| 1. |
- Jiang, Yuguang, et al.
(författare)
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Influences of interconnection structure on the flow and heat transfer behaviors of the hydrocarbon fuel in parallel SCRamjet regenerative cooling channels
- 2023
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Ingår i: Numerical Heat Transfer, Part A Applications. - : Informa UK Limited. - 1040-7782 .- 1521-0634. ; 84:11, s. 1273-1296
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Tidskriftsartikel (refereegranskat)abstract
- Regenerative cooling is of great significance to secure the thermal structure and promote the flight Mach number range of the SCRamjet. Interconnection structure (ICS) plays a key role in improving the coolant flow distribution and heat sink utilization. In this work, the flow and heat transfer behaviors of the hydrocarbon fuel in parallel regenerative cooling channels with ICS are numerically investigated. The ICS improves the flow distribution and alleviates the local heat transfer deterioration. The influences of ICS configuration mainly consist of two aspects: (a). inter-channel pressure communication; (b). transverse mass transfer. The maximum wall temperature falls by 117.48 K/6.96% with the ICS introduced. Different sizes and positions of ICS are also studied. ICS with too small size cannot provide enough space for pressure communication and transverse mass transfer. While ICS with too large size leads to local heat transfer deterioration. The optimal Ф value to achieve the lowest heated wall temperature is Ф = 5 in this work. Regarding the position of ICS, it affects the local heat transfer deterioration through flow distribution and thermal load distribution. Ps = 50% (ICS locates at the middle of the heated section) presents the optimal cooling effect in this work. At last, the ICS configurations are universal to different heat flux distributions. The maximum wall temperature (Case qf2) decreases by 137.72 K/8.15% compared with Case C1 (without ICS).
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| 2. |
- König, Hans-Henrik, et al.
(författare)
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Solidification modes during additive manufacturing of steel revealed by high-speed X-ray diffraction
- 2023
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Ingår i: Acta Materialia. - : Elsevier BV. - 1359-6454 .- 1873-2453. ; 246, s. 118713-
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Tidskriftsartikel (refereegranskat)abstract
- Solidification during fusion-based additive manufacturing (AM) is characterized by high solidification velocities and large thermal gradients, two factors that control the solidification mode of metals and alloys. Using two synchrotron-based, in situ setups, we perform high-speed X-ray diffraction measurements to investigate the impact of the solidification velocities and thermal gradients on the solidification mode of a hot-work tool steel over a wide range of thermal conditions of relevance to AM of metals. The solidification mode of primary delta-ferrite is observed at a cooling rate of 2.12 x 104 K/s, and at a higher cooling rate of 1.5 x 106 K/s, delta-ferrite is sup-pressed, and primary austenite is observed. The experimental thermal conditions are evaluated and linked to a Kurz-Giovanola-Trivedi (KGT) based solidification model. The modelling results show that the predictions from the multicomponent KGT model agree with the experimental observations. This work highlights the role of in situ XRD measurements for a fundamental understanding of the microstructure evolution during AM and for vali-dation of computational thermodynamics and kinetics models, facilitating parameter and alloy development for AM processes.
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| 3. |
- Wang, Sen, et al.
(författare)
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VNE-TD : a Virtual Network Embedding Algorithm Based on Temporal-Difference Learning
- 2019
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Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 161, s. 251-263
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Tidskriftsartikel (refereegranskat)abstract
- Recently, network virtualization is considered as a promising solution for the future Internet which can help to overcome the resistance of the current Internet to fundamental changes. The problem of embedding Virtual Networks (VN) in a Substrate Network (SN) is the main resource allocation challenge in network virtualization. The major challenge of the Virtual Network Embedding (VNE) problem lies in the contradiction between making online embedding decisions and pursuing a long-term objective. Most previous works resort to balancing the SN workload with various methods to deal with this contradiction. Rather than passive balancing, we try to overcome it by learning actively and making online decisions based on previous experiences. In this article, we model the VNE problem as Markov Decision Process (MDP) and develop a neural network to approximate the value function of VNE states. Further, a VNE algorithm based on Temporal-Difference Learning (one kind of Reinforcement Learning methods), named VNE-TD, is proposed. In VNE-TD, multiple embedding candidates of node-mapping are generated probabilistically, and TD Learning is involved to evaluate the long-run potential of each candidate. Extensive simulation results show that VNE-TD outperforms previous algorithms significantly in terms of both block ratio and revenue.
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