Structural Optimization of Truck Front-Frame Under Multiple Load Cases

De, Shuvodeep; Singh, Kamaldeep; Alanbay, Berkan; Kapania, Rakesh K.; Aguero, Raymond

doi:10.1115/imece2018-86293

Cited by 6 publications

(4 citation statements)

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“…• Multidisciplinary Design Optimisation: Multidisciplinary design optimization (MDO) is a research field that explores the use of numerical optimization methods to design engineering systems based on multiple disciplines, such as structural analysis, aerodynamics, materials science, and control systems. MDO is widely used to design automobiles, aircraft, ships, space vehicles, electro-chemical systems, and more, with the goal of improving performance while minimizing weight and cost [5][6][7][8][9][10][11][12][13]. With the advancement of computational power, MDO frameworks can also be used for autonomous vehicles to improve their structural design, aerodynamics, powertrain optimization, sensor integration and placement, path planning, control system optimization, and energy management.…”

Section: Of 29mentioning

confidence: 99%

Practical Autonomous Driving: A Survey of Challenges and Opportunities

De,

Saha,

Sallam

et al. 2023

Preprint

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Autonomous Driving (AD) has become a prominent research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) in recent years. This opens the door wider for self-driving cars to surpass conventional vehicles in the current market share. Despite its apparent simplicity, AD is composed of complex and heterogeneous systems, which are in need of a high level of coordination and alignment to ensure both full automation and safety. Therefore, numerous research studies have been conducted over the last few years to facilitate such coordination and accelerate the capacity of these types of vehicles to be self-managed in complex situations. The paper summarises these approaches that led to building what is known nowadays as the autonomous driving pipeline. Moreover, although skepticism exists regarding the practicality of AD as a viable alternative to traditional vehicles, extensive research suggests the multiple benefits of relying on them for mobility. While challenges remain in implementing AD in the real world, including regulation and technical issues, substantial progress in recent years indicates a growing acceptance of AD in the near future. This paper further explores the advantages and opportunities of the conducted such systems in facilitating the practicality of Autonomous Driving.

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Section: Of 29mentioning

confidence: 99%

Practical Autonomous Driving: A Survey of Challenges and Opportunities

De,

Saha,

Sallam

et al. 2023

Preprint

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show abstract

“…According to advocates, most traffic accidents (more than 90%) are caused by human errors. Therefore, the utilisation of novel materials like stiffened composite materials [4]- [20] and advanced self-driving cars has the potential to reduce accidents by 90% [21]. • Reduced Congestion and Improved Reliability: As humans have restricted perception and reaction times, they can't efficiently deploy highway capacity.…”

Section: A Advantages Over Conventional Vehiclesmentioning

confidence: 99%

Practical Self-Driving Cars: Survey of the State-of-the-Art

Saha¹,

De²

2022

Preprint

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Self-Driving Vehicles or Autonomous Driving (AD) have emerged as the prime field of research in Artificial Intelligence and Machine Learning of late. The indicated market share of existing vehicles might be supplanted by these self-driving vehicles within the next few decades. While AD may appear to be relatively easy, in fact, it is quite the contrary owing to involvement and coordination amongst various kinds of systems. Numerous research studies are being conducted at various stages of these AD systems. While some find the various stages of the AD Pipeline beneficial, others tend to rely on Computer Vision mostly. This paper attempts to summarise the recent developments in Autonomous Vehicle architecture. Although some people might seem to be sceptical about the pragmatic use of AD as an alternative to existing vehicles, the plethora of research and experiments being conducted suggests the opposite. Indeed, there are many challenges to implementing AD in the real world, but significant progress made in the last couple of years indicates general acceptance of AD in upcoming years.

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“…Verification and Validation is a very important and essential field where the computational models [1][2][3][4][5][6][7][8][9][10][11][12][13][14] are tested for their credibility. Computer simulations are used to predict real-life outcomes, control vehicles precisely, design light-weight structures etc.…”

Section: Assumptions In Computational Modelsmentioning

confidence: 99%

Verification and Validation in Computational Mechanics

De¹

2022

Preprint

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Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

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Structural Optimization of Truck Front-Frame Under Multiple Load Cases

Cited by 6 publications

References 0 publications

Practical Autonomous Driving: A Survey of Challenges and Opportunities

Practical Autonomous Driving: A Survey of Challenges and Opportunities

Practical Self-Driving Cars: Survey of the State-of-the-Art

Verification and Validation in Computational Mechanics

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