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Modern air vehicles face increasing internal heat loads that must be appropriately understood in design and managed in operation. This paper examines one solution to creating more efficient and effective thermal management systems (TMSs): vapor cycle systems (VCSs). VCSs are increasingly being investigated by aerospace government and industry as a means to provide much greater efficiency in moving thermal energy from one physical location to another. In this work, we develop the AFRL (Air Force Research Laboratory) Transient Thermal Modeling and Optimization (ATTMO) toolbox: a modeling and simulation tool based in Matlab/Simulink that is suitable for understanding, predicting, and designing a VCS. The ATTMO toolbox also provides capability for understanding the VCS as part of a larger air vehicle system. The toolbox is presented in a modular fashion whereby the individual components are presented along with the framework for interconnecting them.

A global, systems-level model which characterizes the thermal behavior of internal combustion engines is described in this paper. Based on resistor-capacitor thermal networks, either steady-state or transient thermal simulations can be performed. A two-zone, quasi-dimensional spark-ignition engine simulation is used to determine in-cylinder gas temperature and convection coefficients. Engine heat fluxes and component temperatures can subsequently be predicted from specification of general engine dimensions, materials, and operating conditions. Emphasis has been placed on minimizing the number of model inputs and keeping them as simple as possible to make the model practical and useful as an early design tool. The success of the global model depends on properly scaling the general engine inputs to accurately model engine heat flow paths across families of engine designs. The development and validation of suitable, scalable submodels is described in detail in this paper.

The motivation of the present work was to understand the mechanism by which alcohols produce less aromatic species in their combustion process than an equal amount of hydrocarbon with similar molecular structure does. Due to its numerous advantages over short-chain alcohols, butanol has been considered very promising in soot reduction. Excluding the influence of spray, vaporization and mixing process in engine cases, an adiabatic constant-pressure reactor model was applied to investigate the effect of butanol additives on aromatic species, which are known to be soot precursors, in fuel-rich butane flames. To keep the carbon flux constant, 5% and 10% oxygen by mass of the fuel were added to butane using butanol additive, respectively. Based on the soot reduction effects proposed in literature, effects on temperature, key radical concentrations and the carbon removal from the pathway to aromatic species were considered to identify the major mechanism of reduction in aromatic species.

Butanol, which is a renewable biofuel, has been regarded as a promising alternative fuel for internal combustion engines. When blended with diesel and applied to pilot ignited natural gas engines, butanol has the capability to achieve lower emissions without sacrifice on thermal efficiency. However, high blend ratio of butanol is limited by its longer ignition delay caused by the higher latent heat and higher octane number, which restricts the improvement of emission characteristics. In this paper, the potential of increasing butanol blend ratio by adding hot exhaust gas recirculation (EGR) is investigated. 3D CFD model based on a detailed kinetic mechanism was built and validated by experimental results of natural gas engine ignited by diesel/butanol blends. The effects of hot EGR is then revealed by the simulation results of the combustion process, heat release traces and also the emissions under different diesel/butanol blend ratios.

With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. To seek for an optimized volumetric ratio for ABE-diesel blends, the previous work in our team has experimentally investigated and analyzed the combustion features of ABE-diesel blends with different volumetric ratio (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %) in a constant volume chamber. It was found that an increased amount of acetone would lead to a significant advancement of combustion phasing whereas butanol would compensate the advancing effect. Both spray dynamic and chemistry reaction dynamic are of great importance in explaining the unique combustion characteristic of ABE-diesel blend. In this study, a semi-detailed chemical mechanism is constructed and used to model ABE-diesel spray combustion in a constant volume chamber.

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.

We are developing a flexible and general methodology for real-time monitoring of acoustic emissions in machining applications. The goal of this work is to develop an approach to in-process monitoring which allows continuous assessment of tool wear and early warning of process exceptions. The nature of metal removal processes creates short-lived vibrations that carry information about the condition of the cutting tool and quality of cut. We wish to extract and represent these transient events without loss of important spectral structure. Other challenges include the need for system training data selection in the absence of expert labeled data, the modeling of short-term time evolution, and efficient real-time operation on an inexpensive computing platform.

