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Towards implementation of alloy-specific thermo-fluid modelling for laser powder-bed fusion of Mg alloys

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Journal of Magnesium and Alloys 2024年第6期12卷 2327-2344页

作者： Mohammad Hoseini-Athar Mikael Ersson Peter Hedström Department of Material Science and Engineering KTH Royal Institute of TechnologyStockholmSE-10044 Sweden

Multi-physics thermo-fluid modeling has been extensively used as an approach to understand melt pool dynamics and defect formation as well as optimizing the process-related parameters of laser powder-bed fusion(L-PBF).However,its capabilities for being implemented as a reliable tool for material design,where minor changes in material-related parameters must be accurately captured,is still in *** the present research,first,a thermo-fluid computational fluid dynamics(CFD)model is developed and validated against experimental *** the predicted material properties of the pure Mg and commercial ZK60 and WE43 Mg alloys,parametric studies are done attempting to elucidate how the difference in some of the material properties,i.e.,saturated vapor pressure,viscosity,and solidification range,can influence the melt pool *** is found that a higher saturated vapor pressure,associated with the ZK60 alloy,leads to a deeper unstable keyhole,increasing the keyhole-induced porosity and evaporation mass *** viscosity and wider solidification range can increase the non-uniformity of temperature and velocity distribution on the keyhole walls,resulting in increased keyhole instability and formation of ***,the WE43 alloy showed the best behavior in terms of defect formation and evaporation mass loss,providing theoretical support to the extensive use of this alloy in *** summary,this study suggests an approach to investigate the effect of materials-related parameters on L-PBF melting and solidification,which can be extremely helpful for future design of new alloys suitable for L-PBF.

关键词： Mg alloys Laser powder-bed fusion(L-PBF) Melt pool dynamics Computational fluid dynamics Fluid flow

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Elasto-inertial focusing and particle migration in high aspect ratio microchannels for highthroughput separation

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Microsystems & Nanoengineering 2024年第3期10卷 381-394页

作者： Selim Tanriverdi Javier Cruz Shahriar Habibi Kasra Amini Martim Costa Fredrik Lundell Gustaf Mårtensson Luca Brandt Outi Tammisola Aman Russom Division of Nanobiotechnology Department of Protein ScienceScience for Life LaboratoryKTH Royal Institute of TechnologySolna 17165Sweden Division of Microsystems technology Department of Materials Science and EngineeringUppsala UniversityUppsala 75237Sweden FLOW and SeRC(Swedish e-Science Research Centre) Department of Engineering MechanicsRoyal Institute of TechnologyStockholm SE 10044Sweden FLOW and Fluid Physics Laboratory Department of Engineering MechanicsRoyal Institute of TechnologyStockholmSweden Wallenberg Wood Science Center Royal Institute of TechnologyStockholm SE 10044Sweden Department of Energy and Process Engineering Norwegian University of Science and Technology(NTNU)TrondheimNorway AIMES Center for the Advancement of Integrated Medical and Engineering Sciences at Karolinska institutet and KTH royal institute of technology StockholmSweden

The combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using *** there is considerable interest in the field of elasto-inertial microfluidics owing to its potential applications,research on particle focusing has been mostly limited to low Reynolds numbers(Re<1),and particle migration toward equilibrium positions has not been extensively *** this work,we thoroughly studied particle focusing on the dynamic range of flow rates and particle migration using straight microchannels with a single inlet high aspect *** initially explored several parameters that had an impact on particle focusing,such as the particle size,channel dimensions,concentration of viscoelastic fluid,and flow *** experimental work covered a wide range of dimensionless numbers(0.05

关键词： particle separation straight

Path signature-based XAI-enabled network time series classification

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Science China(Information Sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology(CICAEET) Nanjing University of Information Science and Technology School of Computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification *** include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

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Microstructure and magnetocaloric properties of melt-extracted SmGdDyCoAl high-entropy amorphous microwires

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Rare Metals 2024年第3期43卷 1234-1242页

作者： Shi-Jie Wei Hong-Xian Shen Lun-Yong Zhang Lin Luo Xin-Xing Tang Jian-Fei Sun Xiao-Qing Li School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Applied Materials Physics Department of Materials Science and EngineeringKTH-Royal Institute of Technology10044StockholmSweden

