Zhiwei Sun

This study investigates the combined effects of particle-to-fluid density ratio (ρr) and fluid Froude number (Fr) on particle–fluid interactions in horizontal pipe flow, where gravity acts transverse to the mean flow direction. Simulations were conducted for three particle-to-fluid density ratios, ρr=1000, 2500, and 8000, corresponding to low [e.g., poly-methyl methacrylate (PMMA)/plastic], intermediate (e.g., glass beads), and high-density (e.g., copper or heavy metal dust) particle regimes in air, respectively, in three distinct gravity regimes: microgravity (negligible gravity influence), terrestrial gravity (moderate gravity influence), and enhanced gravity (high gravity influence), with a constant mass loading (characterizing the particle-to-fluid mass flux ratio) of Φm=0.4 and a constant particle diameter, dp =40 μm. For the fixed particle size, variations in particle density result in a Stokes number (Stk) range from 11 to 88, representing the ratio of particle to fluid response times. A four-way coupling regime was used, and the coefficient of restitution was set to 0.97 for the interaction of particle–particle and particle–wall. For Fr=17, increasing ρr reduces the asymmetry in the fluid velocity profile about the centerline, as well as a decrease in secondary flow motion due to higher Stokes and fluid Froude numbers, with the highest secondary flow magnitude observed for the lowest density ratio, decreasing as the density ratio increases. A similar trend is observed for the particle velocity. In addition to Fr, the particle Froude number (Frp) is also used, representing the balance between bulk fluid velocity and particle settling velocity, thereby providing a complementary measure of particle response to gravity and its role in secondary flow motions. At higher fluid Froude numbers (Fr=68 and ∞), inertial effects dominate over gravity, and the fluid and particle velocity profiles show negligible variation with ρr. One of the key findings of the study shows that as the fluid Froude number reaches, Fr=68 and above, the flow-related characteristics such as fluid velocity, particle velocity, and turbulent kinetic energy are independent of the density ratio, and they are solely governed by inertia rather than gravity.

This seminar session critically examines methane pyrolysis as a promising pathway for hydrogen production from natural gas, emphasizing its potential to decarbonize energy systems while generating valuable solid carbon co-products. The discussion highlights dry methane splitting technologies that dissociate methane into hydrogen and solid carbon without CO2 emissions, contrasting with conventional combustion processes. Key approaches include catalytic and thermal methods, such as Carbotopia’s low-temperature catalytic vapor deposition and 1414 Degrees’ dual-column reactor integrating renewable thermal energy storage with molten catalysts. Experimental data demonstrate methane conversions up to 70% at temperatures around 1100°C, producing hydrogen-rich gas streams suitable for blending into existing natural gas infrastructure, enabling partial decarbonization without extensive retrofitting. The solid carbon byproduct, characterized by diverse morphologies and high surface area, offers applications in soil enhancement, battery anodes, and advanced materials, contributing to circular economy goals. Challenges identified encompass political and market barriers, knowledge gaps, carbon handling complexities, and the need for standardized carbon accounting frameworks, with ongoing efforts to develop ISO standards for emissions calculation. The integration of methane pyrolysis with renewable electricity and existing gas networks presents a scalable, cost-competitive alternative to water electrolysis, particularly for hard-to-electrify sectors such as heavy industry. The session underscores the importance of collaborative strategies to overcome institutional resistance and accelerate technology readiness, aiming for commercial deployment by 2030. Overall, methane pyrolysis emerges as a viable component of diversified hydrogen economies, aligning with multiple sustainable development goals and offering pathways for negative emissions through industrial carbon removals. Welcome from the Chair Carbon Reserves for a Resilient Union: The CRM Edge of Methane Splitting Merits & Challenges in Methane Decarbonization by different pathways of physical CCU Renewable-Integrated Methane Pyrolysis for Decarbonising the Natural Gas Supply Chain Discussion

