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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

In this paper a Multi-Level System (MLS) optimization algorithm is presented and utilized for the multi-discipline design of a ground vehicle track. The MLS can guide the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources to the disciplines which exhibit the higher level of improvement. The track analysis demonstrates how a multi-discipline design approach can be pursued in ground vehicle applications.

This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

This paper discusses a new Department of Defense (DoD) initiative focused on the development of new generation of MBS computer software that have capabilities and features that are not provided by existing MBS software technology. This three-decade old technology fails to meet new challenges of developing more detailed models in which the effects of significant changes in geometry and large deformations cannot be ignored. New applications require accurate continuum mechanics based vehicle/soil interaction models, belt and chain drive models, efficient and accurate continuum based tire models, cable models used in rescue missions, models that accurately capture large deformations due to thermal and excessive loads, more accurate bio-mechanics models for ligaments, muscles, and soft tissues (LMST), etc.

A mathematical model is presented for analysis and optimization of the adsorbents in the multifiltration beds contained in the International Space Station (ISS) water processor. The model consists of a physical properties database, an equilibrium description for single and multicomponent adsorption, and a kinetic description for adsorption beds in the water processor. The model is verified on a surrogate mixture designed to mimic the adsorption potential of the ISS shower/handwash waste stream.

A mathematical model is presented for analysis and optimization of the ion exchange beds in the International Space Station (ISS) Water Processor. The model consists of a physical properties database, an equilibrium description for binary and multicomponent ion exchange, and a kinetic description for ion exchange beds in the Water Processor. The ion exchange model will be verified for an Ersatz water designed to mimic the ISS shower/handwash waste stream.

The formulation of mathematical model for the computational simulation of transient temperature response and phase change of refrigerant in a vapor condenser of an automotive air conditioning unit is described. A demonstrative computational simulation of a sample air cooled vapor condenser charged with Freon 12 is presented. The computational analysis predicts an initial surge and followed by an oscillation of the condensate outflow rate from the condenser when the air-conditioning unit is started, and the tube length required for complete condensation of inflow vapor is a maximum value at start up. The rise of the temperatures of the condenser tubes and cooling air flow during the start-up and load change operations rate found to be gradual but the scale of these temperature changes are considered small.

The purpose of this study was to investigate the pressure drop and regeneration characteristics of a silicon carbide (SiC) wall-flow diesel particulate filter. The performance of a 25 μm mean pore size SiC dual trap system (DTS) consisting of two 12 liter traps connected in parallel in conjunction with a copper (Cu) fuel additive was evaluated. A comparison between the 25 μm DTS and a 15 μm DTS was performed, in order to show the effect of trap material mean pore size on trap loading and regeneration behavior. A 1988 Cummins LTA 10-300 diesel engine was used to evaluate the performance of the 15 and 25 μm DTS. A mathematical model was developed to better understand the thermal and catalytic oxidation of the particulate matter. For all the trap steady-state loading tests, the engine was run at EPA mode 11 for 10 hours. Raw exhaust samples were taken upstream and downstream of the trap system in order to determine the DTS filtration efficiency.

This paper examines the feasibility of using eigenvalue modification as the effective algorithm for interactive modal analysis. The dynamic characteristics of a cantilever beam are determined experimentally and compared to the results of an analytical study. The model is then modified with the addition of mass and stiffness. The results predicted using eigenvalue modification theory on the analytical model compare very favorably with experimentally determined results.

A study was undertaken to investigate the affect of exhaust gas recirculation (EGR) on engine wear and lubricating oil degradation in a heavy duty diesel engine using a newly developed methodology that uses analytical ferrography in conjunction with short term tests. Laboratory engine testing was carried out on a Cummins NTC-300 Big Cam II diesel engine at rated speed (1800 RPM) and 75% rated load with EGR rates of 0, 5, and 15% using a SAE 15W40 CD/SF/EO-K oil. Dynamometer engine testing involved collecting oil samples from the engine sump at specified time intervals through each engine test. These oil samples were analyzed using a number of different oil analysis techniques that provide information on the metal wear debris and also on the lubricating oil properties. The results from these oil analysis techniques are the basis of determining the effect of EGR on engine wear and lubricating oil degradation, rather than an actual engine tear down between engine tests.

This paper details the computer algorithm which was developed to determine the A/C refrigeration circuit balance point under the system transient operating conditions. The A/C circuit model consisting of major component submodels, such as the evaporator, compressor, condenser, orifice, air handling system, and connecting hoses, are included in the study. Pressure drop and thermal capacity for the evaporator, condenser, and connecting ducts/hoses are also considered in the simulation. The results obtained from the simulation model are in good agreement with the experimental data. Users can take advantage of this CAE tool to optimize the A/C system design and to minimize the development process with time-saving and cost-effective perspectives.

A Computer-Aided Engineering (CAE) model for automobile climate control system is presented to provide engineers with an cost effective analysis tool for designing, developing, and optimizing the vehicle interior climate. It is the objective of this paper to develop a mathematical model which predicts the lumped temperature and lumped humidity variations inside the passenger compartment under design and operating conditions. The transient nature of the passenger cabin temperature, average interior mass temperature, and humidity are modeled using three coupled non-linear ordinary differential equations based on mass and energy balances. These equations are then solved by a fourth-order Runge-Kutta method with adaptive step size control.

Recent developments in time-dependent reliability have introduced the concept of a composite limit state. The composite limit state method can be used to calculate the time-dependent probability of failure for dynamic systems with limit-state functions of input random variables, input random processes and explicit in time. The probability of failure can be calculated exactly using the composite limit state if the instantaneous limit states are linear, forming an open or close polytope, and are functions of only two random variables. In this work, the restriction on the number of random variables is lifted. The proposed algorithm is accurate and efficient for linear instantaneous limit state functions of any number of random variables. An example on the design of a hydrokinetic turbine blade under time-dependent river flow load demonstrates the accuracy of the proposed general composite limit state approach.