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Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

Assessing the dynamic performance of multilayer plates subjected to impulsive loading is of interest for identifying configurations that either absorb energy or transmit the energy in the transverse directions, thereby mitigating the through-thickness energy propagation. A reduced-order modeling approach is presented in this paper for rapidly evaluating the structural dynamic performance of various multilayer plate designs. The new approach is based on the reverberation matrix method (RMM) with the theory of generalized rays for fast analysis of the structural dynamic characteristics of multilayer plates. In the RMM model, the waves radiated from the dynamic load are reflected and refracted at each interface between layers, and the waves within each layer are transmitted with a phase lag. These two phenomena are represented by the global scattering matrix and the global phase matrix, respectively.

Non-conventional operating conditions and fuels in diesel engines can produce longer ignition delays compared to conventional diesel combustion. If those extended delays are longer than the injection duration, the ignition and combustion progress can be significantly influenced by the transient following the end of injection (EOI), and especially by the modification of the mixture field. The objective of this paper is to assess how those long ignition delays, obtained by injecting at low in-cylinder temperatures (e.g., 760-800K), are affected by EOI. Two multi-hole diesel fuel injectors with either six 0.20mm orifices or seven 0.14mm orifices have been used in a 2.34L single-cylinder optical diesel engine. We consider a range of ambient top dead center (TDC) temperatures at the start of injection from 760-1000K as well as a range of injection durations from 0.5ms to 3.1ms. Ignition delays are computed through the analysis of both cylinder pressure and chemiluminescence imaging.

Using the total probability theorem, we propose a method to calculate the failure rate of a linear vibratory system with random parameters excited by stationary Gaussian processes. The response of such a system is non-stationary because of the randomness of the input parameters. A space-filling design, such as optimal symmetric Latin hypercube sampling or maximin, is first used to sample the input parameter space. For each design point, the output process is stationary and Gaussian. We present two approaches to calculate the corresponding conditional probability of failure. A Kriging metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure and the failure rate of the dynamic system. The proposed method is demonstrated using a vibratory system.

Electronic controls in internal combustion engines require an in-cylinder combustion sensor to produce a feedback signal to the ECU (Engine Control Unit). Recent research indicated that the ion current sensor has many advantages over the pressure transducer, related mainly to lower cost. Modified glow plugs in diesel engines, and fuel injectors in both gasoline and diesel engines can be utilized as ion current sensors without the addition any part or drilling holes in the cylinder head needed for the pressure transducer. Multi sensing fuel injector (MSFI) system is a new technique which instruments the fuel injector with an electric circuit to perform multiple sensing tasks including functioning as an ion sensor in addition to its primary task of delivering the fuel into the cylinder. It is necessary to fundamentally understand MSFI system.

Many approaches have been taken to determine the burning velocity in internal combustion engines. Experimentally, the burning velocity has been determined in optically accessible gasoline engines by tracking the propagation of the flame front from the spark plug to the end of the combustion chamber. These experiments are costly as they require special imaging techniques and major modifications in the engine structure. Another approach to determine the burning velocity is from 3D CFD simulation models. These models require basic information about the mechanisms of combustion which are not available for distillate fuels in addition to many assumptions that have to be made to determine the burning velocity. Such models take long periods of computational time for execution and have to be calibrated and validated through experimentation.

Emissions of Unburned Hydrocarbons (UHC) from diesel engines are a particular concern during the starting process, when after-treatment devices are typically below optimal operating temperatures. Drivability in the subsequent warm-up phase is also impaired by large cyclic fluctuations in mean effective pressure (MEP). This paper discusses in-cylinder wall temperature influence on unburned hydrocarbon emissions and combustion stability during the starting and warm-up process in an optical engine. A laser-induced phosphorescence technique is used for quantitative measurements of in-cylinder wall temperatures just prior to start of injection (SOI), which are correlated to engine out UHC emission mole fractions and combustion phasing during starting sequences over a range of charge densities, at a fixed fueling rate. Squish zone cylinder wall temperature shows significant influence on engine out UHC emissions during the warm-up process.

On-board fuel identification is important to ensure engine safe operation, similar power output, fuel economy and emissions levels when different fuels are used. Real-time detection of physical and chemical properties of the fuel requires the development of identifying techniques based on a simple, non-intrusive sensor. The measured crankshaft speed signal is already available on series engine and can be utilized to estimate at least one of the essential combustion parameters such as peak pressure and its location, rate of cylinder pressure rise and start of combustion, which are an indicative of the ignition properties of the fuel. Using a dynamic model of the crankshaft numerous methods have been previously developed to identify the fuel type but all with limited applications in terms of number of cylinders and computational resources for real time control.

Surrogates for JP-8 have been developed in the high temperature gas phase environment of gas turbines. In diesel engines, the fuel is introduced in the liquid phase where volatility plays a major role in the formation of the combustible mixture and autoignition reactions that occur at relatively lower temperatures. In this paper, the role of volatility on the combustion of JP-8 and five different surrogate fuels was investigated in the constant volume combustion chamber of the Ignition Quality Tester (IQT). IQT is used to determine the derived cetane number (DCN) of diesel engine fuels according to ASTM D6890. The surrogate fuels were formulated such that their DCNs matched that of JP-8, but with different volatilities. Tests were conducted to investigate the effect of volatility on the autoignition and combustion characteristics of the surrogates using a detailed analysis of the rate of heat release immediately after the start of injection.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

The present investigation details an experimental procedure for frontal impact responses of a generic steel front bumper crush can (FBCC) assembly subjected to a rigid full and 40% offset impact. There is a paucity of studies focusing on component level tests with FBCCs, and of those, speeds carried out are of slower velocities. Predominant studies in literature pertain to full vehicle testing. Component level studies have importance as vehicles aim to decrease weight. As materials, such as carbon fiber or aluminum, are applied to vehicle structures, computer aided models are required to evaluate performance. A novel component level test procedure is valuable to aid in CAE correlation. All the tests were conducted using a sled-on-sled testing method. Several high-speed cameras, an IR (Infrared) thermal camera, and a number of accelerometers were utilized to study impact performance of the FBCC samples.

