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A Dual Power Split Electronic Continuously Variable Transmission (DPS-ECVT) with an input-split, output coupled, split-power-path configuration is proposed for improving overall system efficiency and range for electric vehicles. By modulating the power split ratio between the mechanical (planetary gear meshes) and electrical (Motor Generator Units) driveline components, a continuous range of gear ratios operating at higher efficiency is obtained. The proposed concept leverages two power-split units that lead to significantly reduced power flow through the electrical drivelines (compared with single speed EV transmissions as well as single power-split E-CVTs) while providing the same overall ratio spread for transmission operation.

Advanced Driver Assistance Systems (ADAS) like Lane Departure Warning (LDW) and Lane Centering Assistance (LCA) have experienced low customer acceptance and market penetration despite the technology being in active development for several years now. This trend is attributed to the inability of many of the perception systems to consistently detect lane markings and localize the vehicle on roads with poor lane markings, changing weather conditions, and occlusions. Currently, no standards or benchmarks are available to evaluate the quality of either the lane markings or the perception algorithms. This work seeks to establish a reference framework that can be used by transportation agencies to evaluate the effect of pavement markings on ADAS functions. Previous works have looked at these problems using on-road pavement markings. However, environmental factors like sun glare, shadows, and road illumination, affect Lane Detection (LD) performance with changing roads and driving directions.

Object detection and tracking is a central aspect of perception for autonomous vehicles. While there has been significant development in this field in recent years, many perception algorithms still struggle to provide reliable information in challenging weather conditions which include night-time, direct sunlight, glare, fog, etc. To achieve full autonomy, there is a need for a robust perception system capable of handling such challenging conditions. In this paper, we attempt to bridge this gap by proposing an algorithm that combines the strength of automotive radars and infra-red thermal cameras. We show that these sensors complement each other well and provide reliable data in poor visibility conditions. We demonstrate the advantages of a thermal camera over a visible-range camera in these situations and employ YOLOv3 for object detection.

Advanced Driver Assistance Systems (ADAS) like Lane Departure Warning (LDW) and Lane Keep Assist (LKA) have been available for several years now but has experienced low customer acceptance and market penetration. These deficiencies can be traced to the inability of many of the perception systems to consistently recognize lane markings and localize the vehicle with respect to the lane markings in the real-world with poor markings, changing weather conditions and occlusions. Currently, there is no available standard or benchmark to evaluate the quality of either the lane markings or the perception algorithms. This work seeks to establish a reference test system that could be used by transportation agencies to evaluate the quality of their markings to support ADAS functions that rely on pavement markings. The test system can also be used by designers as a benchmark for their proprietary systems.

As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis proposal presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses.

NASA and the FAA conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding of high ice water content (HIWC) and develop onboard weather radar processing techniques to detect regions of HIWC ahead of an aircraft to enable tactical avoidance of the potentially hazardous conditions. Both HIWC RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory equipped with a Honeywell RDR-4000 weather radar and in-situ microphysical instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results. The first campaign was conducted in August 2015 with a base of operations in Ft. Lauderdale, Florida.

High Ice Water Content (HIWC) has been identified as a primary causal factor in numerous engine events over the past two decades. Previous attempts to develop a remote detection process utilizing modern commercial radars have failed to produce reliable results. This paper discusses the reasons for previous failures and describes a new technique that has shown very encouraging accuracy and range performance without the need for any modifications to industry’s current radar design(s). The performance of this new process was evaluated during the joint NASA/FAA HIWC RADAR II Flight Campaign in August of 2018. Results from that evaluation are discussed, along with the potential for commercial application, and development of minimum operational performance standards for future radar products.

Recent studies have found that high mass concentrations of ice particles in regions of deep convective storms can adversely impact aircraft engine and air probe (e.g. pitot tube and air temperature) performance. Radar reflectivity in these regions suggests that they are safe for aircraft penetration, yet high ice water content (HIWC) is still encountered. The aviation weather community seeks additional remote sensing methods for delineating where ice particle (or crystal) icing conditions are likely to occur, including products derived from geostationary (GEO) satellite imagery that is now available in near-real time at increasingly high spatio-temporal detail from the global GEO satellite constellation.

This paper discusses different computer vision techniques investigated by the authors for identifying Emergency Vehicles (EV). Two independent EV identification frameworks were investigated: (1) A one-stage framework where an object detection algorithm is trained on a custom dataset to detect EVs, (2) A two-stage framework where an object classification algorithm is implemented in series with an object detection pipeline to classify vehicles into EVs and non-EVs. A comparative study is conducted for different multi-spectral feature vectors of the image, against several classification models implemented in framework 2. Additionally, a user-defined feature vector is defined and its performance is compared against the other feature vectors. Classification outputs from each of the frameworks are compared to the ground truth, and results are quantitatively listed to conclude upon the ideal decision rule.

This article presents strategies for evaluating the mean, variance, and failure probability of a response variable given measurements subject to both epistemic and aleatory uncertainties. We focus on a case in which standard sensor calibration techniques cannot be used to eliminate measurement error since the uncertainties affecting the metrology system depend upon the measurement itself (e.g., the sensor bias is not constant and the measurement noise is colored). To this end, we first characterize all possible realizations of the true response that might have led to each of such measurements. This process yields a surrogate of the data for the unobservable true response taking the form of a random variable. Each of these variables, called a Random Datum Model (RDM), is manufactured according to a measurement and to the underlying structure of the uncertainty.

