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The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, these engines feature an increasing number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

Design and Manufacturing of an Inclinometer sensing element for launch vehicle applications Tony M Shaju, Nirmal Krishna, G Nagamalleswara Rao, Pradeep K Scientist/Engineer, ISRO Inertial Systems Unit, Vattiyoorkavu, Trivandrum, India - 695013 Indian Space Research Organisation (ISRO) uses indigenously developed launch vehicles like PSLV, GSLV, LVM3 and SSLV for placing remote sensing and communication satellites along with spacecrafts for other important scientific applications into earth bound orbits. Navigation systems present in the launch vehicle play a pivotal role in achieving the intended orbits for these spacecrafts. During the assembly of these navigation packages on the launch vehicle, it is required to measure the initial tilt of the navigation sensors for any misalignment corrections, which is given as input to the navigation software. A high precision inclinometer is required to measure these tilts with a resolution of 1 arc-second.

The descent phase of GAGANYAAN (Indian Manned Space Mission) culminates with a crew module impacting at a predetermined site in Indian waters. During water impact, huge amount of loads are experienced by the astronauts. This demands an impact attenuation system which can attenuate the impact loads and reduce the acceleration experienced by astronauts to safe levels. Current state of the art impact attenuation systems use honeycomb core, which is passive, expendable, can only be used once (at touchdown impact) during the entire mission and does not account off-nominal impact loads. Active and reusable attenuation systems for crew module is still an unexplored territory. Three configurations of impact attenuators were selected for this study for the current GAGANYAAN crew module configuration, namely, hydraulic damper, hydro-pneumatic damper and airbag systems.

Effective contrail management while ensuring operational and economic efficiencies for flight services is essential for providing services with minimal adverse environmental impact. The paper explores various aspects of contrail management applicable to different platforms such as Unmanned vehicles, Commercial airliners and Business & regional jets. The aspects unique to each platform such as flight levels of operation, fuel types, flight endurance and radius of operation have been analyzed. Expanse of 5G network is resulting in increased flight activity at flight levels not envisaged hitherto. The paper also dwells on the ramifications of the increased proliferation of different platforms at newer flight levels from the perspective of contrail management.

Metasurfaces, comprised of sub-wavelength structures, possess remarkable electromagnetic wave manipulation capabilities. Their application as radar absorbers has gained widespread recognition, particularly in modern stealth technology, where their role is to minimize the radar cross-section (RCS) of military assets. Conventional radar absorber design are tedious by their time-consuming, computationally intensive, iterative nature, and demand a high level of expertise. In contrast, the emergence of deep learning-based metasurface design for RCS reduction represents a rapidly evolving field. This approach offers automated and computationally efficient means to generate radar absorber designs. However, the practical implementation of radar-absorbing structures on complex aircraft bodies presents significant challenges.

Dimensional optimization has always been a time consuming process, especially for aerodynamic bodies, requiring much tuning of dimensions and testing for each sample. Aerodynamic auxiliaries, especially wings, are design dependent on the primary model attached, as they influence the amount of lift or reduction in drag which is beneficial to the model. In this study CFD analysis is performed to obtain pressure counter of wings. For a wing, the angle of attack is essential in creating proper splits to incoming winds, even under high velocities with larger distances from the separation point. In the case of a group of wings, each wing is then mentioned as a wing element, and each wing is strategically positioned behind the previous wing in terms of its vertical height and its self-angle of attack to create maximum lift. At the same time, its drag remains variable to its shape ultimately maximizing the C L /C D ratio.

In order to improve the obstacle avoidance ability of autonomous vehicles in complex traffic environments, speed planning, path planning, and tracking control are integrated into one optimization problem. An integrated vehicle trajectory planning and tracking control method combining a pseudo-time-to-collision (PTC) risk assessment model and model predictive control (MPC) is proposed. First, a risk assessment model with PTC probability is proposed by considering the differentiation of the risk on the relative motion states of the self and front vehicles, and the obstacle vehicles in the lateral and longitudinal directions. Then, a three-degrees-of-freedom vehicle dynamics model is established, and the MPC cost function and constraints are constructed from the perspective of the road environment as well as the stability and comfort of the ego-vehicle, combined with the PTC risk assessment model to optimize the control.

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.

This paper delves into the intricate realm of Formula 1 race car aerodynamics, focusing on the pivotal role played by floor flow structures in contemporary racing. The aerodynamic design of the floor of a Formula 1 car is a fundamental component that connects the flow structures from the front wing to the rear end of the car through the diffuser, thus significantly influencing the generation of lift and drag. In this work, CFD was used to predict the structure of the vortices and flow pattern underneath a Formula 1 car using a CAD model that mimicked the modern Red Bull Racing Team’s car in recent years. Through comprehensive analysis and simulation, a detailed understanding of the complex flow patterns and aerodynamic phenomena occurring beneath the floor of the car and its vicinity is presented.

Battery electric vehicles are quickly gaining momentum to improve vehicle fuel efficiency and emission reduction. However, they must be designed to provide adequate range on a single charge combined with good acceleration performance, top speed, gradeability, and fast charging times. The paper presents a model for sizing the power train of an electric vehicle, including the power electronic converter, electric motor, and battery pack. A major assumption is that an optimal wheel slip rate can be achieved by modern vehicles using slip control systems. MATLAB/Simulink was used to model the vehicle powertrain. Simulations were conducted based on different speed and acceleration profiles. The purpose of the study focused on the motor and power electronics sizing requirements to achieve optimal range and performance.

