In support of efforts to develop improved models of turbulent spray behavior and combustion in diesel engines, experimental data and analysis must be obtained for guidance and validation. For RANS-based CFD modeling approaches, representative ensemble average experimental results are important. For high-fidelity models such as LES-based CFD simulations, precise individual experimental results are desirable. However, making comparisons between a given experiment and LES simulation is a challenge since local parameters cannot be directly compared. In this work, an optically accessible constant pressure flow rig (CPFR) is utilized to acquire diesel-like fuel injection and reaction behavior simultaneously with three optical diagnostic techniques: rainbow schlieren deflectometry (RSD), OH* chemiluminescence (OH*), and two-color pyrometry (2CP). The CPFR allows a large number of repeated injection experiments to be performed for statistical analysis and convergence using ensemble-averaging techniques, while maintaining highly repeatable test conditions. Even for stable test conditions, variations in local turbulent fuel-air mixing introduce variability which manifests as significant differences in OH* and 2CP results. Experimental measurements of characteristic parameters including liquid and vapor jet penetration, lift-off length, soot temperature and concentration, and turbulent flame speed, along with the shot-to-shot variability of each data set, are presented and discussed. A statistical method is utilized to analyze the extent of this variability, and to identify superlative injections within the data set for discussion and analysis of shot-to-shot variations.

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Many modern electro-optical systems incorporate multiple electro-optical sensors, each having unique wavebands, alignment, and distortion. Traditional laboratory testing requires multiple measurement setups for metrics like inter-channel sensor alignment, near/far focus performance, color accuracy, etc. In this study, a calibrated scene is developed for objective measurements of multiple electro-optical cameras from a single mounting position. This scene uses multiple targets (size and shape), multiple flat fields (blackbodies and spectralon panels), and temporal sources. Some targets work well in both the emissive and reflective bands, allowing for accuracy relative distortion to be measured. Specific attention was given to testing in the presence of scene-based algorithms such as auto-gain/level/exposure, where bright and dark objects are used to drive dynamic range. This approach allows for various measurements to be taken simultaneously and efficiently.

Cyclic variations in conventional diesel combustion engines can lead to large differences in engine out emissions even at steady operation. This study uses an optically accessible constant-pressure flow chamber to acquire fuel injections in quick succession to analyze mixing, auto-ignition, and combustion of diesel-surrogate n-heptane using multiple high-speed optical diagnostics. Prior studies have utilized fewer injections and/or they rely on analysis of ensemble average behavior. These approaches do not yield information on injection-to-injection variation or provide confidence in utilizing individual injection measurements for high-fidelity computational fluid dynamics(CFD) model validation. In this study, a large set of 500 injections is used to obtain global parameters including liquid length, vapor penetration length, ignition delay time, and lift-off length. Results for multiple injections are presented to illustrate large injection to injection variations. Potential sources for these variations are analyzed to conclude localized, small scale turbulence and rate of injection variations as the likely sources. Then, a statistical method based on z-scores is proposed and implemented to identify instantaneous injections that best represent the bulk data-set of jet boundaries measured independently by three different diagnostics. This synthesis of statistics-guided screening of data set and ensemble-average analysis offers higher confidence for CFD model validation relying upon both a representative single and average injection results.

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