Influence of the Wall Heat Transfer on Flame Propagation

In the internal combustion engines, the flames interact with the walls of the cylinder, which affects the flame propagation characteristics and the engine performance. The flame tends to quench near the wall, which is due to wall heat fluxes. Also, wall heat transfer can play a significant role in undesirable auto-ignition of unburned mixture in the cylinder. The flame-unburned mixture-wall interactions can influence engine knock and affect the engine emission. The present research is aimed at understanding the effects of wall heat transfer on flame propagation. The flame propagation in the presence of the walls will be simulated and the effects of wall heat transfer on flame propagation properties will be investigated by changing wall temperature, pressures, channel widths, and equivalence ratios. By analyzing variations of those properties, we will be able to advance an understanding of flame-mixture ignition-wall heat transfer interactions, which will help reduce engine knock and emission for different types of engine structures. This research focuses mainly on the propagation of laminar premixed flames. The numerical method is used to solve the mass, momentum and energy conservation together with the combustion model. The first stage study focuses on using a single step chemistry reaction model to simulate flame propagating along one dimensional domain and two dimensional channels with adiabatic walls under different air fuel ratios, geometries, and injected flow velocity. This simulation is aimed to provide a reasonable distribution of temperature, flow velocity, pressure, fuel, oxidizer and products in the presence of the adiabatic walls. Based on the first stage, the second stage study focuses on adding heat transfer effects to the walls for two dimensional cases and analyze how wall heat transfer affects the distribution of the properties. For the first stage of research, the results from a single step chemistry model are compared with the experimental data. The results show that the single step chemistry model can accurately predict the flame consumption speed when air-fuel equivalence ratio ranges from 0.5 to 1. For the two dimensional channel with adiabatic walls, the simulation shows that the presence of walls influences flame propagation through the flow velocity variation near the wall. In the second stage, wall heat transfer is included and the effects of wall heat transfer is analyzed in terms of flame quenching in the presence of walls. This research will lead to a better understanding of interactions of wall heat transfer and combustion in internal combustion engines, which can be a useful reference to analyze the engine knock and engine emissions.No embargoAcademic Major: Mechanical Engineerin

Spectrum-based deep neural networks for fraud detection

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection

A note on the Bloch representation of absolutely maximally entangled states

The absolutely maximally entangled (AME) states play key roles in quantum information processing. We provide an explicit expression of the generalized Bloch representation of AME states for general dimension $d$ of individual subsystems and arbitrary number of partite $n$. Based on this analytic formula, we prove that the trace of the squared support for any given weight is given by the so-called hyper-geometric function and is irrelevant with the choices of the subsystems. The optimal point for the existence of AME states is obtained

Blue-Green Infrastructure for Sustainable Urban Stormwater Management—Lessons from Six Municipality-Led Pilot Projects in Beijing and Copenhagen

Managing stormwater on urban surfaces with blue-green infrastructure (BGI) is being increasingly adopted as an alternative to conventional pipe-based stormwater management in cities. BGI combats water problems and provides multiple benefits for cities, including improved livability and enhanced biodiversity. The paper examines six municipality-led pilot projects from Beijing and Copenhagen, through a review of documents, site observations and interviews with project managers. Beijing’s projects attempt to divert from a pipe-based approach but are dominated by less BGI-based solutions; they could benefit from more integration of multiple benefits with stormwater management. Copenhagen’s projects combine stormwater management with amenity improvement, but lack focus on stormwater utilization. Reviewed municipality-led pilot projects are shown to play an important role in both testing new solutions and upscaling them in the process of developing more sustainable cities. Key lessons are extracted and a simple guideline synthesized. This guideline suggests necessary considerations for a holistic solution that combines stormwater management and urban space improvements. Key lessons for sustainable solutions include defining a clear water technique priority, targeting both small and big rain events, strengthening ‘vertical design’ and providing multiple benefits. An integrated stormwater management and landscape design process is a prerequisite to the meaningful implementation of these solutions. Research and documentation integrated with pilot projects will help upscale the practice at city scale

Sampling Electronic Fock States using Determinantal Quantum Monte Carlo

Analog quantum simulation based on ultracold atoms in optical lattices has catalyzed significant breakthroughs in the study of quantum many-body systems. These simulations rely on the statistical sampling of electronic Fock states, which are not easily accessible in classical algorithms. In this work, we modify the determinantal quantum Monte Carlo by integrating a Fock-state update mechanism alongside the auxiliary field. This method enables efficient sampling of Fock-state configurations. The Fock-state restrictive sampling scheme further enables the pre-selection of multiple ensembles at no additional computational cost, thereby broadening the scope of simulation to more general systems and models. Employing this method, we analyze static correlations of the Hubbard model up to the fourth order and achieve quantitative agreement with cold-atom experiments. The simulations of dynamical spectroscopies of the Hubbard and Kondo-lattice models further demonstrate the reliability and advantage of this method.Comment: 11 pages, 6 figure

Diffusion model for relational inference

Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI), inspired by a self-supervised method for probabilistic time series imputation. DiffRI learns to infer the probability of the presence of connections between components through conditional diffusion modeling

Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification

Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and inherent imbalanced distributions, due to difficulties in data collection and variations in disease prevalence across different types. In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS), which collectively target the imbalance classification issue at both the instance level and the class level. The OIS module alleviates the data insufficiency problem by generating representative samples tailored for online training of the classifier. On the other hand, the AAS module dynamically balances the synthesized samples among various classes, targeting those with low training accuracy. To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets. The results obtained demonstrate the superiority of our approach over state-of-the-art methods as well as the effectiveness of each component. The source code will be released upon acceptance