Thermal Management Power Converter

Abstract

With growing use of internet and exponential growth in amount of data to be stored and processed (known as “big data”), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.

Keywords

Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server

I. Introduction

As we move to the exascale era, the requirements of data storage have grown exponentially and hence, the power consumption of data-centers has also increased [1, 2]. It has been reported that in year 2006 alone, the datacenters and servers in U.S. consumed 61 billion kilowatt hours of electricity, which is 1.5% of all US electricity consumption and has a monetary cost of $4.5 billion . Many data centers which support cloud services employ tens of thousands of servers which draw tens of Mega-Watts of power at peak . It has been estimated that such data centers draw power worth $9.3 million per year .

Moreover, the worldwide expenditure on enterprise power supply and cooling has been estimated to be more than $30 billion . Hence, achieving energy efficiency in data centers has become an issue of paramount importance.

Recently, several techniques have been proposed for managing power consumption of data centers (e.g. ). Due to large power consumption levels of data centers, use of these techniques has become essential to maintain both energy efficiency and cost efficiency. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several techniques which have been proposed for ensuring green operation of data centers. Since it is not possible to include detailed discussion of a large Technical Report, 2014.

number of techniques, we take the following approach to limit the scope of this paper. We focus on architectural-level and operation-level techniques for improving energy efficiency and not on circuit-level (device-level) techniques. Moreover, we only discuss the key design principles of different techniques and do not discuss the quantitative results, since different techniques have been tested using different evaluation platforms. We only include those techniques which have been evaluated for improving energy efficiency, and do not include the techniques aimed at improving performance, although they are also expected to improve energy efficiency.

The rest of the paper is organized as follows. In the next section, we present the factors which lead to increased power consumption. We also discuss their impact along with the need of managing data center power consumption. We then present a classification of power management techniques based on their characteristics, and then discuss some of these power management techniques in detail. Finally, we provide a discussion of future research directions and concluding remarks.

A. Reasons Behind Increase In Power Consumption

Due to recent growth in use of internet, the demand placed on data-centers has increased. Modern data centers typically contain up to tens of thousands of servers and provide 24×7 services to hundreds or thousands of users. As an example, YouTube serves as much as 100 million videos a day, while Facebook has nearly 400 million active users and 3 billion photos uploaded every month . These images, videos and text data are processed using computation-intensive software programs; and stored and accessed from data centers [12, 13]. Further, in recent years, use of high-performance computing techniques has also increased, which require trading off energy consumption for obtaining increased performance. For this reason, data centers have grown in sizes; which has led to increase in their power consumption.

B. Power Consumption Levels

Recent years have witnessed a huge increase in the power consumption of servers, data centers and supercomputers . The total energy consumption of data centers as a percentage of total US energy consumption has doubled between 2000 and 2007 . As an example, peak power consumption of the most powerful supercomputers in the TOP500 list of supercomputers ranges in tens of megawatts [17, 18].

This amount of power is sufficient to fulfill the demands of a city of 40,000 residents. Research has also shown that the buildings with data centers can be as much as 40 times more energy intensive than the conventional office buildings .

The high levels of power consumption demand a costly cooling infrastructure . For example, a 30,000 square feet data center with 10 megawatts power consumption consumes on the order of $5 million for cooling in a year . Moreover, for every watt of power consumed in the computing equipment, an additional 0.5 to 1W of power is required to operate the cooling system itself , which further adds to the cost.

C. Need Of Power Management

It has been shown that most of the time, the modern servers operate between 10% to 50% of maximum possible utilization [22, 23]. Further, at these utilization levels, the server energy efficiency also becomes very low . Thus, despite the fact that the average utilization remains low; there exist frequent, brief bursts of activity, and to meet the requirements of service-level-agreements (SLAs), operators are forced to allocate high amount of resources, which leads to poor energy efficiency .

Power management is also important from an economic point of view, since effective power management also improves operational efficiencies and increases compaction. Electricity costs for powering servers forms a major cost of operation in data centers and it has been estimated that in near future, energy costs may contribute even more than the cost of IT ([26, 27]). Further, a high ratio of cooling power to computing power restricts the compaction and consolidation possible in data centers, which results in increased operation costs. For example, the high power density poses significant challenges in routing the large amounts of power needed per rack. Currently the power delivery in typical data centers is near 60 Amps per rack and it is expected to reach the limit of power delivery, which will severely affect the operation of servers .

