Stability aspect of magnetized hybrid nanofluid with suction and injection phenomenon: Modified thermal model

Nanoparticles with ultra-high thermal efficiency and stability have dynamic applications in numerous eras of thermal sciences, including energy production, heat transmission devices, cooling and heating systems, manufacturing applications, aircraft, and solar energy, among others. This study's primary objective is to investigate the mathematical modeling using a Tiwari and Das nanofluid model, taking into account the effects of magnetic, suction/injection, and thermal radiation, as well as the stability analysis of a hybrid nanofluid containing copper ($Cu$) and alumina ($A{l}_2{O}_3$) nanoparticles in a water-based liquid. Using similarity transformations, self-similarity solutions of the system of governing ordinary differential equations (ODEs) were obtained, and the resulting ODEs were simulated using implementations of the three-stage Lobatto IIIa technique. The numerical results indicate that the energy characteristics such as thermal conductivity increase rapidly when copper nanomaterials are used. It is also noticed that the combination of both nano-materials results in an excellent energy enhancement. For the solution validation, novel stability performances for the obtained simulations are determined.     

Ion-selectivity of plasticizers in poly(vinyl chloride) membrane electrodes

Electrode membranes were presented which contained only PVC and a plasticizer. The plasticizers studied were tris(2-ethylhexyl)phosphate, 2-nitrophenyl octyl ether and bis(1-butylpentyl)adipate. The response and selectivity of these ligand-free PVC electrodes towards alkali and alkaline earth cations are reported.     

Penalized Estimation in Large-Scale Generalized Linear Array Models

Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package glamlasso. It combines several ideas—previously considered separately—to obtain sparse estimates while at the same time efficiently exploiting the GLAM structure. In this article, the convergence of the algorithm is treated and the performance of its implementation is investigated and compared to that of glmnet on simulated as well as real data. It is shown that the computation time for glamlasso scales favorably with the size of the problem when compared to glmnet. Supplementary materials, in the form of R code, data and visualizations of results, are available online.     

Optimization of operating strategies in a community solar heating system

Minimization of auxiliary energy costs is discussed for heating in a district solar heating system with effective heat storage. The minimization problem is approached by dynamic programming which gives an optimal operating strategy for the auxiliary energy system. The effects of different pricing schemes of auxiliary energy (electricity) have been studied. The results show that with an adequate heat storage capacity, the optimization of the auxiliary energy use in a community solar heating system may lead to considerable cost savings.