The effects of three kinds of oxygenated fuel blends—i.e., ethanol-gasoline, n-butanol-gasoline, and 2,5-dimethylfuran (DMF)-gasoline-on fuel consumption, emissions, and acceleration performance were investigated in a passenger car with a chassis dynamometer. The engine mounted in the vehicle was a four-cylinder, four-stroke, turbocharging gasoline direct injection (GDI) engine with a displacement of 1.395 L. The test fuels include ethanol-gasoline, n-butanol-gasoline, and DMF-gasoline with four blending ratios of 20%, 50%, 75%, and 100%, and pure gasoline was also tested for comparison. The original contribution of this article is to systemically study the steady-state, transient-state, cold-start, and acceleration performance of the tested fuels under a wide range of blending ratios, especially at high blending ratios. It provides new insight and knowledge of the emission alleviation technique in terms of tailoring the biofuels in GDI turbocharged engines. The results of our works showed that operation with ethanol–gasoline, n-butanol–gasoline, and DMF–gasoline at high blending ratios could be realized in the GDI vehicle without any modification to its engine and the control system at the steady state. At steady-state operation, as compared with pure gasoline, the results indicated that blending n-butanol could reduce CO₂, CO, total hydrocarbon (THC), and NO_X emissions, which were also decreased by employing a higher blending ratio of n-butanol. However, a high fraction of n-butanol increased the volumetric fuel consumption, and so did the DMF–gasoline and ethanol–gasoline blends. A large fraction of DMF reduced THC emissions, but increased CO₂ and NO_X emissions. Blending n-butanol can improve the equivalent fuel consumption. Moreover, the particle number (PN) emissions were significantly decreased when using the high blending ratios of the three kinds of oxygenated fuels. According to the results of the New European Drive Cycle (NEDC) cycle, blending 20% of n-butanol with gasoline decreased CO₂ emissions by 5.7% compared with pure gasoline and simultaneously reduced CO, THC, NO_X emissions, while blending ethanol only reduced NO_X emissions. PN and particulate matter (PM) emissions decreased significantly in all stages of the NEDC cycle with the oxygenated fuel blends; the highest reduction ratio in PN was 72.87% upon blending 20% ethanol at the NEDC cycle. The high proportion of n-butanol and DMF improved the acceleration performance of the vehicle.

Inferring local soot temperature and volume fraction distributions from radiation emission measurements of sooting flames may involve solving nonlinear, ill-posed and high-dimensional problems, which are typically conducted by solving ill-posed problems with big matrices with regularization methods. Due to the high data throughput, they are usually inefficient and tedious. Machine learning approaches allow solving such problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present an original and efficient machine learning approach for retrieving soot temperature and volume fraction fields simultaneously from single-color near-infrared emission measurements of dilute ethylene diffusion flames. The machine learning model gathers information from existing data and builds connections between combustion scalars (soot temperature and volume fraction) and emission measurements of flames. Numerical studies were conducted first to show the feasibility and robustness of the method. The experimental Multi-Layer Perceptron (MLP) neural network model was fostered and validated by the N₂ diluted ethylene diffusion flames. Furthermore, the model capability tests were carried out as well for CO₂ diluted ethylene diffusion flames. Eventually, the model performance subjected to the Modulated Absorption/Emission (MAE) technique measurement uncertainties were detailed.

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique that can obtain a three-dimensional ( 2 D + λ ) data cube of the target scene within a single exposure. Previous studies of CTIS suggest that reconstructions usually suffer from severe artifacts due to the limited number of projections available. To overcome this limitation, an iterative algorithm combining superiorization and guided image filtering is proposed to explore the intrinsic properties of the hyperspectral data cube as well as the characteristics of zero-order diffraction for the first time, to the best of our knowledge. Results from both simulative studies and proof-of-concept experiments demonstrate its superiority in suppressing artifacts and improving precision over the frequently used expectation maximization algorithm.