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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Numerical Study of Distorted Tulip Flame Propagation in C...
Fernando Illacanchi, Sebastian Valencia, Cesar Celis, Andres Men · 2023-09-12 · via cs.DC updates on arXiv.org

Understanding the dynamics of premixed flames that propagates in confined systems is important in a wide range of applications. The study of premixed flames propagating in a closed channel covers a variety of complexities related to flame ignition, laminar flame development, and strong non-linear interaction between the flame and the surrounding walls. Accordingly, to study the dynamics of premixed flames propagating in closed channels, numerical simulations of the propagation of distorted tulip flames are carried out in this work. More specifically, a set of fully reactive compressible transport equations are solved here using the high-order PPM. A 21-step chemical kinetic mechanism is employed to model the chemical kinetics and the energy release in an air-hydrogen mixture. Computational mesh independence studies are carried out in this work by both refining grid elements and employing different levels of adaptive mesh refinements (AMR). The main results show that the classic tulip flame behavior evolves into a distorted one. Indeed, two consecutive collapses on the flame front are observed, which are related to wave pressure and the presence of reverse flow. It is particularly found that the pressure wave produced by the interaction of the flame skirt with the side walls reduces the flame velocity and contributes to the formation of tulip flames. This is consistent with the reduction in both flame area and pressure gradient at the flame tip. Furthermore, the collapse of flame cups is associated with the formation of the vortex near the channel side walls and the increase of pressure waves.