Transition from turbulence-dominated to instability-dominated combustion regime in lean hydrogen-air flames

To explore the importance of thermodiffusive and hydrodynamic instabilities of laminar flames in turbulent flows, previously generated direct numerical simulations of statistically one-dimensional complex-chemistry lean hydrogen-air flames in forced turbulence were continued by switching-off turbulence forcing. Three sets of flames characterized by different ratios of initial root-mean-square velocity to laminar flame speed, i.e., (A) u′/SL=2.2, (B) u′/SL=4.0, and (C) u′/SL=8.3, were addressed. Moreover, new complementary simulations of unstable laminar flames were performed. Analyses from the obtained numerical results indicate (but do not prove) that laminar flame instabilities play a minor role at sufficiently high Karlovitz numbers Ka. This supposition is supported, first, in cases B and C, where the initial value of turbulent burning velocity UT is significantly higher than burning velocity evaluated during non-linear stage of laminar flame instability development in the same computational domain. Second, regular large-scale perturbations of instantaneous flame surface are prominent in the unstable laminar flame but are not observed at high Ka. Third, Karlovitz numbers associated with appearance of such perturbations as the turbulence decays are scattered between 4 and 10 and are consistent within an order of magnitude to a recently proposed criterion (Chomiak and Lipatnikov, Phys. Rev. E 107: 015102, 2023) of importance for laminar flame instabilities in turbulent flows. Fourth, at such transition instants, a ratio of potential and solenoidal turbulent kinetic energies, averaged over the flame-brush leading edge, is close to 2.0 in 11 studied cases and a ratio of UT/SL varies between 3.0 and 3.5. Fifth, the maximum fuel consumption rate (over the computational domain) decreases as the turbulence decays. Thus, the initial maximum rates are significantly higher than the counterpart rate in the unstable laminar flame. Together these results show that the hypothesis that laminar flame instabilities play only a minor role at high Ka deserves further study

Deep Nearest Neighbors for\ua0Anomaly Detection in\ua0Chest X-Rays

Identifying medically abnormal images is crucial to the diagnosis procedure in medical imaging. Due to the scarcity of annotated abnormal images, most reconstruction-based approaches for anomaly detection are trained only with normal images. At test time, images with large reconstruction errors are declared abnormal. In this work, we propose a novel feature-based method for anomaly detection in chest x-rays in a setting where only normal images are provided during training. The model consists of lightweight adaptor and predictor networks on top of a pre-trained feature extractor. The parameters of the pre-trained feature extractor are frozen, and training only involves fine-tuning the proposed adaptor and predictor layers using Siamese representation learning. During inference, multiple augmentations are applied to the test image, and our proposed anomaly score is simply the geometric mean of the k-nearest neighbor distances between the augmented test image features and the training image features. Our method achieves state-of-the-art results on two challenging benchmark datasets, the RSNA Pneumonia Detection Challenge dataset, and the VinBigData Chest X-ray Abnormalities Detection dataset. Furthermore, we empirically show that our method is robust to different amounts of anomalies among the normal images in the training dataset. The code is available at: https://github.com/XixiLiu95/deep-kNN-anomaly-detection

Physics-informed machine learning models for ship speed prediction

This paper proposes a novel physics-informed machine learning method to build grey-box model (GBM) predicting ship speed for ocean crossing ships. In this method, the expected ship speed in calm water is first modeled by the physics-informed neural networks (PINNs) based on speed-power model tests. Then the eXtreme Gradient Boosting (XGBoost) machine learning algorithm is integrated to estimate ship speed reduction under actual weather conditions. The proposed GBM has been compared against the traditional black-box model (BBM) using performance monitoring data from two ships. The results show that when the amount of data is sufficient for modeling, the GBM can increase the accuracy of speed prediction by about 30%. When data volume is limited, the GBM can also significantly improve the prediction results. Finally, the GBM is validated by checking its implementation for the ETA predictions of cross-Pacific or North Atlantic voyages. The highest cumulative error of sailing time estimated by the GBM is 5 h among all the study cases

ITERATIVE SOLUTION OF SPATIAL NETWORK MODELS BY SUBSPACE DECOMPOSITION

. We present and analyze a preconditioned conjugate gradient method (PCG) for solving spatial network problems. Primarily, we consider diffusion and structural mechanics simulations for fiber based materials, but the methodology can be applied to a wide range of models, fulfilling a set of abstract assumptions. The proposed method builds on a classical subspace decomposition into a coarse subspace, realized as the restriction of a finite element space to the nodes of the spatial network, and localized subspaces with support on mesh stars. The main contribution of this work is the convergence analysis of the proposed method. The analysis translates results from finite element theory, including interpolation bounds, to the spatial network setting. A convergence rate of the PCG algorithm, only depending on global bounds of the operator and homogeneity, connectivity and locality constants of the network, is established. The theoretical results are confirmed by several numerical experiments

Introduction and fundamentals

This chapter introduces the book and presents the main principles and fundamentals for positioning and location-based analytics, thus effectively providing the basis for the following chapters. After a brief introduction and motivation for the book, we present the main use cases, verticals, and applications for positioning and location-based analytics. Then, we provide the technical fundamentals for understanding positioning and navigation algorithms, as well as location-based analytics. An introduction to the architectural principles is presented. Finally, an outline of the book chapters is provided