The paper presents a semi-empirical model to predict refrigerant and lubricant inventory in both evaporator and condenser of an automotive air conditioning (MAC) system. In the model, heat exchanger is discretized into small volumes. Temperature, pressure and mass inventory are calculated by applying heat transfer, pressure drop and void fraction correlations to these volumes respectively. Refrigerant and lubricant are treated as a zeotropic mixture with a temperature glide. As refrigerant evaporates or condenses, thermophysical properties are evaluated accordingly with the change of lubricant concentration. Experimental data is used to validate the model. As a result, refrigerant and lubricant mass is predicted within 20% in the evaporator. However, in the condenser, lubricant mass was consistently under-predicted while refrigerant mass was predicted within 15% error. Moreover, the lubricant under-prediction becomes more significant at higher Oil Circulation Ratio (OCR).

The modeling of fuel jet atomization is key in the characterization of Internal Combustion (IC) engines, and 3D Computational Fluid Dynamics (CFD) is a recognized tool to provide insights for design and control purposes. Multi-hole injectors with counter-bored nozzle are the standard for Gasoline Direct Injection (GDI) applications and the Spray-G injector from the Engine Combustion Network (ECN) is considered the reference for numerical studies, thanks to the availability of extensive experimental data. In this work, the behavior of the Spray-G injector is simulated in a constant volume chamber, ranging from sub-cooled (nominal G) to flashing conditions (G2), validating the models on Diffused Back Illumination and Phase Doppler Anemometry data collected in vaporizing inert conditions.

In applications ranging from design of customized vehicle interiors to virtual testing of biomedical devices, the processes of modeling, design and analysis involve the simultaneous treatment of artifacts (i.e., parts designed by humans) and anatomical structures. An inherent conflict arises because the geometric descriptions are completely different. Artifact descriptions are typically the output of computer-aided design (CAD) software and consist of a collection of parametric patches that comprise the boundary of the artifact. In stark contrast, the native description of an anatomical structure typically consists of an image stack obtained using a volumetric scanning technology such as computed tomography (CT) or magnetic resonance imaging (MRI). Current practice for simultaneously dealing with both categories of entities involves working primarily in the world of CAD.

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material.

Whole-body vibration (WBV) is associated with several adverse health and safety outcomes including low-back pain (LBP) and driver fatigue. The objective of this study was to evaluate the efficacy of three commercially-available air-suspension truck seats for reducing truck drivers’ exposures to WBV. Seventeen truck drivers operating over a standardized route were recruited for this study and three commercially-available air suspension seats were evaluated. The predominant, z-axis average weighted vibration (Aw) and Vibration Dose Values (VDV) were calculated and normalized to represent eight hours of truck operation. In addition, the Seat Effective Amplitude Transmissibility (SEAT), the ratio of the seat-measured vibration divided by the floor-measured vibration, was compared across the three seats. One seat had significantly higher on-road WBV exposures whereas there were no differences across seats in off-road WBV exposures.

Two-phase ejectors are devices capable of recovering the expansion power that is lost by the throttling process in air conditioning cycles, resulting in improved system performance. High-pressure fluids such as CO2 have received the majority of attention in two-phase ejector studies in recent years due to the fluid's high throttling loss and high potential for improvement. However, low-pressure working fluids such as R134a, commonly used in automotive applications, have received considerably less attention owing to their lower recovery potential. While the two fluids have very different properties, both offer the potential for noticeable COP improvement with ejector cycles. Thus, understanding the operation and performance of ejectors with both fluids can be important to the design of ejector air conditioning cycles. This paper compares available experimental data for the performance of two-phase ejectors using CO2 and R134a.