Previously, a 10-year-old (YO) pediatric thorax finite element model (FEM) was developed and verified against child chest stiffness data measured from clinical cardiopulmonary resuscitation (CPR). However, the CPR experiments were performed at relatively low speeds, with a maximum loading rate of 250 mm/s. Studies showed that the biomechanical responses of human thorax exhibited rate sensitive characteristics. As such, the studies of dynamic responses of the pediatric thorax FEM are needed. Experimental pediatric cadaver data in frontal pendulum impacts and diagonal belt dynamic loading tests were used for dynamic validation. Thoracic force-deflection curves between test and simulation were compared. Strains predicted by the FEM and the injuries observed in the cadaver tests were also compared for injury assessment and analysis. This study helped to further improve the 10 YO pediatric thorax FEM.

A test stand was developed to evaluate an 11.5 cc, two-stroke, internal combustion engine in anticipation of future combustion system modifications. Detailed engine testing and analysis often requires complex, specialized, and expensive equipment, which can be problematic for research budgets. This problem is compounded by the fact that testing “micro” engines involves low flow rates, high rotational speeds, and compact dimensions which demand high-accuracy, high-speed, and compact measurement systems. On a limited budget, the task of developing a micro-engine testing system for advanced development appears quite challenging, but with careful component selection it can be accomplished. The anticipated engine investigation includes performance testing, fuel system calibration, and combustion analysis. To complete this testing, a custom test system was developed.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. It also affects the scheduling for preventive maintenance. Reliability usually degrades with time increasing therefore, the lifecycle cost due to more frequent failures which result in increased warranty costs, costly repairs and loss of market share. In a lifecycle cost based design, we must account for product quality and preventive maintenance using time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. For a repairable system, preventive maintenance is scheduled to avoid failures, unnecessary production loss and safety violations. This article proposes a methodology to obtain the optimal scheduling for preventive maintenance using time-dependent reliability principles.

The U.S. Army currently uses JP-8 for global operations according to the "one fuel forward policy" that was enacted almost twenty years ago in order to help reduce the logistics burden of supplying a variety of fuels for given Department of Defense vehicle and base applications. One particular challenge with using global JP-8 is the lack of or too broad a range of specified combustion and fuel system affecting properties including ignition quality, high temperature viscosity, and lubricity. In addition to these challenges, the JP-8 fuel specification currently allows the use of blending with certain types of synthetic jet fuels up to 50% by volume. This blended fuel also doesn't include an ignition quality or high temperature viscosity specification, but does include a lubricity specification that is much less restrictive than DF-2.

Advanced valvetrain coupled with Direct Injection (DI) provides an opportunity to simultaneous reduction of fuel consumption and emissions. Because of their robustness and cost performance, multi-hole injectors are being adopted as gasoline DI fuel injectors. Ethanol and ethanol-gasoline blends synergistically improve the performance of a turbo-charged DI gasoline engine, especially in down-sized, down-sped and variable-valvetrain engine architecture. This paper presents Mie-scattering spray imaging results taken with an Optical Accessible Engine (OAE). OAE offers dynamic and realistic in-cylinder charge motion with direct imaging capability, and the interaction with the ethanol spray with the intake air is studied. Two types of cams which are designed for Early Intake Valve Close (EIVC) and Later Intake Valve Close (LIVC) are tested, and the effect of variable valve profile and deactivation of one of the intake valves are discussed.

It is well know that the internal flow field and nozzle geometry affected the spray behavior, but without high-speed microscopic visualization, it is difficult to characterize the spray structure in details. Single-hole diesel injectors have been used in fundamental spray research, while most direct-injection engines use multi-hole nozzle to tailor to the combustion chamber geometry. Recent engine trends also use smaller orifice and higher injection pressure. This paper discussed the quasi-steady near-nozzle diesel spray structures of an axisymmetric single-hole nozzle and a symmetric two-hole nozzle configuration, with a nominal nozzle size of 130 μm, and an attempt to correlate the observed structure to the internal flow structure using computational fluid dynamic (CFD) simulation. The test conditions include variation of injection pressure from 30 to 100 MPa, using both diesel and biodiesel fuels, under atmospheric condition.

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The integrated stochastic framework combines the computational physics-based predictions with experimental testing information for assessing vehicle reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size computational mechanics models and high-dimensional stochastic spaces, a HPC simulation-based approach to the reliability problem was implemented.

Internal combustion engine control requires feedback signals to the ECU in order to meet the increasingly stringent emissions standards. Reducing the number of on-board sensors needed for proper engine performance would reduce the cost and complexity of the electronic system. This paper presents a new technique to enable one engine element, the fuel injector, to perform multiple sensing tasks in addition to its primary task of delivering the fuel into the cylinder. The injector is instrumented within an electric circuit to produce a signal indicative of the ionization produced from the combustion process in electronically controlled diesel engines. The output of the multi sensing fuel injector (MSFI) system can be used as a feedback signal to the engine control unit (ECU) for injection timing and diagnostics of the injection and combustion processes.