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Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviours and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining the outputs of these during the model integration phase, an overall health assessment of the aircraft will be generated.

Severe problem of aerodynamic heating and drag force are inherent with any hypersonic space vehicle like space shuttle, missiles etc. For proper design of vehicle, the drag force measurement become very crucial. Ground based test facilities are employed for these estimates along with any suitable force balance as well as sensors. There are many sensors (Accelerometer, Strain gauge and Piezofilm) reported in the literature that is used for evaluating the actual aerodynamic forces over test model in high speed flow. As per previous study, the piezofilm also become an alternative sensor over the strain gauges due to its simple instrumentation. For current investigation, the piezofilm and strain gauge sensors have mounted on same stress force balance to evaluate the response time as well as accuracy of predicted force at the same instant. However, these force balance need to be calibrated for inverse prediction of the force from recorded responses.

With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state).

This article analyzes the aerodynamic performance of Class 8 tractor-trailer geometries made available by the Environmental Protection Agency (EPA) using CFD simulation. Large Eddy Simulations (LES) were carried out with the CFD package, Simerics-MP+. A Sleeper tractor and a 53-foot box trailer configuration was considered. The configuration featured a detailed underbody, an open-grille under-hood engine compartment, mirrors, and the radiator and condenser. Multiple tractor-trailer variants were studied by adding aerodynamic surfaces to the baseline geometries. These include tank fairings and side extenders for the cabins, two types of trailer skirts, and a trailer tail. The effect of these devices towards reducing the overall vehicle drag was investigated. Mesh generation was carried out directly on the given geometry, without any surface modifications, using Simerics’ Binary-Tree unstructured mesher.

In-cylinder fluid dynamics enhance performance and emission characteristics in internal combustion (IC) engines. Techniques such as helical ports, valve shrouding, masking, and modifications to piston profiles or vanes in ports are employed to achieve the desired in-cylinder flows in these engines. However, due to space constraints, modifications to the cylinder head are typically minimal. The literature suggests that introducing baffles into the combustion chamber of an IC engine can enhance in-cylinder flows, air-fuel mixing, and, subsequently, stratification. Studies have indicated that the height of the baffles plays a significant role in determining the level of improvement in in-cylinder flow and air-fuel mixing. Therefore, this study employs Computational fluid dynamics (CFD) analysis to investigate the impact of baffle height on in-cylinder flow and air-fuel mixing in a four-stroke, four-valve, spray-guided gasoline direct injection (GDI) engine.

The current research focuses on enhancing the performance of Si solar cells by using Er2O3 (Erbium Oxide) in cubic crystalline nature serves as an anti-reflection coating material. An anti-reflective coating aims to improve the Efficient Power Conversion (EPC) of polycrystalline silicon wafers solar cells (PSSC) utilised in solar roof panels of the automotive sector. It also exhibits superior light transmittance and least light reflectance, which eventually leads to the increase EPC. Erbium oxide helps to convert low energy photons into high energy photons. The incident photons, which lies on the solar cell, gradually losses its energy to travel in a denser medium and dissipate in the form of heat energy. In order to overcome the rate of reflection, current research aims in synthesis of erbium oxide nanosheets using electrospinning deposition technique for varying deposition timings such as 1, 1.5 and 2 hours.

This research paper focuses on the modelling and analysis of a flywheel energy storage system (FESS) specifically designed for electric vehicles (EVs) with a particular emphasis on the flywheel rotor system associated with active magnetic bearings. The methodology used simulation approaches to investigate the dynamics of the flywheel system. The objective of this study is to explore the effects of implementing the flywheel energy storage system on the performance of the EV. The paper presents a comprehensive model of the flywheel energy storage system, considering the mechanical and electrical aspects. The mechanical model accounts for the dynamic behaviors of the flywheel, including parameters such as rotational speed, inertia, and friction. The electrical model describes the interaction between the flywheel and the power electronics, such as the converter and motor/generator. To evaluate the benefits of the flywheel energy storage system, simulations are conducted.

The primary function of an automotive horn is to alert pedestrians and other nearby vehicles to their safe passage on the road. Most of the human population is subjected to a certain amount of horn sound dosage daily. The study of automotive horn sound quality is equally important as their sound generation mechanism in passenger cars. The sound quality of automotive horns can be studied through subjective and objective test methods. In the present study, a subjective jury test and objective analysis using psychoacoustic parameters are conducted to classify car horn sound samples according to pleasantness. Twenty-two car horns, consisting of a disc and shell, are chosen for binaural sound recording. The recorded sound samples are used for subjective and objective analysis. Thirty members participated in the jury test, and a semantic differential method was used to collect the user response. The Tukey range test is used to classify the subjective test data.

