# Optimization of International Roughness Index Model Parameters for Sustainable Runway

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. ADAMS/Aircraft Virtual Prototype Modeling Method

- Build the components in the template file.
- Define the quality property of the components.
- Build the required motion joints, hardpoints, etc.
- Build the mechanical elements and create and assign the property files.
- Build the communicator.
- Generate the subsystems.
- Construct the assembly.

#### 2.2. Boeing 737 Aircraft Virtual Prototype Model

#### 2.2.1. Mass, Moment of Inertia, and Location of Center of Gravity of B737-800

#### 2.2.2. Airframe Aerodynamics

#### 2.2.3. Landing Gear Buffer System

#### 2.2.4. Wheels

#### 2.3. Verification of Virtual Prototype Model

## 3. Frequency Response Function of IRI Model

_{s}at the same time, two simplified vibration balance equations are obtained, as shown in Equations (6) and (7), respectively.

## 4. Results and Discussion

#### 4.1. Frequency Response Distribution of Aircraft

#### 4.2. Frequency Response Curves of the IRI Model and Aircraft

#### 4.3. Parameter Optimization Based on Particle Swarm Optimization

#### 4.4. Case Study

## 5. Conclusions

- The B737-800 virtual prototype model developed and validated in this study is reliable and can represent aircraft dynamic responses to rough runways.
- The frequency response curves of the IRI model and aircraft model differ greatly, which indicates that the IRI cannot effectively represent the vibration responses of aircraft to runway roughness.
- PSO accelerates the efficiency of parameter optimization. The optimized IRI model approximates the vibration response characteristics of the aircraft better than the original IRI model (without optimized parameters).
- The case study results show that the correlation coefficient between the optimized IRI model and the aircraft vibration response is as high as 0.56. Compared with the original IRI model, the optimized IRI model shows a qualitative leap in terms of correlation coefficients.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Aircraft taxi simulation in ADAMS/Aircraft [19].

**Figure 6.**Sensitive frequency of B737-800 in terms of power spectral density (PSD): (

**a**) pilot station acceleration (PSA) and (

**b**) center of gravity acceleration (CGA).

**Figure 7.**Sensitivity of International Roughness Index (IRI) model and B737 model’s center of gravity acceleration (CGA) to different frequency bands.

**Figure 9.**Correlation between original IRI values and the root mean square (RMS) of the aircraft’s CGA and PSA data.

**Table 1.**Mass, moment of inertia, and location of center of gravity for B737-800 [20].

Mass | Estimated Moment of Inertia | Center of Gravity | ||
---|---|---|---|---|

MTW/kg | 78,472 | $\mathrm{Iy}/\mathrm{kg}\xb7{\mathrm{m}}^{2}$ | 3,394,953 | 13.60 m to nose landing gear |

MTOW/kg | 78,245 | $\mathrm{Ix}/\mathrm{kg}\xb7{\mathrm{m}}^{2}$ | 1,866,711 | |

MLW/kg | 65,317 | $\mathrm{Iz}/\mathrm{kg}\xb7{\mathrm{m}}^{2}$ | 5,097,558 |

Aircraft | $SREF/{feet}^{2}$ | SPAN/m | MAC/m | Aspect Ratio |
---|---|---|---|---|

B737-800 | 1341 | 35.79 | 3.79 | 9.45 |

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**MDPI and ACS Style**

Tian, Y.; Liu, S.; Liu, L.; Xiang, P.
Optimization of International Roughness Index Model Parameters for Sustainable Runway. *Sustainability* **2021**, *13*, 2184.
https://doi.org/10.3390/su13042184

**AMA Style**

Tian Y, Liu S, Liu L, Xiang P.
Optimization of International Roughness Index Model Parameters for Sustainable Runway. *Sustainability*. 2021; 13(4):2184.
https://doi.org/10.3390/su13042184

**Chicago/Turabian Style**

Tian, Yu, Shifu Liu, Le Liu, and Peng Xiang.
2021. "Optimization of International Roughness Index Model Parameters for Sustainable Runway" *Sustainability* 13, no. 4: 2184.
https://doi.org/10.3390/su13042184