Topology Optimization of Two Fluid Heat Transfer Problems for Heat Exchanger Design

Topology optimization of thermal-fluid coupling problems has received widespread attention.This article proposes a novel topology optimization method for laminar two-fluid heat exchanger design.The proposed method utilizes an artificial density field to create two permeability interpolation functions that exhibit opposing trends,ensuring separation between the two fluid domains.Additionally,a Gaussian function is employed to construct an interpolation function for the thermal conductivity coefficient.Furthermore,a computational program has been developed on the OpenFOAM platform for the topology optimization of two-fluid heat exchangers.This program leverages parallel computing,significantly reducing the time required for the topology optimization process.To enhance computational speed and reduce the number of constraint conditions,we replaced the conventional pressure drop constraint condition in the optimization problem with a pressure inlet/outlet boundary condition.The 3D optimization results demonstrate the characteristic features of a surface structure,providing valuable guidance for designing heat exchangers that achieve high heat exchange efficiency while minimizing excessive pressure loss.At the same time,a new structure appears in large-scale topology optimization,which proves the effectiveness and stability of the topology optimization program written in this paper in large-scale calculation.

Chang'E-7 Lunar Soil Water Molecule Analyzer(LSWMA)Prototype for High-Precision Measurement of Water Content and Hydrogen Isotope Ratio

0 INTRODUCTION The lunar water study could provide insight into the Moon's origin and evolution(Qin et al.,2012),and its presence can open up the possibilities of establishing lunar base in the future.However,to date,remote sensing data from lunar water exploration has not yielded consistent results(Li and Milliken,2017;Mitrofanov et al.,2010;Paige et al.,2010;Dino et al.,2009;Lawrence et al.,2006).

Jetting of a near-wall cavitation bubble induced by another tandem bubble

Double bubbles near a rigid wall surface collapse to produce a significant jet impact,with potential applications in surface cleaning and ultrasonic lithotripsy.However,the dynamic behaviors of near-wall bubbles remain unexplored.In this study,we investigate the jetting of a near-wall bubble induced by another tandem bubble.We define two dimensionless standoff distances,γ_(1),γ_(2),to represent the distances from the center of the near-wall bubble to the rigid wall and the center of controlling bubble to the center of the near-wall bubble,respectively.Our observations reveal three distinct jetting regimes for the near-wall bubble:transferred jetting,double jetting,and directed jetting.To further investigate the jetting mechanism,numerical simulations are conducted using the compressibleInterFoam solver in the open-source framework of OpenFOAM.A detailed analysis shows that the transferred jet flow is caused by the pinch-off resulting from the axial contraction velocity at the lower end of the near-wall bubble being greater than the vertical contraction velocity,leading to a maximum jet velocity of 682.58 m/s.In the case of double jetting,intense stretching between the controlling bubble and the wall leads to a pinch-off and a double jetting with a maximum velocity of 1096.29 m/s.The directed jet flow is caused by the downward movement of the high-pressure region generated by the premature collapse of the controlling bubble,with the maximum jet velocity reaching 444.62 m/s.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level Ⅱ or Ⅲ in the central and southwestern study areas,level Ⅳ in the northern region,and level Ⅴ in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

Porous hexagonal Mn_(5)O_(8)nanosheets as fast-charging anode materials for lithium-ion batteries

Among various metal oxide nanomaterials,manganese oxides,which can exist in different structures and valence states,are considered highly promising anode materials for lithium-ion batteries(LIBs).However,conventional manganese oxides,such as MnO and MnO2,face significant challenges during cycling process.Specifically,poor electronic conductivity and large volume changes result in low specific capacity during high current charging and discharging,as well as poor fast-charging performance.This work presents an approach to synthesizing porous hexagonal Mn_(5)O_(8)nanosheets via hydrothermal and annealing methods and applies them as anode materials for LIBs.The Mn_(5)O_(8)nanomaterials exhibit a thin plate morphology,which effectively reduces the distance for ion/electron transmission and mitigates the phenomenon of volume expansion.Additionally,the large pore size of Mn_(5)O_(8)results in abundant interlayer and intralayer defects,which further increase the rate of ion transmission.These unique characteristics enable Mn_(5)O_(8)to demonstrate excellent electrochemical performance(938.7 mAh·g^(-1)after 100 cycles at 100 mA·g^(-1))and fast charging performance(675.7 mAh·g^(-1)after 1000 cycles at 3000 mA·g^(-1)),suggesting that Mn_(5)O_(8)nanosheets have the potential to be an ideal fast-charging anode material for LIBs.