Search Results

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

As vehicle development timelines continue to shorten, it is necessary for the full vehicle NVH engineer to be able to predict performance without actual prototypes. There has been significant advancement in the accuracy of finite element modeling techniques of trimmed bodies; however accuracy is still low in the road noise mid frequency range from 150-400Hz. Also, calculation times for these frequencies are long, with very large results files in some cases. To alleviate these limitations, a Hybrid approach has been used, where a finite element suspension and drive train model is coupled with a test based Frequency Response Function (FRF) model of the trimmed body. The predicted road noise level was compared to actual vehicle tests and exhibited excellent correlation.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

Two high voltage, Series Connected Boost Regulators (SCBR) were developed to demonstrate the benefits of the SCBR topology for low Earth orbit communication satellites. The resulting breadboards had a power density of 1,200 W/kg and a measured efficiency of 95-99%. Several peak power tracking methods and algorithms were implemented to demonstrate the ability of the SCBR to peak power track a solar array. The peak power tracker derived maximum power at all times and reduced the number of sunlight battery discharges necessary. The breadboards also demonstrated several modularity techniques, which will allow a common SCBR module to be used in several applications. The breadboards were tested in an end-to-end high voltage test facility using high fidelity solar array simulators, an actual NiH2 battery, and simulated constant power loads. Design details and test results are presented.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

This research was to model a 6×4 tractor-trailer rig using TruckSim and simulate severe braking maneuvers with hardware in the loop and software in the loop simulations. For the hardware in the loop simulation (HIL), the tractor model was integrated with a 4s4m anti-lock braking system (ABS) and straight line braking tests were conducted. In developing the model, over 100 vehicle parameters were acquired from a real production tractor and entered into TruckSim. For the HIL simulation, the hardware consisted of a 4s4m ABS braking system with six brake chambers, four modulators, a treadle and an electronic control unit (ECU). A dSPACE simulator was used as the “interface” between the TruckSim computer model and the hardware.

Many studies have been performed to quantify the formation and evolution of roughness on ice shapes created in Appendix C icing conditions, which exhibits supercooled liquid droplets ranging from 1-50 µm. For example Anderson and Shin (1997), Anderson et al. (1998), and Shin (1994) represent early studies of ice roughness during short-duration icing events measured in the Icing Research Tunnel at the NASA Glenn Research Center. In the historical literature, image analysis techniques were employed to characterize the roughness. Using multiple images of the roughness elements, these studies of roughness focused on extracting parametric representations of ice roughness elements. While the image analysis approach enabled many insights into icing physics, recent improvements in laser scanning approaches have revolutionized the process of ice accretion shape characterization.

This study investigated the jackknife stability of Class VIII double tractor-trailer combination vehicles that had mixed braking configurations between the tractor and trailers and dolly (e.g. ECBS disc brakes on the tractor and pneumatic drum brakes on the trailers and dolly). Brake-in-turn maneuvers were performed with varying vehicle loads and surface conditions. Conditions with ABS ON for the entire vehicle (and select-high control algorithm on the trailers and dolly) found that instabilities (i.e. lane excursions and/or jackknifes) were exhibited under conditions when the surface friction coefficient was 0.3. It was demonstrated that these instabilities could be avoided while utilizing a select-low control algorithm on the trailers and dolly. Simulation results with the ABS OFF for the tractor showed that a tractor equipped with disc brakes had greater jackknife stability.

Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed.

During Phase VI of the National Highway Traffic Safety Administration's (NHTSA) Light Vehicle Rollover Research Program, three of the twenty-six light vehicles tested exhibited significant response asymmetries with respect to left versus right steer maneuvers. This paper investigates possible vehicle asymmetric characteristics and unintended inputs that may cause vehicle asymmetric response. An analysis of the field test data, results from suspension and steering parameter measurements, and a summary of a computer simulation study are also given.

A protocol has been developed that produced the type of lunar soil abrasion damage observed on Apollo spacesuits. This protocol was then applied to four materials (Kevlar®, Vectran®, Orthofabric, and Tyvek®) that are candidates for advanced spacesuits. Three of the four new candidate fabrics (all but Vectran®) were effective at keeping the dust from penetrating to layers beneath. In the cases of Kevlar® and Orthofabric this was accomplished by the addition of a silicone layer. In the case of Tyvek®, the paper structure was dense enough to block dust transport. The least abrasive damage was suffered by the Tyvek®. This was thought to be due in large part to its non-woven paper structure. The woven structures were all abraded where the top of the weave was struck by the abrasive. Of these, the Orthofabric suffered the least wear, with both Vectran® and Kevlar® suffering considerably more extensive filament breakage.

A research program was conducted to evaluate the effectiveness of icing tunnel hybrid model design. A hybrid design is where the full-scale leading edge of a wing section is maintained only to a certain percentage of the local chord, while the aft section of the model is redesigned into a shortened or truncated planform. An initial study was conducted in 2020 where the ice shape geometries on a full-chord length version of the swept CRM65 wing model were compared to those from the hybrid version of CRM65 that were obtained in the NASA Icing Research Tunnel in 2015. The results were reported in a 2021 paper. For most test conditions, the overall size and shape of the ice shapes compared well. However, the ice shapes from the full-chord model were generally slightly smaller than those from the hybrid model.

