• Journal of Industrial Technology
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• v.18
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• pp.309-316
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• 1998

The airbag system for automobile is one of the most important passenger protect system. And it is very important whether to inflate or not, and when the airbag will be inflated. This paper focuses on how to find airbag triggering time after the automobile is crashed. In this paper we present an algorithm for airbag triggering time decision and compare the triggering time with the time by the other algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.2
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• pp.62-76
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• 1995

The safety analysis becomes very essential in the crash environment with the growth of automobile industry. Recently, an airbag system is required to protect the occupant. The effects of an airbag can be evaluated exactly from the barrier or sled test which is quite expensive. The airbag system in a passenger car is analyzed with the occupant analysis program. The modeling of the passenger car including an airbag is established and the results are verified by comparisons with real crash tests. However, the solution of an airbag design can not be obtained easily with the conventional method such as an optimization due to the nonlinearity and complexity of the problem. An iterative design algorithm using the orthogonal array is proposed to overcome the difficulties. The design trend of an airbag is recommended to minimize the injury of an occupant with the proposed design algorithm and the results are discussed.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.148-156
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• 2003

Many car manufacturers have frequently adopted an aggressive inflator and a lower threshold speed for airbag deployment in order to meet an injury requirement for unbolted occupant at high speed crash test. Consequently, today's occupant safety restraint system has a weakness due to an airbag induced injury at low speed crash event. This paper proposes a new crash algorithm to improve the weakness by suppressing airbag deployment at low speed crash event in case of belted condition. The proposed algorithm consists of two major blocks-crash severity algorithm and deployment logic block. The first block decides crash severity with two levels by means of velocity and crash energy calculation from acceleration signal. The second block implemented by simple AND/OR logic combines the crash severity level and seat belt status information to generate firing commands for airbag and belt pretensioner. Furthermore, it can be extended to adopt additional sensor information from passenger presence detection sensor and safing sensor. A simulation using real crash data for a 1,800cc passenger vehicle has been conducted to verify the performance of proposed algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.45-54
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• 1998

For proper release of side airbags, the onset of crash should be detected first. After crash detection, the algorithm has to make a decision whether the side airbag deployment is necessary. If the deployment is necessary, proper timing has to be provided for the maximum protection of driver or passenger. The side airbag release algorithm should be robust against the statistical deviations which are inherent to experimental crash test data. Deterministic optimization algorithms cannot be used for the side aribag release algorithm since the objective function cannot be expressed in a closed form. From this background, genetic algorithm has been used for the optimization. The optimization requires moderate amount of computation and gives satisfactory results.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.988-995
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• 2020

Air gun shock systems are commonly used as alternative explosion energy sources for underwater explosion (UNDEX) shock tests owing to their low cost and environmental impact. The airbag inflator of automotive airbag systems is also very useful to generate extremely rapid underwater gas release in labscale tests. To overcome the restrictions on the very small computational time step owing to the very fine fluid mesh around the nozzle hole in the explicit integration algorithm, and also the absence of a commercial solver and software for gas UNDEX of airbag inflator, an idealized airbag inflator and fluid mesh modeling technique was developed using nozzle holes of relatively large size and several small TNT charges instead of gas inside the airbag inflator. The objective of this study is to validate the results of an UNDEX response analysis of one and two idealized airbag inflators by comparison with the results of shock tests in a small water tank. This comparison was performed using the multi-material Arbitrary Lagrangian-Eulerian formulation and fluid-structure interaction algorithm. The number, size, vertical distance from the nozzle outlet, detonation velocity, and lighting times of small TNT charges were determined. Through mesh size convergence tests, the UNDEX response analysis and idealized airbag inflator modeling were validated.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.1
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• pp.96-109
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• 1996

The airbag system is known to be extremely efficient for the protection in an automobile crash. The performance of the airbag system is evaluated by real tests. However, the test is very difficult and expensive. Therefore, the computational simulations are carried out with low cost. The airbag analysis is included in the anlysis of the full-car crashworthiness. The behavior of the airbag can be predicted by a thermodynamic analysis. The contact force between the occupant and the airbag is calculated from the contact volume and the pressure in the airbag. The injury rate is evaluated from the contact force and the acceleration of dummies. So far, the contact is defined after the airgag is fully inflated. In many cases, the occupant is seated in the out-of-position and the contact can happen during the inflation process. A new algorithm has been developed for the out-of-position. To describe the inflation process precisely, the airbag is defined by a sphere and a torus. The injury is evaluated for the contact happened at any time. The developed algorithm is coded and interfaced with an existing software in the public domain. The full-car modeling is adopted from the previous study which is tuned for the regular position and real tests. Numerical experimentation have been carried out with a couple of dummies in the out-of-position and the injury processes are analyzed.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.94-99
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• 2020

Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

• Ocean Systems Engineering
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• v.7 no.2
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.179-185
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• 2006

Airbag deployment has been responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decision. This misfortune has led the authorities and the industries to pursue uniquely designed airbags incorporating crash-sensing technologies. This paper provides a thorough discussion underlying crash sensing algorithm approaches for the subject matter. Unfortunately, most algorithms used for crash sensing still have some problems. They either deploy at low severity or fail to trigger the airbag on time. In this work, the crash-sensing algorithm is studied by analyzing the data obtained from the variables such as (i) change of velocity, (ii) speed of the vehicle and (iii) acceleration. The change of velocity is used to detect crash while speed of the vehicle provides relevant information for deployment decision. This paper also demonstrates crash severity with respect to the changing speed of the vehicle. Crash sensing simulations were carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes. These toolboxes are also used to validate the results obtained from the simulated experiments of crash sensing, airbag deployment decision and its crash severity detection of the proposed system.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.3
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• pp.163-170
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• 2000

Accident statistics shows that the portion of fatal occupant injuries due to side impacts is considerably high. The side impact usually leads to a severe intrusion of side structure into the passenger compartment. Furthermore, the safety zone for the side impact is relatively small compared to the front impact. Those kinds of physics for side impact frequently result in a fatal injury for the occupant. Therefore, NHTSA and EEVC are trying to intensify the regulation for the occupant protection against side impact. Both the regulation and recent market trends are asking for an installation of side airbag. There are several types of system configuration for side impact sensing. In this paper, we adopt the acceleration-based remote sensing method for the side airbag control system. We mainly focus on the development of hardware and crash discrimination algorithm of remote sensing unit. The crash discrimination algorithm needs fast decision of airbag firing especially for high-speed side impact such as FMVSS 214 and EEVC tests. It is also required to distinguish between low-speed fire and no-fire events. The algorithm should have a sufficient safety margin against any misuse situation such as hammer blow, door slam, etc. This paper introduces several firing criteria such as acceleration. velocity and energy criteria that use physical value proportional to crash severity. We have made a simulation program by using Matlab/Simulink to implement the proposed algorithm. We have conducted an algorithm calibration by using real crash data for 2,500cc vehicle. The crash performance obtained by the simulation was verified through a pulse injection method. It turned out that the results satisfied the system requirements well.