Diffusion behavior and distribution regulation of MgO in MgO-bearing pellets

In this paper, the diffusion behavior between MgO and Fe2O3（the main iron oxide in pellets） is investigated using a diffusion couple method. In addition, the distribution regulation of MgO in MgO-bearing pellets is analyzed via pelletizing experiments. The results illustrate that MgO is prone to diffuse into Fe2O3 in the form of solid solution; the diffusion rate considered here is 13.64 μm·min^（-1）. Most MgO content distributes in the iron phase instead of the slag phase. The MF phase {（Mg（1-x）Fex）O·Fe2O3, x ≤ 1} is generated in the MgO-bearing pellets. However, the distribution of MgO in the radial direction of the pellets is inconsistent. The solid solution portion of MgO in the MF phase is larger in the outer layer of the pellets than in the inner layer. In this work, the approximate chemical composition of the MF phase in the outer layer of the pellets is {（Mg（0.35-0.77）·Fe（0.65-0.23）） O·Fe2O3} and in the inner layer is {（Mg（0.13-0.45）·Fe（0.87-0.55））O·Fe2O3}.

Effects of Distribution of Induced Defects on Positron Diffusion

The effects of distribution of induced defects on the positron diffusion was studied by using the point-source diffusion model and 6 function method.

Reconstruction of 3D light distribution produced by cylindrical diffuser in deep tissues based on photoacoustic imaging

Measurement of light distribution in biological tissue contributes to selecting strategy and optimizing dose for biomedical application. In this letter, a photoacoustic method combined with Monte Carlo simulation was used to estimate the three-dimensional light distribution in biological tissue. The light distribution was produced by a cylindrical diffuser which interposed into tissues. The light profiles obtained by the method were compared to those detected by photo diodes. The experimental results demonstrate the feasibility of this method. The approach can play a significant role for photo-dosimetry in biomedical phototherapy.

Distributed adaptive direct position determination based on diffusion framework

The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.

Effect of temperature on microstructure of molybdenum diffusion coating on titanium substrate

The halide-activated pack cementation process was used to form molybdenum diffusion coating on titanium substrate. The morphology, structure, elements diffusion distribution and microhardness of the coatings formed at different diffusion temperatures were studied. The results indicate that the coating is made up of deposition layer and diffusion layer, and the surface roughness of specimens is increased after diffusion. In the diffusion layer, the major phases are Mo and β-Ti phase with addition of α′-Ti phase and α″-Ti phase. And the phase composition of Mo→β→α″→α′ is formed for different Mo contents in the diffusion layer from outside to inside. The diffusion of Ti element is very obvious as well as Mo element. With increasing the diffusion temperature, the thickness of diffusion layer is increased rapidly, and the microhardness is changed more smoothly with diffusion depth, which shows the same distribution rules as the Mo content.

Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticle size distribution measurements

Most conventional aerosol neutralizers are based on radioactive sources, which are controlled by strict regulations restricting their handling, transport, and storage. The TSI 3087 soft X-ray （SXR） neutralizer circumvents these legal restrictions. The aim of the present work is to compare the performance of a standalone SXR aerosol neutralizer with that of conventional radioactive aerosol neutralizers based on 85Kr （TSI 3077） and 241Am （Grimm 5522） by performing field tests in a real environmental scenario. The results obtained when the SXR neutralizer was connected to a mobility particle sizer spectrometer （MPS）, different from the device suggested by the manufacturer, were comparable with those obtained with the use of radioactive aerosol neutralizers. In changing the neutralizer, the particle number concentrations, measured with the MPS connected to the SXR neutralizer, almost remained within the 10% uncertainty bounds for the particle size interval 10-300 nm, when diffusion losses inside the SXR tube were considered. Based on our comparisons, the SXR neutralizer can be regarded as a standalone instrument that could solve the problems associated with legal restrictions on radioactive neutralizers and fulfil the need for a portable instrument for different field test purposes.

Effects of Ni on austenite stability and fracture toughness in high Co-Ni secondary hardening steel

Three kinds of high Co Ni secondary hardening steels with different Ni contents were studied. The nanoscale austenite layers formed at the interface of matensite laths were observed. Both observation and diffusion kinetic simulation results showed that both Ni and Co did not obtain enough time to get the equilibrium content in this system. The Ni content in austenite layers decreased significantly, and Co content increased slightly with the decrease of Ni content in overall composition. The austenite stability was estimated by Olson-Cohen model, in which both chemical and mechanical driving force could be calculated by equilibrium thermodynamic and Mohr＇s circle methods, respectively. Simulation and mechanical test results showed that The decrease of Ni content in austenite layers would cause the change of austenite stability and decrease the fracture toughness of the steels. When the Ni content in the overall composition was lower than 7 wt. %, the Ni content in y phase would be lower than 20 wt.%. And the simulation value of M; （stress induced critical martensite transformation temperature） would be up to 80 ℃, which was about 60℃ higher than room temperature. Based on the analysis, the Ni content in the overall composition of high Co Ni secondary hardening steels should be higher than 8 wt. % in order to obtain a good fracture toughness.

On the development of cat swarm metaheuristic using distributed learning strategies and the applications

Purpose–The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems.The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations.In addition,the models are tested for nonlinear systems with different noise conditions.In a nutshell,the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.Design/methodology/approach–Population-based evolutionary algorithms such as genetic algorithm(GA),particle swarm optimization(PSO)and cat swarm optimization(CSO)are implemented in a distributed form to address the system identification problem having distributed input data.Out of different distributed approaches mentioned in the literature,the study has considered incremental and diffusion strategies.Findings–Performances of the proposed distributed learning-based algorithms are compared for different noise conditions.The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate,but incremental CSO is slightly superior to diffusion CSO.Originality/value–This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems.Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.