Artificial ice shapes of various geometric fidelity were tested on a wing model based on the Common Research Model. Low Reynolds number tests were conducted at Wichita State University’s Walter H. Beech Memorial Wind Tunnel utilizing an 8.9% scale model, and high Reynolds number tests were conducted at ONERA’s F1 wind tunnel utilizing a 13.3% scale model. Several identical geometrically-scaled ice shapes were tested at both facilities, and the results were compared at overlapping Reynolds and Mach numbers. This was to ensure that the results and trends observed at low Reynolds number could be applied and continued to high, near-flight Reynolds number. The data from Wichita State University and ONERA F1 agreed well at matched Reynolds and Mach numbers. The lift and pitching moment curves agreed very well for most configurations.

In-flight icing is an important consideration that affects aircraft design, performance, certification and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of additional ice accretion testing conducted in the NASA Icing Research Tunnel in January 2022 with a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small median volumetric diameter (MVD) = 25 μm cloud and a large MVD = 110 μm cloud. A series of tests were conducted over a range of total temperature from -23.8 °C to -1.4 °C with all other conditions held constant.

An existing tire model was investigated for additional normal load-dependent characteristics to improve the large truck simulations developed by the National Highway Traffic Safety Administration (NHTSA) for the National Advanced Driving Simulator (NADS). Of the existing tire model coefficients, plysteer, lateral friction decay, aligning torque stiffness and normalized longitudinal stiffness were investigated. The findings of the investigation led to improvements in the tire model. The improved model was then applied to TruckSim to compare with the TruckSim table lookup tire model and test data. Additionally, speed-dependent properties for the NADS tire model were investigated (using data from a light truck tire).

An experimental research effort was begun to develop a database of airplane aerodynamic characteristics with simulated ice accretion over a large range of incidence and sideslip angles. Wind-tunnel testing was performed at the NASA Langley 12-ft Low-Speed Wind Tunnel using a 3.5% scale model of the NASA Langley Generic Transport Model. Aerodynamic data were acquired from a six-component force and moment balance in static-model sweeps from α = -5 to 85 deg. and β = -45 to 45 deg. at a Reynolds number of 0.24x10⁶ and Mach number of 0.06. The 3.5% scale GTM was tested in both the clean configuration and with full-span artificial ice shapes attached to the leading edges of the wing, horizontal and vertical tail. Aerodynamic results for the clean airplane configuration compared favorably with similar experiments carried out on a 5.5% scale GTM.

This paper presents the development of a CarSim model of an All-Terrain Vehicle (ATV) that can be used to predict the handling and stability characteristic of the vehicle. The inertia and suspension characteristics of a subject ATV are measured and a model of the ATV is built in CarSim based on the measurements. A simplified suspension model is developed to convert the suspension compliance measurements into parameters suitable to a CarSim model. Procedures used to apply vehicle mass, inertia and suspension kinematics data in CarSim are also shown. The model is evaluated using predictions of vehicle response during a constant radius circle test. The simulation results of the maneuver are compared with the field test results shown in a recent CPSC report on ATV’s. Similar cornering characteristics are found in both results. Modifications are made to the model to study how changes to the ATV affect performance.

This paper discusses technologies and software tools that are being implemented in a software toolkit currently under development at NASA Glenn Research Center. Its purpose is to help study the effects of icing on airfoil performance and assist with the aerodynamic simulation process which consists of characterization and modeling of ice geometry, application of block topology and grid generation, and flow simulation. Tools and technologies for each task have been carefully chosen based on their contribution to the overall process. For the geometry characterization and modeling, we have chosen an interactive rather than automatic process in order to handle numerous ice shapes. An Appendix presents features of a software toolkit developed to support the interactive process. Approaches taken for the generation of block topology and grids, and flow simulation, though not yet implemented in the software, are discussed with reasons for why particular methods are chosen.

This paper presents experimental methods for investigating large droplet impingement dynamics and for obtaining small and large water droplet impingement data. Droplet impingement visualization experiments conducted in the Goodrich Icing Wind Tunnel with a 21-in chord NACA 0012 airfoil demonstrated considerable droplet splashing during impingement. The tests were performed for speeds in the range 50 to 175 mph and with cloud median volumetric diameters in the range of 11 to 270 microns. Extensive large droplet impingement tests were conducted at the NASA Glenn Icing Research Tunnel (IRT). Impingement data were obtained for a range of airfoil sections including three 36-inch chord airfoils (MS(1)-0317, GLC-305, and NACA 652-415), a 57-inch chord Twin Otter horizontal tail section and 22.5-minute and 45-minute LEWICE glaze ice shapes for the Twin Otter tail section. Small droplet impingement tests were also conducted for selected test models.

Transmission error has long been identified to be the main exciter of gear whine noise. This research effort seeks to investigate the mechanisms and principal controlling factors that affect the actual noise generation from a typical gearbox housing due to transmission error excitations. The insight gained is expected to help in identifying possible noise control procedures in typical gearing applications. The example gearbox of this paper is an aircraft auxiliary-drive idler gearbox run at low load so that transmission error is the primary mesh excitation. A limited set of dynamic noise and vibration data are collected in transient speed run-ups. A contact-mechanics gear-tooth model is used to predict the static transmission error at each mesh. A finite-element model of the shafting that incorporates complex shaft and bearing data is used to predict the shaft dynamics with the static transmission error at the gear mesh(es) as the sole excitation.