This paper presents a numerical methodology to generate lookup tables that provide d- and q-axis stator current references for the control of electric motors. The main novelty with respect to other literature references is the introduction of the iron power losses in the equivalent-circuit electric motor model implemented in the optimization routine. The lookup tables generation algorithm discretizes the motor operating domain and, given proper constraints on maximum stator current and magnetic flux, solves a numerical optimization problem for each possible operating point to determine the combination of d- and q- axis stator currents that minimizes the imposed objective function while generating the desired torque. To demonstrate the versatility of the proposed approach, two different variants of this numerical interpretation of the motor control problem are proposed: Maximum Torque Per Ampere and Minimum Electromagnetic Power Loss.

Global automobile manufacturers are increasingly adopting vehicle architecture development systems in the early stages of product development. This strategic move is aimed at rationalizing their product portfolios based on similar specifications and functions, with the overarching goal of simplifying design complexities and enabling the creation of scalable vehicles. Nevertheless, ensuring consistent performance in this dynamic context poses formidable challenges due to the wide range of design possibilities and potential variations at each development stage. This paper introduces an efficient reliability analysis process designed to identify and mitigate the distribution of Ride and Handling (R&H) performance. We employ a range of reliability analysis techniques, including Latin Hypercube Sampling and the enhanced Dimension Reduction (eDR) method, utilizing various types of models such as surrogate models and multi-body dynamics models.

The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.

A case study of an application of Shape optimization techniques in the design of a mass simulator has been presented. A simple mass Simulator is to be designed as a replacement for a Telescope Baffle Mass for testing purposes. The simulator is made of simple plate structures like flat plates and cylindrical plates joined together. The overall mass, location of center of gravity and first few modes of the simulator need to be close to the Telescope Baffle, it is replacing. This ensures that the Simulator is a good replacement for the Telescope Baffle both in statics and dynamics performance. Shape Optimization techniques using approximate direct linearization method of MSC/Nastran software have been used to fine-tune the baseline Simulator design to achieve target properties of mass, cg, frequencies, etc.

A tandem aircraft configuration has two wings placed one behind the other longitudinally, with no dedicated horizontal stabilizer. Since there are two wings, high lift is obtained but also at the cost of additional structural weight and drag. In this article, a methodology is proposed to design a tandem aircraft configuration and depict the design process of the radio-controlled model. Flight test is conducted with the model to verify the stability and predicted performance. Aerodynamic optimization is conducted by using computational fluid dynamics to understand the effects of downwash from the front wing to the aft wing. In the end, a conventional aircraft is conceptually designed, which uses the same power plant configuration and the predicted performance is obtained. The predicted performance results of the tandem aircraft and the conventional aircraft are compared and the results are obtained.

Efficient transportation for carrying heavy loads is a common challenge across various applications, from supermarkets to industrial purposes. Conventional trolleys often fall short when loaded with heavy cargo, resulting in increased exertion and diminished productivity. Moreover, these challenges can adversely affect posture and lumbar spine health, especially for elder people and persons with cervical problems. There is a need for more user-friendly, ergonomic, and space-efficient solutions. This project addresses these challenges through an innovative design that encompasses various aspects of trolley functionality, including the study of comfort, wheel selection, and material considerations, drawing from ergonomic research. Multiple methods are employed to optimize the trolley’s dimensions to improve its overall performance. The trolley’s design features a collapsible basket for the transport of smaller-sized items and a base frame for larger goods and luggage.

The focus on driver and occupant safety as well as comfort is increasing rapidly while designing commercial vehicles in India. Improvements in the road network have enhanced road transport for commercial vehicles. Apart from the cost of operation and fuel economy, the commercial vehicles must deliver goods within stipulated time. These factors resulted in higher speed of operation for commercial vehicles. The design should not compromise the safety of the vehicle at these higher speeds of operation. The vehicle should obey the driver’s intended direction at all speeds and the response of the vehicle to driver input must be predictable without much larger surprises which can lead to accidents. The commercial vehicles are designed with rigid axle and RCB type steering system. This suspension and steering design combination introduce steering errors when vehicle travel over bump, braked and while cornering.

Vehicle design necessarily involves tuning various parameters to optimize automotive performance metrics like ride and handling. The tuning process is iterative and involves a trial-and-error approach to understand the influence of the input parameters on various output metrics. We develop tuning models and run many simulations to optimize various parameters, followed by validation. This process is computationally expensive and contingent on the output metrics. Alternatively, data-driven modelling could overcome shortcomings but requires extensive data sets to train, which may not be feasible in the initial design phase. In this work, we demonstrate how we can use Dynamic Mode Decomposition, commonly used in Fluid Mechanics, to create Reduced Order Single Parameter Tuning Models, which are computationally lightweight and can provide the output metrics as a function of one tuning parameter. It reduces the tuning time and also helps to understand the system better.

Effective smart cockpit interaction design can address the specific needs of children, offering ample entertainment and educational resources to enhance their on-board experience. Currently, substantial attention is focused on smart cockpit design to enrich the overall travel engagement for children. Recognizing the contrasts between children and adults in areas such as physical health, cognitive development, and emotional psychology, it becomes imperative to meticulously customize the design and optimization processes to cater explicitly to their individual requirements. However, a noticeable gap persists in both research methodologies and product offerings within this domain.

Transporting baggage is critical in airport ground support services to ensure smooth flight operations. However, the scheduling of baggage transport vehicles faces challenges related to low efficiency and high costs. A multi-objective optimization vehicle scheduling model is proposed to address these issues, considering time and space costs, vehicle utilization, and passenger waiting time. An improved genetic algorithm (IGA) based on the large-scale neighborhood search algorithm is proposed to solve this model. The simulation experiment is conducted using actual flight data from an international airport. The IGA algorithm is compared with the standard genetic algorithm (SGA) based on experimental results, revealing that the former achieves convergence in a significantly shorter time. Moreover, the scheduling paths of baggage cars that violate flight service time window requirements are significantly lower in the final scheduling scheme under the IGA algorithm than in SGA.