On some double Nahm sums of Zagier

Zagier provided eleven conjectural rank two examples for Nahm\u27s problem. All of them have been proved in the literature except for the fifth example, and there is no q-series proof for the tenth example. We prove that the fifth and the tenth examples are in fact equivalent. Then we give a q-series proof for the fifth example, which confirms a recent conjecture of Wang. This also serves as the first q-series proof for the tenth example, whose explicit form was conjectured by Vlasenko and Zwegers in 2011 and whose modularity was proved by Cherednik and Feigin in 2013 via nilpotent double affine Hecke algebras

The heat transfer potential of compressor vanes on a hydrogen fueled turbofan engine

Hydrogen is a promising fuel for future aviation due to its CO2-free combustion. In addition, its excellent cooling properties as it is heated from cryogenic conditions to the appropriate combustion temperatures provides a multitude of opportunities. This paper investigates the heat transfer potential of stator surfaces in a modern high-speed low-pressure compressor by incorporating cooling channels within the stator vane surfaces, where hydrogen is allowed to flow and cool the engine core air. Computational Fluid Dynamics simulations were carried out to assess the aerothermal performance of this cooled compressor and were compared to heat transfer correlations. A core air temperature drop of 9.5\ua0K was observed for this cooling channel design while being relatively insensitive to the thermal conductivity of the vane and cooling channel wall thickness. The thermal resistance was dominated by the air-side convective heat transfer, and more surface area on the air-side would therefore be required in order to increase overall heat flow. While good agreement with established heat transfer correlations was found for both turbulent and transitional flow, the correlation for the transitional case yielded decent accuracy only as long as the flow remains attached, and while transition was dominated by the bypass mode. A system level analysis, indicated a limited but favorable impact at engine performance level, amounting to a specific fuel consumption improvement of up to 0.8\ua0% in cruise and an estimated reduction of 3.6\ua0% in cruise NOx. The results clearly show that, although it is possible to achieve high heat transfer rate per unit area in compressor vanes, the impact on cycle performance is constrained by the limited available wetted area in the low-pressure compressor

Feedstock recycling of cable plastic residue via steam cracking on an industrial-scale fluidized bed

The use of plastic materials in a circular way requires a technology that can treat any plastic waste and produce the same quality of product as the original. Cable plastic residue from metal recycling of electric wires is composed of cross-linked polyethene (XLPE) and PVC, which is a mixture that cannot be mechanically recycled today. Through thermochemical processes, polymer chains are broken into syngas and monomers, which can be further used in the chemical industry. However, feedstock recycling of such a mixture (XLPE, PVC) has been scarcely studied on an industrial scale. Here, the steam cracking of cable plastic was studied in an industrial fluidised bed, aiming to convert cable plastics into valuable products. Two process temperatures were tested: 730 \ub0C and 800 \ub0C. The results show that the products consist of 27–31 wt% ethylene and propylene, 5–16% wt.% other linear hydrocarbons, and more than 10 wt% benzene. Therefore, 40%–60% of the products are high-value chemicals that could be recovered via steam cracking of cable plastic

Performance and robustness analysis reveals phenotypic trade-offs in yeast

To design strains that can function efficiently in complex industrial settings, it is crucial to consider their robustness, that is, the stability of their performance when faced with perturbations. In the present study, we cultivated 24 Saccharomyces cerevisiae strains under conditions that simulated perturbations encountered during lignocellulosic bioethanol production, and assessed the performance and robustness of multiple phenotypes simultaneously. The observed negative correlations confirmed a trade-off between performance and robustness of ethanol yield, biomass yield, and cell dry weight. Conversely, the specific growth rate performance positively correlated with the robustness, presumably because of evolutionary selection for robust, fast-growing cells. The Ethanol Red strain exhibited both high performance and robustness, making it a good candidate for bioproduction in the tested perturbation space. Our results experimentally map the robustness-performance trade-offs, previously demonstrated mainly by single-phenotype and computational studies

Impact of powder properties on deoxidation and densification of carbon steels during powder bed fusion – Laser beam

This work examined the influence of powder properties on deoxidation and densification of carbon steels during powder bed fusion-laser beam (PBF-LB) at compositions between 0.06 and 1.1 wt% C. Analysis revealed that deoxidation was greatest in alloys with high carbon content, reaching losses of up to 440–600 ppm at compositions of 0.75 and 1.1 wt% C. This behavior was not due to enhanced oxygen removal by spatter, as spatter in high carbon alloys had less oxygen pickup (∼4% vs. ∼27%) and formed smaller oxide layers (∼42 nm vs. ∼82 nm). Instead, it was due to the high oxygen affinity of carbon at elevated temperature, which resulted in formation of gaseous carbon oxides that were subsequently removed by the process atmosphere. Regarding densification, powders with high avalanche energy (>7.75 mJ/kg), break energy (>4.75 mJ/kg), and particle size distribution (D10 > 25 μm) were more likely to form lack of fusion porosity at low energy input