The large power consumption and high concentration of nodes in data centers leads to increased node failures. It has been observed that a 15 degree Celsius rise increases the failure rates in hard-disks by a factor of two . Hence, maintaining the computer systems at proper temperature is important for ensuring maximum reliability, longevity, and large return on investment. Finally, large power consumption also has adverse environmental impact, e.g. large carbon emission . For these reasons, the design of green solutions for modern data centers has become a topic of paramount importance. Hence, in this paper, we survey techniques for managing power consumption in data centers.

Iii. Overview Of Power Management Techniques

In recent years, researchers have proposed several techniques for managing power consumption in data centers. While it is very difficult to draw sharp boundaries of classification; for the purpose of study, we broadly classify the techniques in the following four types.

1) DVFS (dynamic voltage/frequency scaling) based techniques . 2) Techniques which transition the server/node to low-power or turned-off state; or use server- consolidation based approach to allocate only required amount of server resources [23, 43–55].

3) Workload management or task scheduling based techniques . 4) Thermal-aware or thermal-management techniques which take into account the thermal properties [56, 59, 61, 64–70]. Further, some techniques address the issues related to cooling in data centers [21, 59, 71].

Based on other characteristics/parameters, the techniques can be further classified. While most techniques aim to reduce average power (energy), a few techniques aim to reduce peak power consumption , or limit power (called power capping) [73, 74]. A few techniques aim to save energy while keeping the performance degradation bounded or maintaining QoS (quality-of-service) guarantee [39, 41, 43, 50, 75– 79].

Several techniques use analytical approach and offer control-theoretical algorithms with provable guarantees [38, 74, 79–84], while most other techniques use system approach and focus only on implementation. Some researchers propose use of renewable energy sources [85, 86]. Some researchers aim to reduce disk energy , while others focus on saving main memory energy [35, 90–93] in data centers.

A. Dvfs Based Techniques

Dynamic voltage and frequency scaling (DVFS) is a widely-used power-management technique where the clock frequency of a processor is dynamically adjusted to allow a corresponding reduction in the supply voltage to achieve power saving . DVFS is especially useful for memory-bound workloads.

The power consumption of a CMOS (Complementary metal oxide semiconductor) circuit is given by

(1)

Here C shows the capacitance of the transistor gates, F shows the operating frequency and V shows the supply voltage. The frequency at which circuit is clocked determines the voltage required for stable operation; and hence, by intelligently reducing the frequency, the supply voltage can also be reduced; which leads to significant power savings due to V 2 relationship shown above. The limitation of DVFS, however, is that a reduction in frequency also reduces the performance of the circuit and hence, DVFS may adversely affect the performance of the processor. For this reason, DVFS needs to be intelligently applied, to maintain high performance. In data centers, DVFS technique is applied to manage the power consumption of multicore processors, DRAM memories and other components.

Sharma et al. propose adaptive algorithms for dynamic voltage scaling in QoS-enabled web servers. Their algorithms aim to minimize energy consumption subject to service level agreements (SLAs). The algorithms are implemented inside the Linux kernel. Their algorithms minimize the energy consumption using a feedback loop which regulates the frequency and voltage levels to keep the instantaneous utilization bounded.

Hsu and Feng propose an algorithm for the dynamically varying voltage and frequency of the processor to realize energy saving while keeping the performance loss bounded. The algorithm takes decisions at the end of fixed time period. Their algorithm uses an estimation model to relate the intensity level of off-chip accesses to total execution time. Using this, the algorithm computes the lowest CPU frequency which keeps the performance loss bounded, while saving largest possible amount of energy.

For multiprocessor environment, the same algorithm is repeated for each processor to set the frequency of the processor individually. Xu et al. propose a technique for saving energy in embedded clusters. Their technique adjusts the number of active nodes based on the system load. In their technique, each node in the cluster performs dynamic voltage scaling independently and runs at the lowest frequency at which it can keep up with the request arrival rate. To prevent the system from reacting to short-term changes in workload, at a time, their technique changes only one node from active to inactive and vice-versa in each interval.

