Simulated Microstructural Evolution and Design of Porous Sintered Wicks

Porous structures formed by sintering of powders, which involves material-bonding under the application of heat, are commonly employed as capillary wicks in two-phase heat transport devices such as heat pipes. These sintered wicks are often fabricated in an ad hoc manner, and their microstructure is not optimized for fluid and thermal performance. Understanding the role of sintering kinetics—and the resulting microstructural evolution—on wick transport properties is important for fabrication of structures with optimal performance. A cellular automaton model is developed in this work for predicting microstructural evolution during sintering. The model, which determines mass transport during sintering based on curvature gradients in digital images, is first verified against benchmark cases, such as the evolution of a square shape into an areapreserving circle. The model is then employed to predict the sintering dynamics of a sideby- side, two-particle configuration conventionally used for the study of sintering. Results from previously published studies on sintering of cylindrical wires are used for validation. Randomly packed multiparticle configurations are then considered in two and three dimensions. Sintering kinetics are described by the relative change in overall surface area of the compact compared to the initial random packing. The effect of sintering parameters, particle size, and porosity on fundamental transport properties, viz., effective thermal conductivity and permeability, is analyzed. The effective thermal conductivity increases monotonically as either the sintering time or temperature is increased. Permeability is observed to increase with particle size and porosity. As sintering progresses, the slight increase observed in the permeability of the microstructure is attributed to a reduction in the surface area

3D Reconstruction and Design of Porous Media from Thin Sections

Characterization and design of fluid-thermal transport through random porous sintered beds is critical for improving the performance of two-phase heat transport devices such as heat pipes. Two-dimensional imaging techniques are quite well developed and commonly employed for microstructure and material characterization. In this study, we employ 2D image data (thin sections) for measuring critical microstructural features of commercial wicks for use in correlation-based prediction of transport properties. We employ a stochastic characterization methodology based on the two-point autocorrelation function, and compare the predicted properties such as particle and pore diameters and permeability with those from our previously published studies, in which 3D x-ray microtomography data was employed for reconstruction. Further, we implement a reconstruction technique for reconstructing a three-dimensional stochastic equivalent structure from the thin sections. These reconstructed domains are employed for predicting effective thermal conductivity, permeability and interfacial heat transfer coefficient in single-phase flow. The current computations are found to compare well with models and correlations from the literature, as well as our previous numerical studies. Finally, we propose a new parametrized model for the design of porous materials based on the nature of the two-point autocorrelation functions. Using this model, we reconstruct sample three-dimensional microstructures, and analyze the influence of various parameters on fluid-thermal properties of interest. With advances in additive manufacturing techniques, such an approach may eventually be employed to design intricate porous structures with properties tailored to specific applications

COMBINED ANALYSE TRUST SECURE ROUTING STRATEGY AND BUILD A GENTIC FUZY BASED MODEL FOR WIRELESS SENSOR NETWORKS

In recent times, the development sensors wirelessly have been an essential requirement for cuttingedgeareas like environment surveillance and intelligent cities. En-route filtering systems are mainlyfocused on energy efficiency by preventing fraudulent report injection attacks. However, the lifetimeof networks is typically neglected. They also have fixed path routing as well as fixed responses toattacks. Additionally, the hot-spot issue is regarded as one of the biggest problems in the extension ofnetwork life. In this paper, we propose an algorithmic basis for a fuzzy genetic optimized re-clusteringtechnique to overcome these restrictions and thus reduce the impact of the hot-spot issue. This fuzzyalgorithm is employed to analyze the network conditions. In re-clustering key issue is when toconduct the subsequent clustering. To determine the exact time of the next clustering (i.e. the energydraining from the nodes to zero) as well as the fuzzy member functions are optimized by using a thegenetic algorithm. Simulation tests verify the proposed algorithm. It has shown that the network'slifetime can be extended by up to 3.64 times while maintaining the detection capability and energyefficiency

Resistance network-based thermal conductivity model for metal foams

A network model for the estimation of effective thermal conductivity of open-celled metal foams is pre-sented. A nodal network representation of three aluminum foam samples from DUOCEL – 10 ppi, 20 ppi and 40 ppi – is constructed out of X-ray microtomography data obtained by computed tomography (CT) scanning of the samples using a commercial CT scanner. Image processing and 3D skeletonization are performed with commercially available image processing software. The effective thermal conductivity is estimated through a 1D conduction model, representing individual ligaments as an effective thermal resistance using the topological information from the scan data. The effective thermal conductivity data thus obtained are compared with the Lemlich theory and other pore-based models. Further, microstruc-tural characterization of foam features – pore size, ligament thickness, ligament length and pore shapes – is performed. All the three foam samples are observed to have similar pore shapes and volumetric poros-ity, while the other features scale with the pore size. For a given porosity the computed permeability is found to scale as the square of the pore diameter, as also noted by previous researchers

