in this research were trained using measurement data captured using a laser tracker and collaborative robot. The ANN model reduced the mean error by 46.4%, 94.8%, and 95.8%, in the x, y, and z-axis, respectively. The SVR model reduced the mean error by 42.4%, 95.9%, and 98.4%, in the x, y, and z-axis, respectively, demonstrating its ability to be implemented as a robotic error compensation model. The success of both the ANN and SVR algorithms enforces the need for further research into other ML algorithms as robot error compensation models, and there is also still potential to further optimize the algorithms used.

This paper focuses on a case study to compare the performances of Fowler wing flaps that are used on the trailing edge of modern-day commercial airliners. The aim is to observe trends in coefficients of lift (Cl) and drag (Cd) with varying flap angles of release on a single-slotted Fowler flap and arrive at the most efficient flap configuration for flight. A series of two-dimensional analyses are carried out using computational fluid dynamics (CFD) to examine the flow separation and occurrence of stalls between the different angles of flap deflection. A two-equation, k-ω shear stress transport (SST) turbulence model is used as it helps in better prediction of flow separation and boundary layer studies. Since the study is carried out for such passenger carriers, the study focuses on the lower transonic ranges of Mach number 0.7-0.9 with a Reynolds number range of 400,000 to 500,000 considering a scaled-down model and upon taking inspiration from related literature. The flaps are analyzed at various angles of release (α) such as 0°, 15°, 25°and 50°, which are selected considering the angles that they are commonly deflected to during take-off and landing scenarios. A NACA0012 symmetrical airfoil is chosen with a fixed chord length for the wing and kept at 0° Angle of Attack (AoA) with subsequent trailing edge Fowler flaps employed for examination. A data-driven approach is followed for the investigation of characteristics represented by the different designs of the Fowler flap, thus typical lift curve plots (Cl vs α), drag plots (Cd vs α), and lift-to-drag ratio (Cl/Cd) plots (Cl/Cd vs α) graphs are presented to understand the comparative study. The results are discussed with respect to the data observed in the graphs and a viable conceptual configuration for the single-slotted Fowler flap is selected. This insight, research, and design have led to the development of the research paper and it is hoped that this comparative study can be used to conduct further research in the field of aeronautics.

There is a demonstrated need for effective design verification testing to support certification strategies for nascent electric motors and electric propulsion systems. Design efforts pursue efficiency pushing electrified propulsion rotors to be lighter and incorporate greater power density; however, there is no clear path established for supporting structural integrity and durability test requirements as required by global certification agencies.Application of new materials, unique rotor design characteristics, and modified certification requirements drive unusual requirements for rotor modeling substantiated by component test data that addresses complex stress distribution characteristics.Our paper addresses testing electrified propulsion rotors using spin test protocols adapted to support integrity and durability test goals. We further incorporate key concerns for planning and executing component spin tests of rotating structures necessary to support global engine certification efforts. Results from component tests are effective for mitigating risks associated with the preservation of certification test schedules and potential end product safety issues.This paper presents data measurement techniques that are incorporated with specific spin tests to enhance the value of acquired data. Adapted test protocols include Overspeed and Low cycle fatigue (LCF) tests, which are more relevant types of spin tests for certification purposes.Innovative measurement techniques for capturing the rotor growth behavior for both mappings the speed-dependent growth trends and the circumferential profile of deforming rotor under centrifugal load generate useful data to evaluate materials and structural behavior affecting performance up to failure.

Refueling operation of the aircraft fuel tanks has some limitations. One of the limitations is refueling time which restricts refueling duration for entire tank. Other one is overfilling situations which are also possible because of the wave damper designs in tank such as barriers and baffles resist against fuel creeping towards all sides of tank. Required refueling duration restricts refueling speed at a certain minimum value. On the other hand, baffle design restricts refueling speed at a certain maximum value. It should be the mathematical region between these two extremum points where the refueling mass flow rate can be defined. Minimum mass flow rate point can be adjusted with defining of mass flow rate depends of requirements easily but upper extremum point should be defined depends on design of baffles. It can only be changed with altering the design of interior wing tank. In that paper mathematical model of the tank interior volumes and its components are established in order to simulate refueling operations with sufficient baffle cutout designs and also sufficient refueling duration. Usage of Catia script files help the simulation about initial geometrical values with scanning CAD model of wing tank interior volume. After gaining initial function and values, MATLAB Simulink program is main script editor and model creator for calculating iterations. After calculations it can be gain possible cutout diameter and location parameters which are eligible both refuel required duration and overfilling criteria. In this paper efficient cutout set and baffle design is generated with this script set and iterative methods.