Horvath et al. present methods to dynamically adjust the server voltages to minimize the total system power consumption, while also meeting end-to-end delay constraints in a multi-tier web service environment. In their architecture, server machines use DVS-capable processors. They propose coordinated distributed voltage scaling policy, where decisions on frequency adjustments are made on each locally while minimizing overall power consumption. Compared to other methods such as server on/off, use of dynamic voltage scaling incurs less overhead which facilitates aggressive energy saving.

Deng et al. use DVFS mechanism to save memory energy. Their technique lowers the frequency of DRAM devices, memory channels and memory controllers at the time of low memory activity. This reduces the memory power consumption. They have also extended their technique for coordinating DVFS across multiple memory controllers, memory devices and channels to reduce the overall system power consumption .

B. Server Consolidation and Power State Transitioning Based Techniques As discussed before, modern servers typically operate at low utilization levels. Moreover, to cater to the peak demand and service-level-agreements and ensure reliability, high amount of server resources needs to be allocated which leads to poor energy efficiency . To address this challenge, many approaches have been proposed. Server consolidation is one such approach aimed to ensure efficient usage of server resources by reducing the total number of servers required by a data center, while still delivering same throughput. In this approach, the existing applications are consolidated onto fewer servers, such that unused servers can be transitioned into low-power (or turned-off) state and the used servers can be operated at full utilization levels. Another approach is transitioning the server resources into low-power mode during periods of low activity. These approaches have been widely used to improve energy efficiency of data centers.

Chun et al. propose a hybrid data center design which uses heterogeneous platforms to save power. Under low utilization levels, their technique transfers the running tasks from a high-power, high performance system to a low-power, low-performance system and turns off the higher power servers. Thus, server virtualization along with task migration reduces the number of active servers by consolidating many servers with low-average utilization to a few servers operating at high utilization. The limitation of sever consolidation, however, is that it may lead to slow response times and high transition costs.

For ensuring energy-efficient operation of data centers, Chase et al. discuss a system resource management based approach. Their method controls server allocation and routing of requests to selected servers using a dynamically reconfigurable switch; and thus enables achieving a trade-off between service quality and cost. For optimizing energy consumption, incoming request traffic is continuously monitored and only desired amount of server resources are allocated such that the service level agreements can be fulfilled. Since internet sites observe highly varying usage patterns (e.g. as much as 11:1 peak-to-trough ratio of usage), their method enables intelligent adaptive resource provisioning.

Ranganathan et al. propose a technique for managing server power at the ensemble (i.e. collection of systems) level instead of individual server level. Their design observes the resource-usage trends across multiple systems. By taking advantage of inter-server variations, their technique allows active server to steal power from the inactive servers. Their technique facilitates reductions in the requirements for power delivery, power consumption, and cooling in the data centers.

To leverage the variation in workload for saving energy, Anagnostopoulou et al. propose a “barely- alive” server design. Their design transitions the servers to a barely-alive power state, where the server can be still accessed, even if many of its other components are turned off. Their design uses a small embedded processor to only keep the memory of idle servers active so that in-memory application code/data remain unaffected, and the free memory space can be used for cooperative application data caching.

To reduce the energy consumption of servers during periods of reduced load, Rusu et al. discuss a cluster-wide QoS-aware technique which uses dynamic reconfiguration based approach. For a given workload, their algorithm dynamically decides the servers which need to remain turned on/off to minimize global power consumption. To minimize the time penalty of server on/off, the algorithm accounts for the booting time of the server and turns on a server before it is actually needed. The authors also use dynamic voltage scaling (DVS) to conduct QoS-aware power management. The authors show the use of their technique in the context of a web server.

Ghosh et al. propose a technique to save energy in data centers by using out-of-band management processors which are typically used for remotely managing a server, to satisfy the I/O requests from a remote server. By transferring the load from the primary server to the management processor, their technique allows the primary server to stay in low-power state for longer time, which improves the energy efficiency.

To utilize the opportunity of energy saving at multiple levels, Da Costa et al. present an integrated framework, called GREEN-NET. GREEN-NET provides a multi-faceted approach for saving energy in clouds and grids. Their approach has three levels. At first level, it increases the awareness of users of their energy consumption. At second level, it involves the users in decisions to trade-off performance for saving energy. Finally, at third level, it conducts adaptive management of grids by techniques such as server turn-off to save energy.

