Tootfinder

On the spatial structure and intermittency of soot in a lab-scale gas turbine combustor: Insights from large-eddy simulations
Leonardo Pachano, Daniel Mira, Abhijit Kalbhor, Jeroen van Oijen
https://arxiv.org/abs/2602.23155 https://arxiv.org/pdf/2602.23155 https://arxiv.org/html/2602.23155
arXiv:2602.23155v1 Announce Type: new
Abstract: This work presents a numerical investigation of soot formation in the Cambridge lab-scale gas turbine combustor. Large-eddy simulations (LES) of a swirl-stabilized ethylene flame are performed using the flamelet generated manifold method coupled with a discrete sectional model to account for soot formation, growth, and oxidation. The study aims to elucidate the mechanism governing the spatial structure and intermittency of soot, supported by comparisons with experimental data. The predicted soot distribution agrees well with measurements, with peak concentrations near the bluff body. Flow recirculation is identified as the key mechanism driving soot accumulation in fuel-rich regions, where surface reactions dominate soot mass growth. Soot intermittency arises from fluctuations in the flow field driven by interactions between the flame front and the recirculation vortex. Two soot modeling approaches are evaluated, differing in their treatment of soot model quantities: the first approach employs on-the-fly computation of source terms (FGM-C), while the second uses fully pre-tabulated source terms (FGM-T). Their predictive performance and computational cost are compared in the context of unsteady, sooting flames in swirl-stabilized combustors.
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Estimating Spatially Resolved Radiation Fields Using Neural Networks
Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor
https://arxiv.org/abs/2512.17654 https://arxiv.org/pdf/2512.17654 https://arxiv.org/html/2512.17654
arXiv:2512.17654v1 Announce Type: new
Abstract: We present an in-depth analysis on how to build and train neural networks to estimate the spatial distribution of scattered radiation fields for radiation protection dosimetry in medical radiation fields, such as those found in Interventional Radiology and Cardiology. Therefore, we present three different synthetically generated datasets with increasing complexity for training, using a Monte-Carlo Simulation application based on Geant4. On those datasets, we evaluate convolutional and fully connected architectures of neural networks to demonstrate which design decisions work well for reconstructing the fluence and spectra distributions over the spatial domain of such radiation fields. All used datasets as well as our training pipeline are published as open source in separate repositories.
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Proton Energy Dependence of Radiation Induced Low Gain Avalanche Detector Degradation
Veronika Kraus, Marcos Fernandez Garcia, Luca Menzio, Michael Moll
https://arxiv.org/abs/2602.01800 https://arxiv.org/pdf/2602.01800 https://arxiv.org/html/2602.01800
arXiv:2602.01800v1 Announce Type: new
Abstract: Low Gain Avalanche Detectors (LGADs) are key components for precise timing measurements in high-energy physics experiments, including the High Luminosity upgrades of the current LHC detectors. Their performance is, however, limited by radiation induced degradation of the gain layer, primarily driven by acceptor removal. This study presents a systematic comparison of how the degradation evolves with different incident proton energies, using LGADs from Hamamatsu Photonics (HPK) and The Institute of Microelectronics of Barcelona (IMB-CNM) irradiated with 18 MeV, 24 MeV, 400 MeV and 23 GeV protons and fluences up to 2.5x10^15 p/cm2. Electrical characterization is used to extract the acceptor removal coefficients for different proton energies, whereas IR TCT measurements offer complementary insight into the gain evolution in LGADs after irradiation. Across all devices, lower energy protons induce stronger gain layer degradation, confirming expectations. However, 400 MeV protons consistently appear less damaging than both lower and higher energy protons, an unexpected deviation from a monotonic energy trend. Conversion of proton fluences to 1 MeV neutron-equivalent fluences reduces but does not eliminate these differences, indicating that the standard Non-Ionizing Energy Loss (NIEL) scaling does not fully account for the underlying defect formation mechanisms at different energies and requires revision when considering irradiation fields that contain a broader spectrum of particle types and energies.
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Equilibria: Fair Multi-Tenant CXL Memory Tiering At Scale
Kaiyang Zhao, Neha Gholkar, Hasan Maruf, Abhishek Dhanotia, Johannes Weiner, Gregory Price, Ning Sun, Bhavya Dwivedi, Stuart Clark, Dimitrios Skarlatos
https://arxiv.org/abs/2602.08800 https://arxiv.org/pdf/2602.08800 https://arxiv.org/html/2602.08800
arXiv:2602.08800v1 Announce Type: new
Abstract: Memory dominates datacenter system cost and power. Memory expansion via Compute Express Link (CXL) is an effective way to provide additional memory at lower cost and power, but its effective use requires software-level tiering for hyperscaler workloads. Existing tiering solutions, including current Linux support, face fundamental limitations in production deployments. First, they lack multi-tenancy support, failing to handle stacked homogeneous or heterogeneous workloads. Second, limited control-plane flexibility leads to fairness violations and performance variability. Finally, insufficient observability prevents operators from diagnosing performance pathologies at scale.
We present Equilibria, an OS framework enabling fair, multi-tenant CXL tiering at datacenter scale. Equilibria provides per-container controls for memory fair-share allocation and fine-grained observability of tiered-memory usage and operations. It further enforces flexible, user-specified fairness policies through regulated promotion and demotion, and mitigates noisy-neighbor interference by suppressing thrashing.
Evaluated in a large hyperscaler fleet using production workloads and benchmarks, Equilibria helps workloads meet service level objectives (SLOs) while avoiding performance interference. It improves performance over the state-of-the-art Linux solution, TPP, by up to 52% for production workloads and 1.7x for benchmarks. All Equilibria patches have been released to the Linux community.
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