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对热泵低温蒸发海水淡化中的传热与海水淡化性能进行了理论分析。建立了热泵低温蒸发海水淡化系统的传热和热力学模型。编制计算机程序,分析了蒸发压力对系统性能的影响。计算结果表明,蒸发压力的提升,对系统的性能有利有弊:蒸发压力的提升,换热管的换热增强,淡水能耗降低,系统产水效率提高;但所需换热面积增加并且单位换热面积的产水效率降低。 相似文献
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随着计算机迅速发展,利用计算机来进行仿真分析时可以求解一些无法利用理论分析解答的问题,待分析的实际问题多为三维图形,但利用分析软件对三维模型进行分析时,所需的计算量非常大,大多数情况下是将形状规则的三维模型简化为二维模型,但简化是有一定条件的,以电容器为例,利用ANSYS分析软件分析了阴极有圆锥型突起时的电场分布,与二维简化的模型进行了计算对比,从而得出简化模型的局限性。 相似文献
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本文以能量守恒和质量守恒为基础建立了土壤耦合热泵U型埋管换热器三维非稳态模型,重点研究了土壤湿度对U型埋管换热器换热性能的影响.结果表明:土壤导热率主要取决于土壤湿度.随着土壤湿度的增长,不论单u或双u型管,钻石换热率都呈逐渐增长趋势,且增长幅度有所减弱,本文给出了其大致线性关系表达式,对土壤源热泵的实际施工有一定的指导意义. 相似文献
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采用试验的方法对不同几何尺寸下的多孔扁管内单相流体的流动与换热特性进行研究。将试验值与适用于常规尺寸通道的关联式计算值及W.M.Kays试验数据[1]作比较,分析微小尺寸对扁管流动与传热特性的影响。试验表明,大尺寸扁管的流动和传热特性与常规尺寸扁管的接近,但随着扁管当量直径减小,试验值就越偏离经典关联式的计算值。试验值在层流区比W.M.Kays数据偏小,但在过渡区和湍流区,试验值偏大。 相似文献
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基于板式蒸发器水蒸汽冷凝传热实验,对人字形板式蒸发器水蒸汽冷凝侧的传热和压降性能进行分析.使用两相流冷凝模型预测本文人字形板式蒸发器冷凝侧的换热性能,采用多元线性回归方法拟合得出人字形板式蒸发器水蒸汽冷凝换热实验关联式.实验得出沿单位长度人字形波纹板的压降随水蒸汽流速的增大不断增大;比较了几种人字形板式蒸发器水蒸汽冷凝侧压降模型,并拟合推导出摩擦因子与当量雷诺数的实验关联式. 相似文献
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利用高温相变材料储热，同时用重力热管作为能量传输元件这一高效偶合装置在生产和生活中有广阔的应用前景。本文在实验基础上建立一个考虑自然对流影响的相变传热与热管传热相偶合的传热模型，并对该模型进行数值计算，通过与实验结果进行的对比，证实所建立的模型与所采用的计算方法均是合理可行的。 相似文献
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基于Web的3D技术--X3D与VRML97 总被引：3，自引：0，他引：3
X3D是一种基于Web的交互式三维文件格式标准．相比它的前身——VRML97，X3D在多方面做了巨大改进．本文分析对比了X3D和VRML97两种标准各自的特点． 相似文献
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Particle physics is the study of the fundamental particles and their fundamental interactions.These fundamental particles are six quarks (u, d, s, c, b and t)and six leptons (e, ve, μ, vμ, τ, vτ) which serve as the blocks for building up the matter in the universe.The elementary theory of the particle physics - the Standard Model (SM)includes both the electroweak theories and the quantum chromodynamics (QCD).Breakthroughs in the electroweak theories have been awarded by three Nobel Prizes, and the QCD has been also honored with the Nobel Prize for its prediction about the particle interaction at high energy.However, the lattice QCD (LQCD) and other QCD effective theories for describing the interaction at lower energy still need to be experimentally tested. 相似文献
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《Information processing & management》2023,60(4):103372
The unsupervised 3D model retrieval is designed to joint the information of well-labeled 2D domain and unlabeled 3D domain to learn collaborative representations. Most existing methods adopted semantic alignment, but were inevitably affected by false pseudo-label. In this paper, we design a novel Instance-Prototype Similarity Consistency Network (IPSC) to guide domain alignment with similarity consistency, which can simultaneously suppress the impact of false pseudo-label information and well reduce the domain discrepancy. IPSC contains two similarity strategies, named Single instance vs Multiple prototypes and Instance-pair vs Single prototype. The first strategy utilizes a single instance as an anchor, and measures the similarities between the anchor and multiple prototypes with the same category but from different domains. The minimization between these similarities can better align the cross-domain prototypes with Kullback–Leibler (KL) divergence than traditional Euclidean similarities. The second strategy utilizes a single prototype as an anchor, and measures the similarities between this anchor and an instance-pair with the same category but from different domains. The minimization between these similarities can conduct the instance-level alignment with KL divergence, which can better suppress the negative effect of noisy pseudo-labels. We conduct various experiments on two datasets, MI3DOR-1 (21000 2D images and 7690 3D models) and MI3DOR-2 (19694 2D images and 3982 3D models), to verify the superiority of our algorithm. 相似文献
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《Information processing & management》2023,60(4):103351
Existing 2D image-based 3D model retrieval (IBMR) methods usually use the pseudo labels as semantic guidance to reduce the domain-wise and class-wise feature distribution difference across annotated 2D images and unlabeled 3D models. However, they cannot entirely guarantee the quality of pseudo labels, which will decrease prediction discriminability and diversity to affect feature distribution alignment. Therefore, we propose a novel unsupervised self-training correction learning (USTCL) network for the IBMR task. Specifically, we first utilize a CNN to encode 2D images and 3D models (described as multi-view images). Then, we design a noise-corrected self-training learning module (NCST) to denoise pseudo labels in an adversarial manner to make the predicted categories more easily discriminated to improve prediction discriminability. Besides, we employ a target-guided pseudo label refining strategy (TPLR) to progressively refine generated pseudo labels to prevent minority categories from being pushed into majority categories, thereby enhancing prediction diversity. Comprehensive experiments on popular IBMR benchmarks validate the effectiveness and robustness of USTCL, e.g., it can achieve the average gains of 54.20%/22.30%, 62.17%/43.21%, 54.21%/31.18%, 63.48%/43.28%, 59.87%/44.49% in terms of NN, FT, ST, F-measure, DCG and the decrease of 29.50%/35.15% corresponding to ANMRR on MI3DOR (21,000 2D images and 7,690 3D models) and MI3DOR-2 (19,694 2D images and 3,982 3D models), respectively. 相似文献
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