This paper presents a systematic investigation of the microstructure and magnetocaloric properties of melt-extracted Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)high-entropy *** fabricated wires exhibited an amorphous structure,and the temperature interval of the undercooled liquid AT was 45 *** microwires underwent a second-order magnetic transition from a ferromagnetic to a paramagnetic state near the Curie temperature(T_(C)=52 K),The maximum magnetic entropy change(-ΔS_M^(max)),the relative cooling power and the refrigeration capacity reached 6.34 J·kg^(-1)·K^(-1).422.09 J·kg^(-1)and 332.94 J·kg^(-1),respectively,under a magnetic field change of 5 *** addition,the temperature-averaged entropy changes with two temperature lifts(3 and 10 K)were 6.32 and 6.27 J·kg^(-1)·K^(-1),*** good magnetocalorie performance highlights the significant potential for the Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)microwires to be used as magnetic refrigerant materials in low-temperature region *** work will serve as a valuable reference for future investigations on low-temperature high-entropy magnetocaloric materials.

关键词： Melt-extraction High-entropy amorphous microwires Magnetocaloric effect Magnetic refrigeration

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Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

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Building Simulation 2024年第3期17卷 399-414页

作者： Giovanni Calzolari Wei Liu Division of Sustainable Buildings Department of Civil and Architectural EngineeringKTH Royal Institute of TechnologyBrinnelvägen 23Stockholm11428Sweden

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics(CFD)simulations and deep learning *** coupling approach is showcased by the development of a data-driven turbulence *** new turbulence model is built using a deep learning neural network,whose mapping structure is based on a zero-equation turbulence model for built environment simulations,and is coupled with the CFD software OpenFOAM to create a hybrid *** neural network is a standard shallow multi-layer *** number of hidden layers and nodes per layer was optimized using Bayesan optimization *** framework is trained on an indoor environment case study,as well as tested on an indoor office simulation and an outdoor building array *** show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes(RANS)turbulence models,while maintaining a similar level of *** model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow *** some challenges,namely the mapping constraint,the limited training dataset size and the source of generation of training data,the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

关键词： neural networks computational fluid dynamics(CFD) turbulence model OpenFOAM

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer Science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary Science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

Patients Transportation in Surgery Scheduling Problem

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Journal of Systems Science & Complexity 2024年第3期37卷 1100-1113页

作者： CHIKHI Nacira DJEHICHE Boualem Department of Operations Research Faculty of MathematicsUniversity of Science and Technology USTHBBab-Ezzouar 161llAlgeria Department of Mathematics KTH Royal Institute of TechnologySE-10044 StockholmSweden

The scheduling problem in surgery is difficult because, in addition of the planning of the operating rooms which are the most expensive resources in hospitals, each surgery requires a combination of human and material resources. In this paper, the authors address a surgery scheduling problem which arises in operated health care facility. Moreover, the authors consider simultaneously materiel and human resources. This problem is a three-stages flow shop scheduling environment. The first stage(ward) contains a limited number of resources of the same type(beds);The second stage contains different resources with limited capacity(operating rooms, surgeons, nurses, anesthesiologists)and the third stage contains a limited number of recovery beds. There is also a limited number of transporters(porters) between the ward and the other stages. The objective of the problem is to minimize the completion time of the last patient(makespan). The authors formulate this NP-Hard problem in a mixed integer programming model and conduct computational experiments to evaluate the performance of the proposed model.

关键词： Makespan patient scheduling surgery transport

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Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN

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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU School of Computer Science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

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Prescribed-time leader-following consensus of linear multi-agent systems by bounded linear time-varying protocols

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Science China(Information Sciences) 2024年第1期67卷 140-153页

作者： Kai ZHANG Bin ZHOU Xuefei YANG Guangren DUAN Center for Control Theory and Guidance technology Harbin Institute of Technology

This paper considers the prescribed-time leader-following consensus problem of input-constrained linear multi-agent systems under generally directed communication topology in two cases:the Laplacian matrix related to the entire communication topology between agents is either known or *** particular,the consensus problem for the former case is solved by a novel bounded linear time-varying(LTV) protocol,where the feedback gain is formulated by the parametric Lyapunov equation and the knowledge of the Laplacian ***,by utilizing a distributed observer,a fully bounded LTV protocol is proposed for the latter *** should be noted that,compared with the existing results,the system under consideration is more general,the designed protocols are linear,and the consensus problem is accomplished even in a fully distributed ***,the effectiveness of the proposed approach is verified by a numerical example.

关键词： leader-following consensus time-vary feedback prescribed-time control fully distributed consensus