Heat transfer enhancement using double taper microchannel

A three-dimensional numerical study on the combined effect of height as well as width tapering on the thermal performance of double taper microchannel is presented in this paper. The channel inlet width is considered as 300 µm, taper ratio on sidewalls and bottom wall are varied from 0 to 1 and 1 to 3.9, respectively. The thermal resistance ratio, average bottom wall temperature, temperature difference ratio, and pumping power ratio of the channel are evaluated for various flow rates, height, and width tapering. Results showed higher reduction of wall temperature with combined effect height as well as width tapering compared with straight channel. The optimal size of the micro channel to minimize the pumping power and average wall temperature on the constraint of heat flux and footprint area is found. The reduction in average bottom wall temperature is 17.34%, and pumping power ratio is 0.44 (56% power reduction) noted, respectively, at Reynolds number 340. Finally, optimal dimension of double taper microchannel is evaluated for better thermo-hydraulic performance.

Residential density classification for sustainable housing development using a machine learning approach

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here we develop a methodology using a Random Forests algorithm (in combination with GIS spatial data processing) to detect and classify the residential neighbourhoods and their spatial characteristics within the region between Oxford and Cambridge, that is, the ‘Oxford-Cambridge Arc’. The classification model is based on four pre-defined urban classes, that is, Centre, Urban, Suburban, and Rural for the entire region. The resolution is a grid of 500 m × 500 m. The features for classification include (1) dwelling geometric attributes (e.g. garden size, building footprint area, building perimeter), (2) street networks (e.g. street length, street density, street connectivity), (3) dwelling density (number of housing units per hectare), (4) building residential types (detached, semi-detached, terraced, and flats), and (5) characteristics of the surrounding neighbourhoods. The classification results, with overall average accuracy of 80% (accuracy per class: Centre: 38%, Urban 91%, Suburban 83%, and Rural 77%), for the Arc region show that the most important variables were three characteristics of the surrounding area: residential footprint area, dwelling density, and number of private gardens. The results of the classification are used to establish a baseline for the current status of the residential neighbourhoods in the Arc region. The results bring data-driven decision-making processes to the level of local authority and policy makers in order to support sustainable housing development at the regional scale.

Experimental Analysis and Modelling of a Novel Thermosyphon System for Electronics Cooling

In an era of ever-growing digitalisation, the absorbed power of processing units is becoming an actual challenge for cooling systems. The effectiveness is imperative, but compactness and passiveness are driving factors in the design as well. The goal of the present paper is twofold: 1) to present a detailed experimental campaign on a thermosyphon system for high-heat-load electronics; 2) to propose a model of the thermosyphon system using a Machine Learning approach. The thermosyphon system is composed of a micro-channel evaporator plate directly attached to the heat-generating device and an air-cooled multiport condenser. The height between the evaporator and condenser inlets is 12 cm. The condenser is also proposed in two solutions: the first one has a footprint heat exchange area of 180 x 120 mm2, which allows a single fan's placement; the second one has a footprint area of 240x120 mm2, allowing the placement of two fans. The working fluid used in the system is R1234ze(E) with different charges. The experimental results show that the single-fan condenser reached a maximum heat rejection of 330 W, corresponding to a heat flux of 21.9 W/cm2. The double-fan condenser bore a maximum heat rejection of 570 W (37.7 W/cm2). The model, constructed purely via a Machine Learning tool, shows a very satisfactory agreement between experimental and predicted data.

Changes in granule mobility and age contribute to changes in insulin secretion after desensitization or rest

IntroductionFunctional impairment of the stimulus secretion coupling in pancreatic beta cells is an essential component of type 2 diabetes. It is known that prolonged stimulation desensitizes the secretion of insulin and thus contributes to beta cell dysfunction. Beta cell rest, in contrast, was shown to enhance the secretory response. Here, the underlying mechanisms were investigated.Research design and methodsTo characterize the consequences of desensitization or rest for the number and mobility of submembrane granules, insulin-secreting MIN6 cells were desensitized by 18-hour culture with 500 µM tolbutamide or rested by 18-hour culture with 1 µM clonidine. The granules were labeled by hIns-EGFP or hIns-DsRed E5, imaged by TIRF microscopy of the cell footprint area and analyzed with an observer-independent program. Additionally, the insulin content and secretion were measured.ResultsConcurrent with the insulin content, submembrane granules were only slightly reduced after desensitization but markedly increased after rest. Both types of pretreatment diminished arrivals and departures of granules in the submembrane space and increased the proportion of immobile long-term resident granules, but desensitization lowered and rest increased the number of exocytoses, in parallel with the effect on insulin secretion. Labeling with hIns-DsRed E5 (‘timer’) showed that desensitization did not affect the proportion of aged granules, whereas rest increased it. Aged granules showed a high mobility and made up only a minority of long-term residents. Long-term resident granules were more numerous after rest and had a lower lateral mobility, suggesting a firmer attachment to the membrane.ConclusionThe number, mobility and age of submembrane granules reflect the preceding functional states of insulin-secreting cells. Representing the pool of releasable granules, their quantity and quality may thus form part of the beta cell memory on renewed stimulation.

Self-Excited Full-Vehicle Oscillations Caused by Tire–Road Interaction: Virtual and Real-World Experimental Investigation

ABSTRACT In this article, self-excited full-vehicle oscillations (power-hops) are introduced. Initially, results of full-vehicle measurements are shown followed by the presentation of a specially build test rig (longitudinal dynamics test rig). Subsequently, these oscillations are investigated by using simulation-based tools within multibody simulation–related full-vehicle modeling. Tire–road interaction is evaluated in this process either by characteristic curves or by a proprietary quasistatic tire model that returns overall tangential forces by evaluating the state of every discretized element within the footprint area.

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment of Area, Accuracy, and Agreement at Thirty Refugee Settlements in Uganda

Satellite-based broad-scale (i.e., global and continental) human settlement data offer foundational information for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology, and demographic modeling. While many human settlement products report exceptional detection accuracies above 85%, there is a substantial blind spot in that product validation is typically centered on large urban areas rather than rural, small-scale settlements that are home to 3.4 billion people. In this study, we make use of a data-rich collection of 30 refugee settlements in Uganda to produce a targeted assessment of small-scale settlement detection by four broad-scale human settlement products: Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), World Settlement Footprint (WSF), High Resolution Settlement Layer (HRSL), and Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). We measured each product’s areal coverage within refugee settlements, assessed product detection accuracies in comparison to an independent dataset of 317,416 refugee settlement building footprints, and examined agreement between products. For refugee settlements established before 2016, the human settlement products had a low median F1-Score (F1) of 0.24, a high median false alarm rate of 0.59, and tended to only agree at locations of highest building density. Individually, WSF entirely overlooked 8 of the 30 study refugee settlements (median F1=0.17); GHS-BUILT-S2 underestimated the building footprint area by a median 50% (F1=0.15); GRID3 overestimated the building footprint area by a median 280% (F1=0.38); and HRSL underestimated the median area by 7% (F1=0.34). All products suffer from low detection accuracy and high false alarm rates, but GRID3 and HRSL, based on 0.5 meter resolution imagery, offer better detection accuracy than GHS-BUILT S2 and WSF, which are based on 10-30 meter resolution imagery. These results show that human settlement products have far to go in providing an accurate depiction of small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validation of broad-scale human settlement detection.