Search Results

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

For over 30 years, field research and laboratory testing has consistently demonstrated that proper utilization of a seat belt dramatically reduces the risk of occupant death or serious injury in motor vehicle crashes. The injury prevention benefits of seat belts require that they remain fastened during collisions. Federal Motor Vehicle Safety Standards and SAE Recommended Practices set forth seat belt requirements to ensure proper buckle performance in accident conditions. Numerous analytical and laboratory studies have investigated buckle inertial release properties. Studies have repeatedly demonstrated that current buckle designs have inertial release thresholds well above those believed to occur in real-world crashes. Nevertheless, inertial release theories persist. Various conceptual amplification theories, coupled with high magnitude accelerations measured on vehicle frame components are used as support for these release theories.

An automated process was developed for the calibration of numerical aftertreatment models. The chemical kinetic mechanism examined in this case was part of a simplified SCR model. The process adopted for calibrating the SCR model was based on a micro-population multi-objective genetic algorithm. The algorithm developed was used to calibrate the SCR model using data derived from another, more detailed model to ensure that the evaluation focused only on the effectiveness of the calibration process and was not affected by issues of experimental inaccuracies or details of the model chemistry involved.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

Engine operating strategies typically geared towards higher fuel economy and lower NOx widely affect exhaust composition and temperature. These exhaust variables critically drive the performance of After Treatment (AT) components, and hence should guide their screening and selection. Towards this end, the effect of H2 level in diesel exhaust on the performance of a Diesel Oxidation Catalyst (DOC) was studied using flow reactor experiments, vehicle emission measurements and mathematical models. Vehicle chassis dynamometer data showed that exhaust from light-duty and heavy-duty diesel trucks contained very little to almost no H2 (FTP average CO/H2 ∼ 40 to 70) as compared to that of a gasoline car exhaust (FTP average CO/H2 ∼ 3). Two identical flow reactor experiments, one with H2 (at CO/H2 ∼ 3) and another with no H2 in the feed were designed to screen DOCs under simulated feed gas conditions that mimicked these two extremes in the exhaust H2 levels.

A thorough understanding of vehicle exhaust aftertreatment system performance requires time-resolved emissions measurements that accurately follow driving transients, and that are correctly time-aligned with exhaust temperature and flow measurements. The transient response of conventional gas analyzers is characterized by both a time delay and an attenuation of high-frequency signal components. The distortion that this imposes on transient emissions measurements causes significant errors in instantaneous calculations of aftertreatment system efficiency, and thus in modal mass analysis. This creates difficulties in mathematical modeling of emissions system performance and in optimization of powertrain control strategies, leading to suboptimal aftertreatment system designs. A mathematical method is presented which improves the response time of emissions measurements. This begins with development of a model of gas transport and mixing within the sampling and measurement system.

A Lunar habitat will be highly sensored and generate large amounts of data or telemetry. For this data to be useful to humans monitoring these systems and to automated algorithms controlling these systems it will need to be converted into more abstract data. This abstracted data will reflect the trends, states and characteristics of the systems and their environments. Currently this data abstraction process is manual and ad hoc. We are developing a Data Abstraction Architecture (DAA) that allows engineers to design software processes that iteratively convert habitat data into higher and higher levels of abstraction. The DAA is a series of mathematical or logical transformations of telemetry data to provide appropriate inputs from a hardware system to a hardware system controller, system engineer, or crew. The DAA also formalizes the relationships between data and control and the relationships between the data themselves.

Piezoelectric self-sensing allows to measure frequency response functions of dynamical systems with one single piezoelectric element. This piezoceramics is used as actuator and sensor simultaneously. In this study, a model-based piezoelectric self-sensing technique is presented to obtain potential squealing frequencies of an automotive disc brake. The frequency-response function of the brake system is obtained during operation by measuring the current flowing through the piezoelectric element while the piezoelectric element is driven by a harmonic voltage signal with constant amplitude. The current flow is composed of the part which is required to drive the piezoelectric element as an actuator and a second part which is the sensor signal that is proportional to the vibration amplitude of the attached mechanical system. Typically the first part is dominant and the influence of the mechanical system is marginal.

This paper describes the joint development by Tenneco and Pi Shurlok of a complete diesel engine aftertreatment system for controlling particulate matter emissions. The system consists of a DOC, DPF, sensors, controller and an exhaust fuel injection system to allow active DPF regeneration. The mechanical components were designed for flow uniformity, low backpressure and component durability. The overall package is intended as a complete PM control system solution for OEMs, which does not require any significant additions to the OEM's engine control strategies and minimizes integration complexity. Thus, to make it easier to adapt to different engine platforms, ranging from small off-road vehicle engines to large locomotive engines, model-based control algorithms were developed in preference to map-based controls.

The presented paper introduces the new software complex aimed at simulation of the riveting process as applied to aircraft parts. The software complex implements the novel mathematical model based on minimization of the potential energy. The paper gives the detailed description of the mathematical model and particularizes the main features of the software. The physical and numerical tests aimed at validation of the software are also described in the paper.

The electrical and mechanical failures (such as bearing and winding failures) combine to cause premature failures of the generators, which become a flight safety issue forcing the crew to land as soon as practical. Currently, diagnostic / prognostic technologies are not implemented for aircraft generators where repairs are time consuming and its costs are high. This paper presents the development of feature extraction and diagnostic algorithms to ultimately 1) differentiate between these failure modes and normal aircraft operational modes; and 2) determine the degree of damage of a generator. Electrical signature analysis based features were developed to distinguish between healthy and degraded generators while taking into account their operating conditions. The diagnostic algorithms were developed to have a high fault / high-hour detection rate along with a low false alarm rate.

