Degradation Modeling of 2024 Aluminum Alloy During Corrosion Process

Corrosion is one of the most damaging mechanisms in aluminum alloys used in aerospace engineering structures. In this article, the degradation behavior of AA 2024-T3 as a function of time under corrosive conditions is studied through experiments and modeling. Corrosion experiments were conducted on AA 2024-T3 specimens under controlled electrochemical conditions. The chemical element alloy map was investigated through EDS technique for evaluation purposes. Based on the experimental data, an analytical model is developed relating the material loss to the degradation during the corrosion process. The analytical model uses genetic algorithms (GAs) to map the relationship through optimization. The results obtained from GAs were compared with a standard non-linear regression model. The results obtained indicate that a quadratic relationship exists in time between the material loss due to corrosion and the degradation behavior of the alloy. Based on the good results obtained, the present approach of degradation modeling can be extended to other metals.     

Joint range assignment and routing to conserve energy in wireless ad hoc networks

In wireless ad hoc networks, energy utilization is perhaps the most important issue, since it corresponds directly to the operational network lifetime. Topology Control (TC) is a well-known energy saving technique which tries to assign transmission ranges of nodes to optimize their energy utilization while keeping the network connected. In current TC schemes, the transmission range of each node is mostly accounted as the exclusive estimator for its energy consumption, while ignoring the amount of data it forwards. Especially when such schemes are coupled with the popular shortest path routing, they usually create a highly-loaded area at the center of the network in which nodes deplete their battery very quickly. In this paper, we introduce efficient strategies that take both load and range into account to handle this problem. We first consider the simple strategy in which a proper transmission range is computed for all nodes of the network to optimize their energy utilization under the presence of the shortest path routing. Inspiring from the results of this strategy, we then propose our combined strategy and argue that a combination of circular paths and shortest paths could result in a much better solution. We also provide detailed analytical models to measure the forwarding load and interference of nodes and then corroborate them with simulation results. Using the combined strategy, the achieved improvement in terms of traffic load, interference, and maximum energy consumption is about 50%, as compared with the simple strategy.     

Injuries due to handtools. Results of a questionnaire

A questionnaire was mailed to various federal and state agencies in the United States to determine the frequency, severity and annual cost of handtool-related injuries in industry and to identify problem areas with regard to tool type, accident type, nature of injury, parts of body affected, type of industry and characteristics of the injured worker. The responses of various state and regional agencies were tabulated and analysed. This paper summarises the findings.     

Significance of Capping Agents of Colloidal Nanoparticles from the Perspective of Drug and Gene Delivery,Bioimaging, and Biosensing: An Insight

The over-growth and coagulation of nanoparticles is prevented using capping agents by the production of stearic effect that plays a pivotal role in stabilizing the interface. This strategy of coating the nanoparticles’ surface with capping agents is an emerging trend in assembling multipurpose nanoparticles that is beneficial for improving their physicochemical and biological behavior. The enhancement of reactivity and negligible toxicity is the outcome. In this review article, an attempt has been made to introduce the significance of different capping agents in the preparation of nanoparticles. Most importantly, we have highlighted the recent progress, existing roadblocks, and upcoming opportunities of using surface modified nanoparticles in nanomedicine from the drug and gene delivery, bioimaging, and biosensing perspectives.     

Modeling exposure period for solar disinfection (SODIS) under varying turbidity and cloud cover conditions

Solar disinfection (SODIS) is widely practiced all around the world. The process requires variable exposure periods depending upon a number of process parameters (e.g., water turbidity, atmospheric temperature, and cloud cover conditions). This paper describes the development of a mathematical model to estimate required exposure period to achieve Fecal coliforms (FCs) removal for changing process parameters. Daily and hourly solar radiation were estimated and found to be suitable for SODIS application with intensity of 500 W/m² over a period of 3–5 h/day. Randomized SODIS experiments over a period of 3 years were conducted to consider seasonal and weather variations. Six samples each for five levels of turbidity (0, 5, 10, 20, and 30 NTU) were exposed to sunlight under variable cloud cover conditions on different days during the 3-year sampling period. Samples were collected and analyzed for remaining FCs at different intervals in each sampling day. Analysis of variance revealed that turbidity and percent of cloud cover are the most significant process parameters. It was found that FCs die-off in SODIS bottles followed the first-order kinetics. Different data sets were used for the development and calibration of the model. The calibrated model was further verified against the literature. Simple characteristics curves have also been established for practical application at household level to estimate exposure periods. The study revealed a significant difference between the required exposure periods for different turbidity and cloud cover conditions.     

Doping Induced Tailoring in the Morphology,Band-Gap and Ferromagnetic Properties of Biocompatible ZnO Nanowires,Nanorods and Nanoparticles

Nano-Micro Letters - The modification of nanostructured materials is of great interest due to controllable and unusual inherent properties in such materials. Single phase Fe doped ZnO...     

Bayesian inference and uncertainty quantification for hydrogen-enriched and lean-premixed combustion systems

Development of probabilistic modelling tools to perform Bayesian inference and uncertainty quantification (UQ) is a challenging task for practical hydrogen-enriched and low-emission combustion systems due to the need to take into account simultaneously simulated fluid dynamics and detailed combustion chemistry. A large number of evaluations is required to calibrate models and estimate parameters using experimental data within the framework of Bayesian inference. This task is computationally prohibitive in high-fidelity and deterministic approaches such as large eddy simulation (LES) to design and optimize combustion systems. Therefore, there is a need to develop methods that: (a) are suitable for Bayesian inference studies and (b) characterize a range of solutions based on the uncertainty of modelling parameters and input conditions. This paper aims to develop a computationally-efficient toolchain to address these issues for probabilistic modelling of NOx emission in hydrogen-enriched and lean-premixed combustion systems. A novel method is implemented into the toolchain using a chemical reactor network (CRN) model, non-intrusive polynomial chaos expansion based on the point collocation method (NIPCE-PCM), and the Markov Chain Monte Carlo (MCMC) method. First, a CRN model is generated for a combustion system burning hydrogen-enriched methane/air mixtures at high-pressure lean-premixed conditions to compute NOx emission. A set of metamodels is then developed using NIPCE-PCM as a computationally efficient alternative to the physics-based CRN model. These surrogate models and experimental data are then implemented in the MCMC method to perform a two-step Bayesian calibration to maximize the agreement between model predictions and measurements. The average standard deviations for the prediction of exit temperature and NOx emission are reduced by almost 90% using this method. The calibrated model then used with confidence for global sensitivity and reliability analysis studies, which show that the volume of the main-flame zone is the most important parameter for NOx emission. The results show satisfactory performance for the developed toolchain to perform Bayesian inference and UQ studies, enabling a robust and consistent process for designing and optimising low-emission combustion systems.