Uncertainty evaluation of calculated and measured kinetics parameters of Tehran Research Reactor

Effective delayed neutron fraction β_eff and neutron generation time Λ are important factors in reactor physics calculation and transient analysis. In the first stage of this research, these kinetics parameters have been calculated for two states of Tehran Research Reactor (TRR), i.e. cold (fuel, clad and coolant temperature 20 °C) and hot (fuel, clad and coolant temperature 65, 49 and 44 °C, respectively) states using MTR_PC computer code. The ratio of (β_eff)_i/(β_eff)_core plays an important role in reactivity accident analysis codes. This parameter and its contribution to effective delayed neutron fraction from each nucleus have been calculated in cold and hot reactor states. Uncertainty of effective delayed neutron fraction is evaluated in terms of following four quantities; basic delayed neutron constants, delayed neutron spectra, energy dependence of delayed neutron yield (ν_d) and fission cross-section of ²³⁵U and ²³⁸U. In the second stage, these parameters have been measured with an experimental method based on Inhour equation. The calculated and measured values are in good agreement. Relative Percent Errors (RPEs) are 2.8% for β_eff and 5.7% for Λ in the cold state.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Taguchi Optimization of Solvent-Antisolvent Crystallization to Prepare Ammonium Perchlorate Particles

The sphericity and size of ammonium perchlorate (AP) particles significantly influence the properties of composite propellants. As the AP particles become more spherical, the accumulation coefficient increases, the viscosity during casting decreases, and the particle loading and burning rate increase. Hence, the production of micronized AP particles with an average size between 1 and 20 μm is important to increase the loading percentage of AP in the composite propellant. Here, the Taguchi experimental design was used to optimize the solvent-antisolvent crystallization (SAC) process for the preparation of micronized AP particles with higher sphericity. SAC parameters such as the type of antisolvent, the solvent-to-antisolvent ratio, the antisolvent temperature, the stirring speed, and the retention time were investigated at four levels. The type of antisolvent and the solvent-to-antisolvent ratio were found to mainly contribute to improving the sphericity and size of the AP particles, respectively.     

Improving the energy production of roof-top solar PV systems through roof design

Building Simulation - Australia is receiving an average of 58 million PJ of solar radiation per year, which is about 1000 times larger than its total energy generation. Roof-top solar photovoltaic...     

Temperature/pH/magnetic triple‐sensitive nanogel–hydrogel nanocomposite for release of anticancer drug

Hydrogels, nanogels and nanocomposites show increasing potential for application in drug delivery systems due to their good chemical and physical properties. Therefore, we were encouraged to combine them to produce a new compound with unique properties for a long‐term drug release system. In this regard, the design and application of a nanocomposite hydrogel containing entrapped nanogel for drug delivery are demonstrated. To this aim, we first prepared an iron oxide nanocomposite nanogel based on poly(N‐isopropylacrylamide)‐co‐((2‐dimethylaminoethyl) methacrylate) (PNIPAM‐co‐PDMA) grafted onto sodium alginate (NaAlg) as a biocompatible polymer and iron oxide nanoparticles (ION) as nanometric base (PND/ION‐NG). This was then added into a solution of PDMA grafted onto NaAlg. Through dropwise addition of mixed aqueous solution of iron salts into the prepared polymeric solution, a novel hydrogel nanocomposite with excellent pH, thermal and magnetic responsivity was fabricated. The synthesized samples were fully characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy with energy‐dispersive X‐ray analysis, vibrating sample magnetometry and atomic force microscopy. A mechanism for the formation of PNIPAM‐co‐PDMA/NaAlg‐ION nanogel–PDMA/NaAlg‐ION hydrogel and PND/ION nanogel is suggested. Swelling capacity was measured at various temperatures (25 to 45 °C), pH values (from 2 to 11) and magnetic field and under load (0.3 psi) and the dependence of swelling properties of the nanogel–hydrogel nanocomposite on these factors was well demonstrated. The release rate of doxorubicin hydrochloride (DOX) as an anticancer drug was studied at different pH values and temperatures in the presence and absence of a magnetic field. The results showed that these factors have a high impact on drug release from this nanocomposite. The result showed that DOX release could be sustained for up to 12.5 days from these nanocomposite hydrogels, significantly longer than that achievable using the constituent hydrogel or nanogel alone (<1 day). The results indicated that the nanogel–hydrogel nanocomposite can serve as a novel nanocarrier for anticancer drug delivery. © 2019 Society of Chemical Industry     

Observer‐based adaptive optimal output containment control problem of linear heterogeneous Multiagent systems with relative output measurements

This paper develops a relative output‐feedback–based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed‐forward part, depending on the convex hull of the leaders' state. To comply with most real‐world applications, the feedback and feed‐forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off‐policy reinforcement learning algorithm on an actor‐critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.