Requirements analysis for aircraft simulators is often driven by photographs and videos of the actual aircraft. An engineer may gather and organize hundreds or even thousands of source photos of various instruments and devices unique to the aircraft. Managing all of this source information and referencing it to generate software requirements can be challenging and time-consuming. This paper explores Content Based Image Retrieval (CBIR) techniques to automatically process and search those images to generate basic requirements and to facilitate reuse. An unsupervised clustering algorithm groups source images based on minimal user input. Images processed in this way can also be queried by image similarity, thereby allowing project managers to find common source material among projects. The effectiveness of these techniques is demonstrated on an example cockpit.

Complexity and nonlinearity of engines makes precise first principle engine models often difficult to obtain, as for instance for emissions. System identification is a well known possible alternative, successfully used in several automotive applications. In most cases system identification is concerned with the estimation of the unknown parameters of a known set of equations. Unfortunately, for many engine subsystems, there is no sufficiently precise or real time suitable model. This paper presents a sequential algorithm which allows to derive real time suitable models on line by a combination of model structure hypothesis of increasing complexity and an associated optimal input design and selection process. This paper introduces the method and shows its use both for a rather simple and a very difficult engine identification task, a dynamical model of the airpath of a Diesel engine and a dynamical model of nitrogen oxides and particulate matter.

After launch and activation activities, the Columbus module started its operational life on February 2008 providing resources to the internal and external experiments. In March 2008 two US Payloads were successfully installed into Columbus Module: Microgravity Sciences Glovebox (MSG) and a US payload of the Express rack family, Express Rack 3, carrying the European Modular Cultivation System (EMCS) experiment. They were delivered to the European laboratory from the US laboratory and followed few months later by similar racks; Human Research Facility 1 (HRF1) and HRF2. The following paper provides an overview of US Payloads, giving their main features and experiments run inside Columbus on year 2008. Flight issues, mainly on the hydraulic side are also discussed. Engineering evaluations released to the flight control team, telemetry data, and relevant mathematical models predictions are described providing a background material for the adopted work-around solutions.

The focus of this research is to perform a detailed investigation of the tradeoffs between mass, efficiency, service factor (SF), power factor (PF), and cost of commercially available induction machines (IMs). To support this effort, data from a large number of IMs is used to establish Pareto-optimal fronts between these competing objectives. From the Pareto-optimal fronts, relatively straightforward models are formulated for the mass versus loss, cost versus loss, SF versus mass, PF versus cost. Parameters of the models are obtained using a genetic algorithm (GA).

This paper develops a novel integrated navigational system for autonomous vehicle motion control. Vehicle control is defined in terms of the required vehicle steering angle, rate of steering change and speed. This paper proposes predictive control in the image plane. The proposed predictive control enables the navigation on the desired path, reduces the control complexity and increases the application space for multiple types of vehicles. The paper investigates vehicle control stability; especially in scenarios containing varying curvature turns and variable vehicle speeds. The primary emphasis of this paper is on vehicle control rather than scene analysis. To demonstrate the proposed vehicle control, a computer vision based multi-lane detection algorithm is introduced. The control strategy is applied such that the vehicle maintains position within the lane boundaries. Stability of the control algorithm is tested and demonstrated in multiple scenarios using 3-D simulation results.

In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks (i.e., gross vehicle weight greater than 4,536 kg). The Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed by the Federal Motor Carrier Safety Administration (FMCSA) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Visual warnings have been shown to be effective, assuming the following driver is looking directly at the warning display or has his/her eyes drawn to it. A visual warning can be placed where it is needed and it can be designed so that its meaning is nearly unambiguous. FMCSA contracted with the Virginia Tech Transportation Institute (VTTI) to investigate potential benefit of additional rear warning-light configurations as rear-end crash countermeasures for heavy trucks.

Virtual testing is a method that simulates lab testing using multi-body dynamic analysis software. The main advantages of this approach include that the design can be evaluated before a prototype is available and virtual testing results can be easily validated by subsequent physical testing. The disadvantage is that accurate specimen models are sometimes hard to obtain since nonlinear components such as tires, bushings, dampers, and engine mounts are hard to model. Therefore, virtual testing accuracy varies significantly. The typical virtual rigs include tire and spindle coupled test rigs for full vehicle tests and multi-axis shaker tables for component tests. Hybrid simulation combines physical and virtual components, inputs and constraints to create a composite simulation system. Hybrid simulation enables the hard to model components to be tested in the lab.

Accident data show that the head and the chest are the most frequently injured body regions in side impact fatal accidents. Curtain side air bag (CSA) and thorax side air bag (SAB) have been installed by manufacturers for the protection devices for these injuries. In this research, first we studied the recent side impact accident data in Japan and verified that the head and chest continued to be the most frequently injured body regions in fatal accidents. Second, we studied the occupant seating postures in vehicles on the roads, and found from the vehicle's side view that the head location of 56% of the drivers was in line or overlapped with the vehicle's B-pillar. This observation suggests that in side collisions head injuries may occur frequently due to contacts with the B-pillar. Third, we conducted a side impact test series for struck vehicles with and without CSA and SAB.

Intelligent tires are envisioned to be an important part of the future vehicle control systems and the three dimensional wireless MEMS accelerometers embedded inside the tire stand out as a promising candidate for the development of intelligent tires. The first part of the paper focuses on accelerometer based tire sensors for the estimation of slip angle and tire/road friction coefficient. We use a simple tire finite element model to generate lateral, tangential and radial tire accelerations for a fixed load and slip angle. The profiles are validated by using experimental data. The simulated acceleration profiles are used for the estimation of slip angle and tire/road friction coefficient. We present the estimation algorithms, promising simulative results and output sensitivities studies focused on the effects of changes in normal load, tire pressure and vehicle velocity.