Aerodynamic Drag and Noise Minimization of Rear End Parameters in a Simplified Car Model Utilizing Robust Parameter Design Method

Beigmoradi, Sajjad

doi:10.4271/2015-01-1360

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…There are different desirability functions according to the maximizing, minimizing, or assigning a target for an object. To minimize an object, drag or lift coefficient desirability function can be defined as [25]: (15) C L = −0.16990 − (0.008535 × ) − (0.004046 × ) In relation (16), U i and T i are upper and target values, correspondingly. For the desirability function with a minimum object, T i has a small enough value [25].…”

Section: Multi-objective Optimization Resultsmentioning

confidence: 99%

“…For the desirability function with a minimum object, T i has a small enough value [25]. In relation (16), fitted response value ( Ŷ i ) is used instead of Y i which is usual in the practice [25].…”

Section: Multi-objective Optimization Resultsmentioning

confidence: 99%

“…They have used fraction factorial method to find out optimum configuration of the rear end to reduce drag forces. Beigmoradi [16] has obtained optimum levels of the rear-end parameters for a simplified car model considering aerodynamic drag and noise. Beigmoradi has applied computational fluid dynamics to survey air flow structure around the models beside Taguchi algorithm to find optimum level of variables.…”

Section: Introductionmentioning

confidence: 99%

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Optimal configuration of a hatchback rear end considering drag and stability objectives

Beigmoradi

Vahdati

2019

J Braz. Soc. Mech. Sci. Eng.

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Aerodynamic load is recognized as one of the most important factors, which has significant effect on fuel consumption, vehicle performance, and stability. Designing external car body to meet aerodynamic drag and stability requirements is one of the most challenging works in vehicle development process. In this paper, optimum design of rear-end parameters for a hatchback car is studied considering both drag and lift goals. Five geometrical parameters of rear end are chosen as design variables at two levels. Interaction of these factors on drag and lift coefficients is investigated using design of experiments, and optimum level for each parameter is achieved. To reduce the number of case studies, fractional factorial design algorithm is nominated. Then, drag and lift regression equations based on important terms are derived and the relationship of these parameters with objectives are discussed. Finally, aerodynamic performance around the optimal model is reported.

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Section: Multi-objective Optimization Resultsmentioning

confidence: 99%

Section: Multi-objective Optimization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal configuration of a hatchback rear end considering drag and stability objectives

Beigmoradi

Vahdati

2019

J Braz. Soc. Mech. Sci. Eng.

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“…To determine SWL, the model set forth in Proudman (1952) is used here. This model has been used successfully by multiple investigators in the recent published literature (Beigmoradi, 2015;Lee et al, 2015;El-Sayed et al, 2014;Mousavi et al, 2014;Pawar et al, 2014). The magnitude of the SWL is related to the local sound intensity I A in units of W/m 2 and to a reference sound intensity I o whose value for air is 10 À12 W/m 2 .…”

Section: Sound Power Analysismentioning

confidence: 99%

Fluid mechanics and sound generation for lung-clearance therapy

Gorman

Sparrow

Krautbauer

2017

HFF

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Purpose The study described here aims to set forth an analysis approach for a specific biomedical therapeutic device principally involving fluid mechanics and resulting sound generation. The function of the therapeutic device is to clear mucus from the airways of the lungs. Clearance of the airways is a primary means of relief for cystic fibrosis and is also effective in less profound dysfunctions such as asthma. The complete system consists of a device to periodically pulse air pressure and a vest that girdles the abdomen of the patient and receives and discharges the pulsating airflow. The source of pulsed air can be tuned both with respect to the amplitude and frequency of the pressure pulsations. Design/methodology/approach The key design tools used here are computational fluid dynamics and the theory of turbulence-based sound generation. The fluid flow inside of the device is multidimensional, unsteady and turbulent. Findings Results provided by the fluid mechanic study include the rates of fluid flow between the device and the inflatable vest, the rates of air supplied to and extracted from the device, the fluid velocity magnitudes and directions that result from the geometry of the device and the magnitude of the turbulence generated by the fluid motion and the rotating component of the device. Both the velocity magnitudes and the strength of the turbulence contribute to the quantitative evaluation of the sound generation. Originality/value A comprehensive literature search on this type of therapeutic device to clear mucus from the airways of the lungs revealed no previous analysis of the fluid flow and sound generation inside of the device producing the pulsed airflow. The results presented in this paper pinpoint the locations and causes of sound generation that can cause audible discomfort for patients.

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“…Their findings concluded that tapered extensions have the potential to enhance drag reduction. Beigmoradi (2015) determined the optimum levels of rear end parameters for a simplified car model, considering aerodynamic drag and noise. Employing computational fluid dynamics, he surveyed the airflow configuration around the bodies and utilized the Taguchi method to identify the optimal levels of parameters.…”

Section: Introductionmentioning

confidence: 99%

Harmonizing Aerodynamic Efficiency, Stability, and Acoustic Excellence: Multi-Objective Optimization for Electric Vehicle Rear-End Design

Beigmoradi

2024

Preprint

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In recent years, the automotive field has exposed a significant surge in electrification, presenting a pivotal solution to environmental concerns. However, addressing the challenge of driving mileage remains a critical aspect in the realm of electric vehicles (EVs). Among the various factors influencing battery energy consumption during operation, aerodynamic force emerges as a primary contributor. Mitigating this force holds the key to substantial improvements in driving mileage, making it a focal point of exploration in this research. The impact of aerodynamic force extends beyond mere energy consumption; it intricately influences battery size, vehicle mileage, performance, stability, and passenger comfort. Navigating this multifaceted terrain poses a formidable challenge for automotive engineers engaged in the development of electric vehicles. This study undertakes the optimization of rear-end design factors for a hatchback electric vehicle, specifically addressing drag, lift, and aerodynamic noise objectives. Five critical geometric factors of the rear end – Rear Spoiler Length, Rear Spoiler Angle, Rear Diffuser Angle, Boat Tail Angle, and 5th Door Height – are identified as key design parameters. The interplay of these factors and their impact on objectives is systematically investigated through a Design of Experiment (DoE) approach. To enhance the efficiency of the investigation, a fractional factorial design method is utilized, effectively reducing the number of individual case studies. The formulation of regression equations, capturing the essence of significant terms for each objective, lays the groundwork for a subsequent multi-objective optimization process. This optimization, driven by the maximization of a composite desirability function, identifies optimal levels for each design factor. The research culminates in the selection of a model with optimum rear-end factors based on a comprehensive evaluation of drag, lift, and aerodynamic noise objectives. The aerodynamic performance surrounding this optimal model is intricately described, offering valuable insights into the holistic impact of the chosen design parameters on the electric vehicle's aerodynamics.

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Aerodynamic Drag and Noise Minimization of Rear End Parameters in a Simplified Car Model Utilizing Robust Parameter Design Method

Cited by 6 publications

References 16 publications

Optimal configuration of a hatchback rear end considering drag and stability objectives

Optimal configuration of a hatchback rear end considering drag and stability objectives

Fluid mechanics and sound generation for lung-clearance therapy

Harmonizing Aerodynamic Efficiency, Stability, and Acoustic Excellence: Multi-Objective Optimization for Electric Vehicle Rear-End Design

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