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Cheng, Zhen

Publications and source records attributed to Cheng, Zhen.

High-Fidelity Energy Deposition Ignition Model Coupled with Flame Propagation Models at Engine-like Flow Conditions

With the heightened pressure on car manufacturers to increase the efficiency and reduce the carbon emissions of their fleets, more challenging engine operation has become a viable option. Highly dilute, boosted, and stratified charge, among others, promise engine efficiency gains and emissions reductions. At such demanding engine conditions, the spark-ignition process is a key factor for the flame initiation propagation and the combustion event. From a computational standpoint, there exist multiple spark-ignition models that perform well under conventional conditions but are not truly predictive under strenuous engine operation modes, where the underlying physics needs to be expanded. In this paper, a hybrid Lagrangian-Eulerian spark-ignition (LESI) model is coupled with different turbulence models, grid sizes, and combustion models. The ignition model, previously developed, relies on coupling Eulerian energy deposition with a Lagrangian particle evolution of the spark channel, at every time-step. The spark channel is attached to the electrodes and allowed to elongate at a speed derived from the flow velocity. The LESI model is used to simulate spark ignition in a nonquiescent crossflow environment at engine-like conditions, using converge commercial computational fluid dynamics (CFD) solver. The results highlight the consistency, robustness, and versatility of the model in a range of engine-like setups, from typical with Reynolds-averaged Navier-Stokes (RANS) and a larger grid size to high fidelity with large-eddy simulation (LES) and a finer grid size. The flame kernel growth is then evaluated against Schlieren images from an optical constant volume ignition chamber with a focus on the performance of flame propagation models, such as G-equation and thickened flame model, versus the baseline well-stirred reactor model. Finally, future development details are discussed.

Advanced ignition modeling↗

Molecular Imaging of Matrix Metalloproteinase-2 in Atherosclerosis Using a Smart Multifunctional PET/MRI Nanoparticle

Matrix metalloproteinases from macrophages are important intraplaque components that play pivotal roles in plaque progression and regression. This study sought to develop a novel multifunctional positron emission tomography (PET) and magnetic resonance imaging (MRI) contrast agents based on MMP-2 cleavable nanoparticles to noninvasive assessment of MMP-2 activity in mouse carotid atherosclerotic plaques. Macrophage-rich vascular lesions were induced by carotid ligation plus high-fat diet and streptozotocin-induced diabetes in CL57/BL6 mice. To render iron oxide nanoparticles (IONP) specific for the extracellular MMP-2, the magnetic nanoparticle base material has been derivatized with 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) for the nuclear tracer 64 Cu labeling and the MMP-2-cleavable peptide modified with polyethylene glycol 2000, yielding a multi-modality reporter ( 64 Cu-NOTA-IONP@MMP2c-PEG2K, MMP2cNPs) for PET/MR imaging. Small animal PET imaging and biodistribution data revealed that MMP2cNPs exhibited remarkable plaque uptake (3.06 ± 0.87% ID/g and 1.83 ± 0.28% ID/g at 4 and 12 h, respectively). And MMP2cNPs were rapidly cleared from the contralateral normal carotid artery, resulting in excellent plaque-to-normal carotid artery contrasts. Furthermore, in vivo MRI showed a preferential accumulation of MMP2cNPs in atherosclerotic lesions compared with the non-cleavable reference compound, MMP2ncNPs. In addition, histological analyses revealed iron accumulations in the carotid atherosclerotic plaque, in colocalization with MMP-2 expression and macrophages. Using a combination of innovative imaging modalities, in this study, we demonstrate the feasibility of applying the novel smart MMP2cNPs as a PET/MR hybrid imaging contrast agent for detection of MMP-2 in atherosclerotic plaque in vivo.

60 APPLIED LIFE SCIENCES↗

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO 2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

Topological supramolecular network enabled high-conductivity, stretchable organic bioelectronics

Intrinsically stretchable bioelectronic devices based on soft and conducting organic materials have been regarded as the ideal interface for seamless and biocompatible integration with the human body. A remaining challenge is to combine high mechanical robustness with good electrical conduction, especially when patterned at small feature sizes. We develop a molecular engineering strategy based on a topological supramolecular network, which allows for the decoupling of competing effects from multiple molecular building blocks to meet complex requirements. We obtained simultaneously high conductivity and crack-onset strain in a physiological environment, with direct photopatternability down to the cellular scale. We further collected stable electromyography signals on soft and malleable octopus and performed localized neuromodulation down to single-nucleus precision for controlling organ-specific activities through the delicate brainstem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