Over the last couple decades, there has been a growing interest in incorporating commercial off-the-shelf (COTS) technologies and open standards in the design of human-rated spacecraft. This approach is intended to reduce development and upgrade costs, lower the need for new design work, eliminate reliance on individual suppliers, and minimize schedule risk. However, it has not traditionally been possible for COTS solutions to meet the high reliability and fault tolerance requirements of systems implementing critical spacecraft functions. Byzantine faults are considered particularly dangerous to such systems because of their ability to escape traditional means of fault containment and disrupt consensus between system components. In this paper, we discuss the design of a voting protocol using Time-Triggered Ethernet capable of achieving data integrity in the presence of a single Byzantine fault.

Rotordynamics developed from the beginning of the 20th century to deal with problems associated with steam turbines. This paper deals with intense developments starting around 1975 through 2000 in rotordynamics to deal with new, larger machines running at higher speeds and higher power levels. Most of the new problems of interest dealt with subsynchronous instabilities. Issues associated with “synchromnously unstable” motion due to the Morton Effect is also reviewed.

It is important for engineering firms to be able to develop forecasts of recommended courses of action based on available information. In particular, engineering firms must be able to assess the benefit of performing information-gathering actions. For example, an automobile manufacturer may use a computer simulation of a hydraulic motor and pump in the design of a new vehicle. The model may contain random variables that can be more accurately determined through expensive information-gathering actions, e.g., physical experiments, surveys, etc. To decide whether to perform these information-gathering actions, the automobile manufacturer must be able to quantify the expected value to the firm of conducting them. However, the cost of computing the expected value of information (through optimization, Monte Carlo sampling, etc.) grows exponentially with the amount of information that is to be gathered and can often exceed the cost of actually gathering the information.

The objective of this study is to measure high-frequency, short-duration, actual liquid fuel spray events using a simple photo detector and validate the results with high-speed camera measurements. This paper presents an optical approach for detecting bulk fuel injection's temporal characteristics, i.e. opening delay and duration times. A key component in the measurement system is a commercially available low-cost photo detector which is shown to be highly effective for detecting high frequency, short duration spray events. The paper provides an in-depth discussion of a photo detector based measurement system, a test fixture, and its validation. Test results with a two-stage pulse-width-modulation (PWM) current controlled approach are provided for various operation parameter settings. Its effectiveness is validated by comparing with the results obtained with a high-speed camera.

Future electronics and photonics systems, weapons systems, and environmental control systems in aircraft will require advanced thermal management technology to control the temperature of critical components. Two-phase Thermal Management Systems (TMS) are attractive because they are compact, lightweight, and efficient. However, maintaining stable and reliable cooling in a two-phase flow system presents unique design challenges, particularly for systems with parallel evaporators during thermal transients. Furthermore, preventing ingress of liquid into a vapor compressor during variable-gravity operation is critical for long-term reliability and life. To enable stable and reliable cooling, a highly stable two-phase system is being developed that can effectively suppress flow instability in a system with parallel evaporators. Flow stability is achieved by ensuring that only single-phase liquid enters the evaporators.

A major decision to make in design projects is the selection of the best technology to provide some needed system functionality. In making this decision, the designer must consider the range of technologies available and the performance of each. During the useful life of the product, the technologies composing the product evolve as research and development efforts continue. The performance evolution rate of one technology may be such that even though it is not initially a preferably technology, it becomes a superior technology after a few years. Quantifying the evolution of these technologies complicates the technology selection decision. The selection of energy storage technology in the design of an electric car is one example of a difficult decision involving evolving technologies.

Conventional spark plugs ignite a fuel-air mixture via an electric-to-plasma energy transfer; the effectiveness of which can be described by an electric-to-plasma energy efficiency. Although conventional spark plug electric-to-plasma efficiencies have historically been viewed as adequate, it might be wondered how an increase in such an efficiency might translate (if at all) to improvements in the flame initiation period and eventual engine performance of a spark-ignition engine. A modification can be made to the spark plug that places a peaking capacitor in the path of the electrical current; upon coil energizing, the stored energy in the peaking capacitor substantially increases the energy delivered by the spark. A previous study has observed an improvement in the electric-to-plasma energy efficiency to around 50%, whereas the same study observed conventional spark plug electric-to-plasma energy efficiency to remain around 1%.

Cloud properties retrieved from satellite data are used to diagnose aircraft icing threat in single layer and multilayered ice-over-liquid clouds. The algorithms are being applied in real time to the Geostationary Operational Environmental Satellite (GOES) data over the CONUS with multilayer data available over the eastern CONUS. METEOSAT data are also used to retrieve icing conditions over western Europe. The icing algorithm's methodology and validation are discussed along with future enhancements and plans. The icing risk product is available in image and digital formats on NASA Langley ‘s Cloud and Radiation Products web site, http://www-angler.larc.nasa.gov.

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.

OBD (On Board Diagnosis) has been applied to detect malfunctions in powertrains. OBD requirements have been extended to detect various failures for ensuring the vehicle emission control system being normal. That causes further costs for additional sensors and software works. Two layers diagnosis system is proposed for a passenger car gearbox system to detect changes from normal behavior. Conventional physical constraints based diagnosis is placed on the base layer. Model based diagnosis and specific symptom finding diagnosis are built on the second layer. Conventional physical constraints based diagnosis is direct and effective way to detect the failure of system if the detected signals exceed their normal ranges. However under the case of system failure with related signals still remain in normal ranges, the conventional detection measures can not work normally. Under this case, Model based diagnosis is proposed to enhance the functionality of diagnosis system.