L. Liu et al. use virtual-machine migration approach where a virtual machine (VM) is transferred across physical computers to enable server consolidation and allow more computers to be turned-off. Their technique dynamically decides the time to trigger VM migration, and the alternative physical machines to achieve optimal VM placement. Their technique enables live migration of VMs, such that the users can hardly notice that their applications are being or have been migrated.

Leverich et al. use per-core power-gating (PCPG) approach to manage power consumption of multicore processors. Their technique works by selectively turning on/off the power supply to individual cores of a multicore processor based on the utilization and quality-of-service requirements. The authors have also shown that by syngersitically combining their technique with DVFS technique, additional power savings can be obtained.

C. Workload Scheduling Based Techniques

Modern data centers typically have a large number of servers and hence, the decision about placement of workloads on specific servers significantly affects the heat-dissipation and power-consumption. A poor placement may greatly increase the temperature of the building which will further increase the heat-dissipation of the servers and also increase the cooling requirements. Hence, workload-scheduling techniques have been proposed which intelligently place the workloads on available servers with the goal of saving power, reducing the temperature and the cooling requirements.

Nathuji and Schwan propose a dynamic power management technique to support the isolated and independent operation of VMs running on a virtualized platform, while globally coordinating the diverse power management strategies applied by the VMs to the virtualized resource. Their technique uses a set of virtualized power states to permit guest VMs to run their own, independent power management methods. Further, guest VM-level power management policies act upon these states to minimize power consumption, while meeting application requirements.

A thermal-aware technique for spatial workload placement in data centers is presented by Banerjee et al. . One of their techniques uses the information about dynamic behavior of computer room air conditioned (CRAC) to place the jobs in a manner which reduces the cooling demands from the CRACs.

By integrating this technique with a temporal scheduling technique, the decision about when and on which server to execute a job can be taken, leading to a spatio-temporal scheduling technique. Bradley et al. present a predictive power management technique for saving power in parallel computer systems. In web-based applications where the load on a system varies a lot, minimizing power consumption while meeting the demands of the workload is challenging. For such systems, their technique projects workload ahead of time to allow adequate resources to be powered on and kept ready for work when required to execute the workload. Their algorithm uses CPU utilization data to estimate the workload demand. When the utilization becomes larger than a predefined threshold, additional servers are powered on to reduce utilization on all servers to below that threshold. Conversely, when utilization at all servers is below that threshold and there is adequate capacity in the resulting server group to absorb the load of at least one server without any resource on any server being over-utilized, one or more server are powered off.

D. Thermal-Aware Power Management Techniques

Several power management techniques work in thermal-unaware manner, i.e. they do not take the temperature-dependence of server power consumption into account. However, the heat-dissipation of processor components has strong temperature dependency and hence, an increase in operating temperature leads to increased heat-dissipation, which further increases the temperature and so on. This may drastically increase the component failure rates. To address this, several techniques have been proposed which manage the power consumption of the data centers while taking into account the thermal properties.

To minimize the total energy costs of data-center operation while providing a reliable thermal environment, Tang et al. propose a thermal-aware task-scheduling technique. In a blade server, multiple blades are integrated into each chassis, in which blades share a common supply and cooling fan. Each blade may itself have many processors. Operation of a chassis incurs chassis startup power consumption, along with actual power consumption of the blades. Hence, the power consumption cost of adding a task to one chassis may be different depending upon whether it involves waking up an idle chassis or an idle blade. Based on this observation, they evaluate several thermal-aware task-scheduling techniques, which exercise the trade-off between cost of start-up and power saving coming from turning off the blade servers.

Moore et al. present techniques to control the heat generation in servers using temperature-aware workload placement. Their techniques take into account the fact that since the heat may travel a large distance inside the data center before arriving at a temperature sensor, cooling inefficiencies can often arise in locations which are spatially uncorrelated from the place where the heat originates. Based on this, their technique uses the information about steady-state hot-spots and cold-spots in the data center to develop scheduling algorithms.

As discussed above, in data centers, work-scheduling algorithms have a significant influence on temperature distribution. Li et al. present a thermal forecasting model to predict the temperatures near the servers in a data center. Their model is based on continuous streams of temperature and airflow measurements. Their model integrates both physical laws and sensor observations in the data center. It uses the data obtained from the sensors to learn the parameters of the cyber-physical system of the data center.