Measuring the displacements in vehicle seat suspensions and the displacements the seat has to absorb may assist vehicle seat designers in better designing seats to absorb vibrations. Low frequency seat displacement is important in seat design to identify end-stop events and higher frequency shorter displacements are also important since seat components can be optimized to absorb these smaller displacements. Displacements can be directly measured with special instruments, but it would be less complicated if simple, compact accelerometers could be used to measure the seat displacements. This paper compares accelerometer-derived displacement measurements to known displacements derived from sinusoidal physics and field measured random displacements measured with potentiometers. Using known, controlled sinusoidal displacements, three lab-based experiments were conducted to determine how well accelerometers, using double integration, could measure displacements.

This paper reports a meta analysis of all studies located in the literature that have compared head up versus head down display of equivalent information, as these displays support both tracking (e.g., flight path control) and discrete event detection. The data clearly indicate a HUD advantage for most tasks, except tracking during cruise flight and event detection during final approach. The latter HUD cost however is observed only when events to be detected are entirely unexpected, reflecting a form of cognitive tunneling. The meta-analysis also reveals an advantage for conformal over non-conformal HUD imagery.

Ejector as a work recovery device offers potential for developing energy efficient heating and cooling systems based on vapor compression technology. For applications like automobile air conditioning, the operating conditions vary significantly which can lead to considerable performance degradation when the system is operated in off-design conditions. Therefore, system designing warrants development of accurate ejector performance models for a wide range of operating conditions. In this paper, a novel methodology for ejector performance maps is proposed using ejector efficiency as performance parameter and volumetric entrainment ratio as characterization parameter. The proposed performance map is developed after conducting experiments to find appropriate performance representation where ejector driven flow can be characterized using ejector motive flow. The developed performance map can predict ejector pressure lift within an accuracy of 20% using an iterative solver.

EcoCAR 2: Plugging in to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 30 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The EcoCAR 2 VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

A new Aircraft Accident Awareness Program (AAAP) was developed, evaluated, and is available to emergency response service provider organizations (firefighters, emergency medical technicians, trauma center personnel, law enforcement, clergy, coroners, and media) who would be called to an aircraft accident scene. Aircraft accident responder training is a critical factor in accident victim crash survivability and successful life-safety outcomes. This program was designed to teach participants about the unique conditions and safety hazards associated with aircraft crashes. A blend of academic classroom investigation, exposure to airworthy/ unairworthy aircraft including operating systems and components, computer accident simulations, “hands-on” (destructive) extrication protocol training, and participation in simulated in-the-field accident scenarios was used as an instructional delivery model.

Using neural networks, an algorithm has been developed to steer a wheel loader vehicle. Mathematical functions have been used in the past in an attempt to model a human in their operation of many types of vehicles. Since such functions can typically only be derived for situations in which the problem domain is thoroughly understood, research continues in an effort to develop a complete “operator model”. Neural Network algorithms were utilized in an attempt to determine the feasibility of accurately modeling the operator of a wheel loader construction vehicle. These algorithms were also used to determine how the control of different vehicle functions might be automated on a wheel loader.

This research compares the responses of a vehicle modeled in the 3D vehicle simulation program SIMON in the HVE simulation operating system against instrumented responses of a 3-axle tractor, 2-axle semi-trailer combination. The instrumented tests were previously described in SAE 2001-01-0139 and SAE 2003-01-1324 as part of a continuous research effort in the area of vehicle dynamics undertaken at the Vehicle Research and Test Center (VRTC). The vehicle inertial and mechanical parameters were measured at the University of Michigan Transportation Research Institute (UMTRI). The tire data was provided by Smithers Scientific Services, Inc. and UMTRI. The series of tests discussed herein compares the modeled and instrumented vehicle responses during quasi-steady state, steady state and transient handling maneuvers, producing lateral accelerations ranging nominally from 0.05 to 0.5 G's.