Optimization Under Uncertainty Applied to Heat Sink Design

Optimization under uncertainty (OUU) is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, yields output uncertainties that help the designer choose appropriate values for input parameters to produce safe designs. Apart from providing basic statistical information, such as mean and standard deviation in the output quantities, uncertainty-based optimization produces auxiliary information, such as local and global sensitivities. The designer may thus decide the input parameter(s) to which the output quantity of interest is most sensitive, and thereby design better experiments based on just the most sensitive input parameter(s). Another critical output of such a methodology is the solution to the inverse problem, i.e., finding the allowable uncertainty (range) in the input parameter(s), given an acceptable uncertainty (range) in the output quantities of interest. We apply optimization under uncertainty to the problem of heat transfer in fin heat sinks with uncertainties in geometry and operating conditions. The analysis methodology is implemented using DAKOTA, an open-source design and analysis kit. A response surface is first generated which captures the dependence of the quantity of interest on inputs. This response surface is then used to perform both deterministic and probabilistic optimization of the heat sink, and the results of the two approaches are compared

Numerical Investigation of Pressure Drop and Heat Transfer through Reconstructed Metal Foams and Comparison against Experiments

Direct numerical simulation of transport in foam materials can benefit from realistic representations of the porous-medium geometry generated by employing non-destructive 3D imaging techniques. X-ray microtomography employs computer-processed X-rays to produce tomographic images or slices of specific regions of the object under investigation, and is ideally suited for imaging opaque and intricate porous media. In this work, we employ micro-CT for numerical analysis of air flow and convection through four different high-porosity copper foams. All four foam samples exhibit approximately the same relative density (6.4% - 6.6% solid volume fraction), but have different pore densities (5, 10, 20, and 40 pores per inch, PPI). A commercial micro-computed tomography scanner is employed for scanning the 3D microstructure of the foams at a resolution of 20 μm, yielding stacks of two-dimensional images. These images are processed in order to reconstruct and mesh the real, random structure of the foams, upon which simulations are conducted of forced convection through the pore spaces of the foam samples. The pressure drop values from this μCT based CFD analysis are compared against prior experimental results; the computational interfacial heat transfer results are compared against the values predicted by an empirical correlation previously reported, revealing excellent agreement between the numerical and experimental/empirical hydraulic and thermal results, thus highlighting the efficacy of this novel approach

Phase I clinical trials in patients with advanced non-small cell lung cancer treated within a Drug Development Unit: What have we learnt?

Objectives Despite advances in novel drug development for patients with advanced non-small cell lung cancer (NSCLC), there are still only a limited number of approved treatments. We therefore evaluated the clinical outcomes of patients with advanced NSCLC referred to a dedicated phase I clinical trials unit assessed baseline clinical factors associated with successful enrollment onto phase I trials.Material and methods We conducted a retrospective study involving patients with advanced NSCLC referred to the Drug Development Unit at the RMH between January 2005 and December 2013.Results 257 patients with advanced NSCLC were referred for consideration of phase I trials, of which only 89 (35%) patients successfully commenced phase I trials. The commonest reasons for not entering study included poor ECOG performance status and rapid disease progression. A multivariate analysis identified that ECOG performance status (0-1) and RMH prognostic score (0-1) were associated with successful enrollment onto phase I trials (p<0.001). Single agent therapies included novel agents against the phosphatidylinositol-3 kinase pathway, insulin growth factor-1 receptor and pan-HER family tyrosine kinases. These trial therapies were well tolerated and mainly associated with grade 1-2 adverse events, with a minority experiencing grade 3 toxicities. Nine (10%) patients, 4 with known EGFR or KRAS mutations, achieved RECIST partial responses. Median time to progression was 2.6 months and median overall survival was 8.1 months for patients enrolled.Conclusions Phase I trial therapies were generally well tolerated with potential antitumor benefit for patients with advanced NSCLC. Early referral to drug development units at time of disease progression should be considered to enhance the odds of patient participation in these studies

Identity Leadership, Employee Burnout and the Mediating Role of Team Identification: Evidence from the Global Identity Leadership Development Project

Do leaders who build a sense of shared social identity in their teams thereby protect them from the adverse effects of workplace stress? This is a question that the present paper explores by testing the hypothesis that identity leadership contributes to stronger team identification among employees and, through this, is associated with reduced burnout. We tested this model with unique datasets from the Global Identity Leadership Development (GILD) project with participants from all inhabited continents. We compared two datasets from 2016/2017 (n = 5290; 20 countries) and 2020/2021 (n = 7294; 28 countries) and found very similar levels of identity leadership, team identification and burnout across the five years. An inspection of the 2020/2021 data at the onset of and later in the COVID-19 pandemic showed stable identity leadership levels and slightly higher levels of both burnout and team identification. Supporting our hypotheses, we found almost identical indirect effects (2016/2017, b = −0.132; 2020/2021, b = −0.133) across the five-year span in both datasets. Using a subset of n = 111 German participants surveyed over two waves, we found the indirect effect confirmed over time with identity leadership (at T1) predicting team identification and, in turn, burnout, three months later. Finally, we explored whether there could be a “too-much-of-a-good-thing” effect for identity leadership. Speaking against this, we found a u-shaped quadratic effect whereby ratings of identity leadership at the upper end of the distribution were related to even stronger team identification and a stronger indirect effect on reduced burnout