A dynamic bi-orthogonal field equation approach to efficient Bayesian inversion

This paper proposes a novel computationally efficient stochastic spectral projection based approach to Bayesian inversion of a computer simulator with high dimensional parametric and model structure uncertainty. The proposed method is based on the decomposition of the solution into its mean and a random field using a generic Karhunen–Loève expansion. The random field is represented as a convolution of separable Hilbert spaces in stochastic and spatial dimensions that are spectrally represented using respective orthogonal bases. In particular, the present paper investigates generalized polynomial chaos bases for the stochastic dimension and eigenfunction bases for the spatial dimension. Dynamic orthogonality is used to derive closed-form equations for the time evolution of mean, spatial and the stochastic fields. The resultant system of equations consists of a partial differential equation (PDE) that defines the dynamic evolution of the mean, a set of PDEs to define the time evolution of eigenfunction bases, while a set of ordinary differential equations (ODEs) define dynamics of the stochastic field. This system of dynamic evolution equations efficiently propagates the prior parametric uncertainty to the system response. The resulting bi-orthogonal expansion of the system response is used to reformulate the Bayesian inference for efficient exploration of the posterior distribution. The efficacy of the proposed method is investigated for calibration of a 2D transient diffusion simulator with an uncertain source location and diffusivity. The computational efficiency of the method is demonstrated against a Monte Carlo method and a generalized polynomial chaos approach

Performance Analysis of High Strength Pavement Quality Concrete with GGBS, Polypropylene Fiber and Silica Fume

This study investigates the presentation of high strength pavement quality concrete with preventive percentage of Silica fume and GGBS Focusing on Workability and mechanical properties. High strength pavement quality concrete (HSPQC) necessitates low water-to–cement ratio, high quality raw material, and the incorporation of mineral admixture and high-performance activities. The durability of high strength pavement quality concrete is crucial in structural design, being extensively used in rigid road construction due to its high-volume stability, compressive strength, and workability. In this paper an experimental investigation is done to determine the Suitability of GGBS and silica fume as a replacement of cement. Aiming to improve the concrete performance,the combine use of silica fume and polypropylene fiber and also GGBS and polypropylene fiber was investigated. While each of these materials contributes independently to improving concrete performance. The effect of adding polypropylene fiber with different shape and volume fractions on the compressive strength, modulus of rupture, flexural strength of concrete was investigated. Crimped and twisted polypropylene fiber were used with 0.6 % volume fraction. It was found that the compressive strength, flexural strength of concrete increased by adding 0.6 % volume fraction of polypropylene fiber. Pavement Concrete is a brittle material when it undergoes heavy load, crack will form and to reduce this improve high strength in concrete certain admixture are used. To produce high strength, concrete these ground granulated blast furnace slag and silica fume is used. it has a higher portion of the strength enhancing calcium silicate hydrates (CSH) than concrete made with Portland cement only and a reduced content of free lime which does not contribute to concrete strength, concrete made with GGBS continues to gain strength overtime, and has been shown to double its 28 days’ strength over periods of 10 to 12 years and silica fume made with concrete to gain early strength. The aim is to evaluate HSPQC containing supplementary cementitious material such as silica fume and GGBS, which are increasingly necessary in the construction industry. Effort to enhance concrete performance have been shown that replacing cement with material like GGBS and silica fume along with mineral admixture and chemical admixtures, can improve strength and durability

Attribute driven inverse materials design using deep learning Bayesian framework

AbstractMuch of computational materials science has focused on fast and accurate forward predictions of materials properties, for example, given a molecular structure predict its electronic properties. This is achieved with first principles calculations and more recently through machine learning approaches, since the former is computation-intensive and not practical for high-throughput screening. Searching for the right material for any given application, though follows an inverse path—the desired properties are given and the task is to find the right materials. Here we present a deep learning inverse prediction framework, Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS), for efficient and accurate prediction of molecules exhibiting target properties. We apply this framework to the computational design of organic molecules for three applications, organic semiconductors for thin-film transistors, small organic acceptors for solar cells and electrolyte additives with high redox stability. Our method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery.</jats:p

Knowledge and Attitude amongst the Dental and Medical students towards radiation hazards and radiation protection: A Questionnaire survey

Aim and objective: To assess the knowledge and attitudes regarding radiation hazards and protection amongst medical and dental students. Materials and method: A validated 20 point questionnaire about radiation protocol in the form of multiple choices was used for the study where 400 participants ( undergraduate students and interns) were included from medical and dental field. Results were analyzed using SPSS 20.0. Results: The knowledge, attitude and awareness about radiation protection was highest in dental interns followed by dental students, medical interns and medical students. Among the total participants, majority felt that lectures and workshops should be conducted to acquire knowledge on radiation hazards and protection. Conclusion: There is need “to fill” the knowledge deficit for students from both medical and dental fraternity thereby creating awareness about radiation hazards and protection. There is a need to educate current and future doctors regarding unnecessary exposure of individual to radiation.</jats:p