E. Other Techniques

While most techniques aim to reduce energy consumption, a few techniques have also been proposed which aim to control peak power consumption. Lefurgy et al. present a technique for controlling the peak power consumption of a high-density server. Their technique uses a feedback controller to periodically select the highest performance state while keeping the system within a fixed power constraint.

The control loop is designed using the control theoretic methodology which helps in obtaining analytical guarantee on system stability and controller performance, despite variation in workloads. Their technique allows controlling power at different time-granularity, e.g. one second, eight seconds etc. The authors have shown that their technique performs better than the ad-hoc and open-loop techniques.

To fulfill the needs of several performance-critical and data-intensive applications that execute on many data center platforms , a large amount of main memory resources need to be provisioned. Hence, improving the energy efficiency of main memories is important to achieving data center energy efficiency.

Yoon et al. propose a technique for saving memory power consumption in data centers by intelligently utilizing low power mobile DRAM components. By using buffering mechanism to aggregate the data outputs from multiple ranks of low frequency mobile DRAM devices (such as 400MHz LPDDR2), their technique enables achieving high bandwidth and high storage capacity equal to server-class DRAM devices (such as 1600MHz DDR3).

Chatterjee et al. propose a technique to take advantage of heterogeneity in DRAM memories to accelerate critical word access. In recent years, processors use DRAM chips with vastly differing latency and energy characteristics. Their technique recognizes the critical word in a cache line which is placed in a low-latency region of the main memory. The remaining non-critical words of the cache line are placed in a low-energy consuming region. Thus, their approach enables achieving high performance while reducing memory energy.

Deployment In Out-Of-Position Situations

D. Bendjaballah1, A. Bouchoucha1, M. L. Sahli1,2* and J-C. Gelin2

Abstract

Side-impact collisions represent the second greatest cause of fatality in motor vehicle accidents. Side-impact airbags have been installed in recent model year vehicle due to its effectiveness in reducing passengers’ injuries and fatality rates. In meeting these requirements, simulations of folding and deploying airbags are very useful and are widely used. The paper presents a simulation method for the deploying airbags using three materials in different working conditions. Finite element analysis is primarily used to evaluate this concept. In these simulations, the gas flow is described by the conservation laws of mass, momentum, and energy. The numerical results indicate that the FE method in this paper is capable of capturing airbag deploying process accurately.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Keywords: Airbag simulations, Out-of-position, Crash, Modeling, Out-of-position

Background

The passive safety of cars has become a very high prior- ity issue for the automotive industry. Today, there are not only one or two airbags in a car; certain models have ten times more than that. With the increasing usage of airbags, the number of accidents where the airbag itself can cause an injury to the occupant also increases

(Augenstein Et Al. 2003; Gabauer And Gabler 2010;

Audrey et al. 2011). As is well known, safety belts are also now devices designed to provide protection to the users of vehicles during crash events, minimizing the loads necessary to adapt their movement to the move- ment of the car (Freesmeier and Butler 1999; Schmitt et al. 1997). In general, the seat belt is designed to restrain the occupant in the vehicle and prevent the

Occupant From Having Harsh Contacts With Interior

surfaces of the vehicles. The airbag acts to cushion any impact with vehicle structure and has positive internal pressure, which can exert distributed restraining forces over the head and face. As a safety component of auto- mobile, an airbag decreases occupants’ injury likelihood effectively in case of an accident (Ruff et al. 2007). These safety elements can reduce the death rates on the roads, and its protection effects have been widely approved (Crandall et al. 2001; Teru and Ishikawa 2003). With computational tools such as finite element methods designed for dynamic contact problems, crashworthiness simulations can now be used with reliable accuracy to evaluate occupant protection in various collision condi- tions with safety metric/parameters such as acceleration, head injury criteria, intrusion distance, intrusion vel- ocity, and neck forces (neck injury risk or whiplash).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Thus, new types of airbag products are being developed to handle different collision scenarios.