Methane pyrolysis for hydrogen production is critical for decarbonization efforts, provided that the co-produced solid carbon can be separated affordably and valorized at scale. Studies demonstrate the potential of molten metal alloy catalysts operating in bubble column reactors, achieving industrial-relevant productivities (10⁻⁵ to 10⁻⁶ mol/s/cm³). Key catalytic mechanisms involve surface segregation and surface charge distribution, with electronic effects significantly influencing C–H and C–O bond activation rates. For instance, copper-indium alloys are shown to catalyze the formation of multi-walled carbon nanotubes (CNTs) from nanodroplets, with subsequent heat treatment enabling effective catalyst removal. Another approach, utilizing floating-catalyst gas-phase reactors with iron and sulfur precursors and gas recycling, has demonstrated net hydrogen production (84.5 vol% H₂) alongside CNTs. Significant advances in post-processing have led to CNT fibers with tensile strengths exceeding 8 GPa and recent reports of electrical conductivity surpassing copper and aluminum, making them competitive with high-end carbon fibers. Reactor productivity has increased 100-fold, though further optimization of catalyst selectivity and residence time is needed to prevent undesirable radial growth and ensure high-quality product. For industrial-scale application, a non-catalytic methane pyrolysis process aims for cost parity with steam methane reforming (SMR) coupled with carbon capture, utilization, and storage (CCUS). While initial Gen 1 reactors demonstrated hydrogen and carbon yields, achieving consistent commercial-grade carbon black requires improved reactor temperature and retention time control. New Gen 2 reactor architectures are being developed to increase hydrogen capacity and ensure consistent carbon quality. Overall, the economic viability and scalability of methane pyrolysis are contingent on developing robust carbon valorization pathways, with new markets beyond traditional carbon black, such as in construction materials, advanced conductors, and aerospace composites, being essential for accommodating the substantial volumes of carbon produced. Considerations for the long-term fate of carbon and the reduction of upstream fugitive methane emissions are also critical for the overall environmental impact. Welcome from the Chair Molten metal catalyzed carbon nanotube production Pyrolysis of Methane to Bulk CNT Materials: Manufacturing Carbons that Are Useful for Manufacturing EKONA: Our Journey to Develop Low-Cost, Clean Hydrogen from Methane Pyrolysis Discussion

With the assistance of deep learning (DL), we present a framework for predicting the turbulent eddy viscosity in unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for particle-laden jet flows. We report a complete workflow from identifying the input flow and particle quantities in the training phase to predicting the flow and particle fields in the testing phase. The framework incorporates a deep neural network model, also known as multi-layer perceptrons, into the momentum equations of the Euler–Lagrangian gas–solid flow system. A data-driven, physics-informed DL approach was employed to predict the modeled turbulent eddy viscosity field, formulated as a function of the instantaneous flow and particle quantities. In the training phase, these regression functions were trained with an existing high-fidelity direct numerical simulation database. In the testing phase, the trained model was then used to predict the instantaneous local eddy viscosity to update the closure term and to solve the URANS equations iteratively. A series of round, turbulent particle-laden jets in a co-flow with various Stokes numbers were assessed, including those beyond the range of conditions employed for training. The proposed DL–URANS model was found to provide enhanced accuracy for predicting both flow and particle quantities when compared with the baseline URANS simulation.

Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, coupled with deep learning, to significantly increase the effectiveness in searching for and identifying rescue targets during a fire. With a relatively simple optical system, broadband flame luminosity can be spectrally filtered out from the scattering signal of the object. The application of deep learning algorithms can further and significantly enhance the effectiveness of object search and identification. The work shows that this novel optics–deep learning combined method can improve the object identification accuracy from 7.0% with the naked eye to 83.1%. A processing speed of 10 frames per second can also be achieved on a single CPU. These results indicate that the optical method coupled with machine learning algorithms can potentially be a very useful technique for object searching in fire rescue, especially considering the emergence of low-cost, powerful, compact violet light sources and the rapid development of machine learning methods. Potential designs for practical systems are also discussed.