The thermal performance of an engine coolant system is efficient when the engine head temperature is maintained within its optimum working range. For this, it is desired that air should not be entrapped in the coolant system which can lead to localized hot spots at critical locations. However, it is difficult to eliminate the trapped air pockets completely. So, the target is to minimize the entrapped air as much as possible during the coolant filling and deaeration processes, especially in major components such as the radiator, engine head, pump etc. The filling processes and duration are typically optimized in an engine test stand along with design changes for augmenting the coolant filling efficiency. However, it is expensive and time consuming to identify air entrapped locations in tests, decide on the filling strategy and make the design changes in the piping accordingly.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

Commercial automotive diesel engine service and repair, post a diagnostic trouble code trigger, relies on standard troubleshooting steps laid down to identify or narrow down to a faulty engine component. This manual process is cumbersome, time-taking, costly, often leading to incorrect part replacement and most importantly usually associated with significant downtime of the vehicle. Current study aims to address these issues using a novel in-house simulation-based approach developed using a Digital Twin of the engine which is capable of conducting in-mission troubleshooting with real world vehicle/engine data. This cost-effective and computationally efficient solution quickly provides the cause of the trouble code without having to wait for the vehicle to reach the service bay. The simulation is performed with a one-dimensional fluid dynamics, detailed thermodynamics and heat transfer-based diesel engine model utilizing the GT-POWER engine performance tool.

Valve seat inserts (VSI) are installed in cylinder heads to provide a seating surface for poppet valves. Insert material is more heat and wear resistant than the base cylinder head material and hence it makes them better suited for valve seating and improved engine durability. Also use of inserts permits easier repair or rebuild of cylinder heads as only the wear surfaces need to be replaced. Desirable performance characteristics are appropriate sealing, heat-transfer and minimizing valve’s seating face to VSI wear and undesired outputs include valve seat dropping and cracking. With the downsizing trend of diesel engines, it leads to increasing power density and therefore higher cylinder pressure and temperatures. Hence the engine components are getting exposed to more severe loadings and hence to damage modes, which were heretofore not experienced. Among such possible damage modes are insert’s yielding and corresponding press-fit loss leading to either it’s cracking or drop-out.

This paper describes steady state, computationally rigorous, three-dimensional conjugate heat transfer 3D CFD analysis of an oil cooler. Thermal performance of an oil cooler is very significant from engine oil consumption, bearings performance etc. In an engine water jacket, coolant flows around and through the oil cooler making the flow three dimensional. Therefore, demanding the need of a 3D CFD analysis for capturing all the flow and heat transfer aspects and thereby accurate prediction of thermal performance. An oil cooler contains intricate turbulators in flow paths and have dimensions varying from as small as 0.25 mm to as large as 350 mm, therefore making the meshing and solution a formidable task. In current work an oil cooler with all the intricate details is modelled in a commercial CFD code. Objective is to develop a solution approach which can predict thermal performance of an oil cooler in an accurate way.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

A multi-dimensional model of the spark ignition process for SI engines was developed as a user defined function (UDF) integrated into the commercial engine simulation software CONVERGE™ CFD. For the present research, the model simulated spark plasma development in an inert flow environment without combustion. The UT model results were then compared with experiments. The UT CONVERGE CFD-based model includes an electrical circuit sub-model that couples the primary and secondary sides of an inductive ignition system to predict arc voltage and current, from which the transient delivered electrical energy to the gap can be determined. Experimentally measured values of the arc resistance and spark plug calorimeter measurements of the efficiency of electrical to thermal energy conversion in the gap were used to determine the thermal energy delivered to the gas in the spark gap for different pressures and gap distances.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

Evaluation of the platooning legislative space suggests a limited near-term opportunity for autonomous vehicles as currently only nine states have platooning and autonomous favorable legislations. An extensive closed-loop vehicle model simulation was conducted to quantify two-truck platooning fuel economy entitlement benefits across all United States (US) interstate routes (I-xx) spanning over 40,000 miles as compared to a single truck. A simultaneous study was carried out to identify the density of Class 8 heavy-duty trucks on these interstates, using the Freight Analysis Framework (FAF) 4 database. These two studies were combined to ascertain interstates that foresee the least fuel consumption due to platooning and thus identifying states with the most platooning benefits. Identification of states with most platooning benefits provides realistic data to push for autonomous driving and platooning legislations.

The ignition quality of a fuel is described by its cetane number. Experimental methods used to determine cetane number employ Co-operative fuel research (CFR) engine and Ignition quality tester (IQT) which are expensive, have less repeatability and require skilled operation, and hence least preferred. There are many prediction models reported, which involve number of double bonds and number of carbon atoms whose determination is not direct. Using models that relate biodiesel composition to its cetane number is limited by the range of esters involved. Hence, a model to predict cetane number of biodiesels that addresses the limitations of the existing models, without ignoring the influence of factors such as degree of unsaturation and number of carbon atoms, is needed. Fourier transform infrared spectroscopy (FTIR) could be one such method.

Flow variations from one cycle to the next significantly influence the mixture formation and combustion processes in engines. Therefore, it is important to understand the fluid motion and its cycle-to-cycle variations (CCVs) inside the engine cylinder. Researchers have generally investigated the cycle-to-cycle flow variations in moderate- to large-sized engines. In the present work, we have performed the flow measurement and analysis in a small spark-ignition engine. Experiments are conducted in an optically accessible, single-cylinder, port-fuel-injection engine with displacement volume of 110 cm3 at different throttle openings (i.e. 50% and WOT) using particle image velocimetry. Images are captured at different crank angle positions during both intake and compression strokes over a tumble measurement plane, bisecting the intake and exhaust valves and passing through the cylinder axis.