Become Standard Equipment On Most New Passenger

vehicles (Braver and Kyrychenko 2004; Teng et al. 2007; Yoganandan et al. 2007). The airbag cushion is com- posed of a woven fabric which is rapidly inflated during a car crash. The airbag dissipates the passenger’s kinetic energy thereby reducing injury through biaxial stretching of the fabric bag and escaping gas through vents. There- fore, the performance of the airbag is greatly influenced by the mechanical properties of the fabric. Generally, air bags are designed to deploy in a crash that is equivalent to a vehicle crashing into a solid wall at 8 to 14 mph.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Air bags most often deploy when a vehicle collides with another vehicle or with a solid object like a tree. There are various types of airbags: frontal, side-impact, and curtain airbags. In general, the passenger side airbags are usually larger than the driver airbags (see Fig. 1).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Besançon, France

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Bendjaballah et al. International Journal of Mechanical

Doi 10.1186/S40712-016-0070-2

Extensive studies have shown that the airbag deploy- ment in load cases consists of two occupant loading phases: a punch-out effect where the airbag bursts out of its container with the airbag and airbag module cover accelerating towards the occupant and a second loading phase during which the airbag is taking on its deployed shape and volume (membrane-loading effect). Bankdak et al. (2002) developed an experimental airbag test system to study airbag-occupant interactions during close proximity deployment. The results provided insight for simulating the effect of inflation energy and mass flow on target response. Bedard et al. (2002) found that while left-side (driver-side) impacts accounted for only 13.5% of all crashes, the fatality rate among these

Crashes Was 68.3% In Comparison To Front Impact

(48.3%), right-side impact (31.3%), and rear impact (38.4%). These studies underscore the importance of oc- cupant safety during side-impact collisions. In the last years, the current market requested to reduce the time and cost airbag development. In order to achieve this result, virtual simulations play an important role since they allow to minimize the number of experimental tests (Pei et al. 2013; Cao et al. 2014). Several simulation models of airbag were established (Wang et al. 2007). It is feasible to optimize the parameters of airbag deploy- ment using simulation technology. Experimental and numerical studies have quantified injury risks to close- proximity occupants from deploying side airbags. These studies have focused on the prevention of the most ad- verse effects of airbag deployment (Duma et al. 2003).

Other studies have proposed airbag characteristics to minimize particular biomechanical responses (Haland and Pipkorn 1996). In a more recent study, Marklund and Nilsson (2003) compared deformation patterns with experimental data as well as the computational costs associated with three different airbag deployment simu- lation methods; they concluded that the SPH method is relatively inexpensive and produces incremental deform- ation patterns that compare most closely to the experi- mental results. The process of inflation of an airbag is one of the determining factors in saving lives. The duration from the initial impact of the crash to the full inflation of an airbag is about 40 ms, and during this time, the airbag goes from being in a folded state to a fully inflated state, with a high internal pressure. After achieving this state, the airbag begins to deflate, thus providing a nice cushion for the body impacting it.

Ideally, the person in the crash should come into contact with the airbag at this time. In the present study, a large volume passenger side airbag model is developed to handle different collision scenarios. The main aim is evaluate the performance of deploying of passenger side airbag using finite element methods (FEM).

Materials

The tensile specimens were made in different airbags (P: Peugeot, R: Renault, and VW: Volkswagen) with a length of 200 mm long and a width of 40 mm. Table 1 shows the mechanical properties of the airbag.

Tensile Tests

To determine the mechanical properties of the material of airbag used in the test pieces, tensile tests were performed on Lloyd EZ20 universal testing machine in Constantine. These tests were conducted using rect- angular samples. The axial force and axial displacement acquired during a test are converted into stress and the strain in order to be used for the fabric material model.

The continuous recording of the stress-strain data was performed during both the load and unload phases. A minimum of five samples were made in order to check the repeatability of the measurements. All the data was collected by using a PC-based data acquisition system and analyzed by commercial software. The picture frame test device that is made for this study is shown in Fig. 2.

Fig. 1 a Frontal and side airbags. b Oblique view of facet occupant model in sitting posture following airbag deployment (Lim et al. 2014)

0.150

Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

Page 2 Of 9

Figure 3 shows the stress-strain relationship of the airbag sample under axial tensile loads. The results are showing a linear increase in extension with the increas- ing stresses. This is an expected output and it confirms with the theoretical behavior of a sample subjected to tensile stress. The rupture strain values for different airbags (R/P/VW) were 0.322, 0.441, and 0.472, respect- ively. The measured elastic parameters (i.e., Young’s modulus E and initial yield strength) and Poisson’s ratio are summarized in Table 2. The tensile tests of the woven fabrics can show differences on mechanical prop- erties because woven fabrics can resist in-plane shear loads once the yarn lock-up angle has been reached. The differences of material property on material direction can affect the shape of fully deployed bag (see Fig. 3b).