The electromagnetic compatibility problem of the permanent magnet synchronous motor (PMSM) has become increasingly prominent with its continuous development to high power and high torque. To solve this problem, this paper adopts a method based on the establishment of body structure and windings of the PMSM to analyze its electromagnetic radiation (EMR). The radiation stimulus source is acquired by establishing the control model of the PMSM drive system under different driving conditions in Simulink and Carsim. The EMR model of the whole vehicle is established in FEKO by creating winding models and importing three-dimensional (3D) mesh models of the vehicle body and PMSM. The field circuit co-simulation and transmission line method are used in this paper. Finally, we can obtain the electric field radiation strength at different detection points under different driving conditions. The simulation results correspond with the EMR theory and electromagnetic shielding theory. It plays a leading role in the EMR analysis of the PMSM drive system in an electric vehicle.

Plug-in Hybrid Electric Vehicles (PHEVs) achieve significant fuel economy by utilizing advanced energy management strategies in controlling the power distribution decision in real-time. Traditional heuristic approaches bring no additional benefits, including efficiency and development cost, considering the increasing complexity in control objectives. This paper extends a previous study of the same problem (RL) and vehicle topology to develop a Reinforcement Learning agent by investigating the performance of state-of-the-art algorithms, such as Rainbow-DQN with its variants, PPO and A3C, against the baseline rule-based and Dynamic Programming (DP) strategies. The developed RL agent is optimizing challenging control objectives such as fuel economy, vehicle drivability and driver comfort. The Rainbow-DQN is studied separately to optimize the agent compared to all the algorithm variants and after wards, the best performing variant is compared to tuned PPO and A3C agents. Proper evaluation criteria is defined and the concerned agents are tested with nine different scenarios to examine the generalization capabilities and performance robustness. The results revealed that the A3C agent surplussed both the PPO and the Rainbow-DQN achieving a maximum performance of 98.43% of the DP with a robustness of 97.32% ± 0.78 for the other cycles and an average of 177.7 sec for each engine start compared to 96.3 sec for the rule-based approach. Furthermore, as a future work, the paper investigated and proposed a cloud-based training concept for automated scaled-up training, evaluation and deployment of RL policies for the (P)HEVs of the future.

A set of additives was selected to improve the durability of the physical-chemical and biological characteristics of mineral diesel and its blend with biodiesel. Two biodiesels were used: soybean (SME) and rapeseed (RME). Both physical-chemical properties and fuel dispersion of fuel blends and their mixtures with additives were measured that could have effects on the combustion process in diesel engines. The dispersion of the fuel is affected by the injection nozzle integrity, influencing the capacity of the fuel to vaporize, while the modification of the fuel molecular structure can cause changes in combustion reaction. A 7 hole Common Rail (CR) 2nd generation injector, 136 ?m in diameter, was used at 80 MPa and 1.0 ms injection pressure and duration, respectively. The injection rate was determined using the Bosch's Method, while the fuel dispersion was measured by analyzing the images of spray evolving in an optical accessible quiescent vessel. The results showed that the additives have a great influence on the stability of the blends. Adding the biocide "P", antioxidant "AN" and pour point depressant "D", an increase of the oxidation stability of 390 % of the B20SME samples compared to B20 without additives. For B20SME containing "P", "AS" and "D", the oxidation stability increased of 810%. The injection rate data showed that a slight reduction in the rate of injections (9.91% for Diesel B5RME, 8.13% relative to B20RME and 12.84% relative to B20SME) was registered when additives were used. Finally, the inclusion of the additives did not produce particular variations on the fuel spray tip penetration.