Theoretical Background

Numerical simulations of airbags use very complex and techniques such as an orthotropic model to identify the mechanical behaviors during the airbag inflation and the fluid mechanics (gas flow) to describe the inflator gas flow (pressure gradient) and improve the representation of the pressures within the airbag. To model the airbag as an orthotropic model, three material constants have to be provided. Assuming a plane stress condition, the

Ð1Þ

where σ is the normal stress and τ is the shear stress, the subscript refers to the principal material directions, i.e., the fill and warp directions. Also, ε and γ are the strain components. The material elastic constants Qij are

Ð2Þ

where E1 and E2 are the Young’s modulus in the fill and wrap directions and G12 is the shear modulus of the fabric material. νij is the Poisson ratio of the material.

The gas exerts a pressure load on the airbag causing it to expand. This expansion puts the airbag under tensile stress lowering the expansion rate. In this study, heat conduction and heat transfer is not taken into account.

Fig. 2 A photograph of Lloyd EZ20 universal testing Fig. 3 Stress versus strain using Lloyd EZ20 machine for a three different airbags at 0° and 90° and b VW airbag test specimens at

Different Angles

Table 2 Physical and mechanical properties of the airbag

Page 3 Of 9

In the deployment of an airbag, an inflator supplies high velocity gas into an airbag causing it to expand rapidly. The gas inside the airbag is assumed to be ideal, to be of constant entropy, and to satisfy the equation of state:

Ð3Þ

Here p, ρ, and e are respectively the pressure, density, and specific internal energy, and γ is the ratio of the heat capacities of the gas. The gas flow is described by the conservation laws for mass, momentum, and energy that

Ð4Þ

here, V is a volume, A is the boundary of this volume,

N Is The Normal Vector Along The Surface A, And U

denotes the velocity vector in the volume. Applying Bernoulli’s equation in the case of an ideal gas with

Ð5Þ

Here, the subscript ex denotes quantities at the throat of the tube. Furthermore u, p, and ρ denote the quan- tities inside that part of the tube that is supplying mass.

Materials And Boundary Conditions

The airbag system mainly consists of three parts: the airbag itself, the inflator unit, and the crash sensor or diagnostic unit. Thus, to study the behavior of the airbag using FE simulations, we need to have an FE model of the airbag in the folded position. A FE model of the airbag was used to simulate the test condition as shown in Fig. 5. LS-DYNA® material model FABRIC (MAT_34) is used to simulate the airbag material. It is a variation of the layered orthotropic material model. Additionally, in the LS-DYNA® material model, fabric leakage can be accounted for. However, for this CAB material, the leak- age is almost negligible and therefore no leakage is specified. The mechanical properties can be determined from the physical test. Typical material properties for airbag fabrics are taken as given in Chawla et al. (2004a) (Table 3). These properties are used to simulate inflation process of airbag (see Table 1). The car dashboard is modeled as the rectangular thin plate using a MAT_RI-

Gid Material, And The Degrees Of Freedom Are Con-

strained in all the directions. The similar properties of thermoplastic polymer are assigned for contact purposes. The porosity of the fabric is assumed zero. The nitro- gen gas is taken for inflating the airbag. Properties of nitrogen gas and initial bag conditions are shown in Table 4. The example on which we perform the study is a typical passenger side airbag. The geometric de- tails have been measured from a commercially avail- able airbag. The initial state of the airbag is a closed rectangular whose sides are to be finished to 482 × 635 mm2 and is shown in Fig. 4.

Table 3 Material properties of airbag and rigid plate used in FE

–

Table 4 Initial values used for FE simulation of the swelling of

3.33 × 10−4

Fig. 4 The initial airbag geometry in the form of a rectangular Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

Related Journal Articles & DOI Links

Selected peer-reviewed publications relevant to 12 Lead ECG Acquisition. Click the DOI to access the full paper (may require institutional access).

Why Choose Us?

Bangalore guidance for robotics, Spectre and autonomous systems projects.