A burning process in a combustion chamber of an internal combustion engine is very important to know the maximum temperature of the gases, the speed of combustion, the ignition delay time of fuel and air mixture exact moment at which ignition will occur. The automobilist industry has invested considerable amounts of resources in numerical modeling and simulations in order to obtain relevant information about the processes in the combustion chamber and then extract the maximum engine performance control the emission of pollutants and formulate new fuels. This study aimed to general construction and instrumentation of a shock tube for measuring shock wave. As specific objective was determined reaction rate and ignition delay time of diesel and ethanol doped with different levels of additive enhancer cetane number. The results are compared with the delays measured for the ignition diesel and biodiesel.

Acetone-Butanol-Ethanol (ABE), an intermediate product in the ABE fermentation process for producing bio-butanol, is considered a promising alternative fuel because it not only preserves the advantages of oxygenated fuel which typically emit less pollutants compared to conventional diesel, but also lowers the cost of fuel recovery for each individual component during the fermentation. With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. In this respect, it is desirable to estimate the performance of different ABE blends to determine the best blend and optimize the production process accordingly.

In the present study, ethanol was added in lower proportions to non-edible vegetable oil “Schleichera oleosa” or “Kusum”, to evaluate various performance and emission characteristics of a single cylinder; diesel engine. For engine's trial, four samples were prepared with 5%, 10%, 15% and 20% ethanol in kusum oil (v/v) and the blends were named as E5K95, E10K90, E15K85 and E20K80 respectively. Neat Kusum oil was named as K100. The results indicated that brake thermal efficiency (BTE) was found to increase with increase in volume fraction of ethanol in the kusum oil. E5K95, E10K90, E15K85 and E20K80 test fuels exhibited maximum BTE of 25.4%, 26.4%, 27.4% and 27.7% respectively as compared to 23.6% exhibited by the neat Kusum oil. Similarly, full load brake specific energy consumption (BSEC) decreased from 16.3MJ/kWh in case of neat Kusum oil to 15.1MJ/kWh for E20K80 with an almost linear reduction pattern with increased ethanol composition in the test fuel. Full load carbon monoxide emissions were found to be 0.18% volume for neat Kusum oil which was reduced to 0.1% for E20K80. Similarly, smoke emissions and unburnt hydrocarbons were also found to be reduced by a margin of 7-12% compared to the K100 operation. However, E5K95, E10K90, E15K85 and E20K80 test fuels exhibited 779, 890, 860 and 877 ppm each of NO x emissions as compared to 795 ppm exhibited by the neat Kusum oil.

A commercially available fuel, E85, a blend of ~85% ethanol and ~15% gasoline, can be a viable substitute for fossil fuels in internal combustion engines in order to achieve a reduction of the greenhouse gas (GHG) emissions. Ethanol is traditionally made of biomass, which makes it a part of the food-feed-fuel competition. New processes that reuse waste products from other industries have recently been developed, making ethanol a renewable and sustainable second-generation fuel. So far, work on E85 has focused on spark ignition (SI) concepts due to high octane rating of this fuel. There is very little research on its application in CI engines. Alcohols are known for low soot particle emissions, which gives them an advantage in the NOx–soot trade-off of the compression ignition (CI) concept. Therefore, the main objective of this research is to experimentally characterise the impact of E85 on performance and emissions of a heavy-duty (HD) direct ignition compression ignition (DICI) engine at mid-to-low load, and to identify possible challenges. To do so, a surface response method of the Box-Behnken type is implemented on a measurement campaign on a HD single cylinder CI engine. The effects of common rail pressure (Prail), λ and combustion timing (CA50) as control parameters on experimentally measured values of soot, regulated gaseous emissions (NOx, CO and THC) and gross indicated efficiency (GIE) of the engine are studied. Linear regression (LR) analysis indicates that the outputs of the NOx and soot models are affected by all three control parameters, whereas GIE, THC and CO models in this case exclude λ effects. E85 fuel shows potential to be a good candidate for highly efficient low temperature combustion (LTC) in DICI engines, with reduced NOx and soot levels compared to fossil diesel combustion.