Modeling of viscoelastic flows in terms of fractional calculus is an inquisitive area in engineering and industry. The physical models with fractional operators in nonNewtonian fluid mechanics represent more realistic behavior of flows involving nonlinear complex dynamics. The efficiency is because of freedom to choose derivative of any order in the mathematical formulation of the flows. Non-integer derivatives are important for the systems having hereditary behavior as they depend on the past conditions along with the local conditions. Viscoelastic fluids keep memory of old deformations and their behavior is related to these deformations. The fractional derivatives are more adequate in predicting the characteristics of viscoelastic fluids than the ordinary derivatives. In literature linear flow problems, with non-integer derivatives are solved by classical transforms methods. Unfortunately, most of the viscoelastic fluids unlike Newtonian fluids, are not characterized by only one relation. Mathematical equations, for the viscoelastic fluid flows are highly nonlinear in nature. In most of situations, highly nonlinear PDEs cannot be solved exactly, by existing techniques. Literature survey indicates, that appropriate consideration is not given to the numerical solutions, of anomalous nonlinear flow problems with noninteger derivatives. In this study, we have modeled the viscoelastic flow problems via fractional calculus approach and considered numerical techniques using finite difference approximations along with ”L1 algorithm”to discretize non-integer derivatives of time and finite element, discretization is used for space variables, in order to solve the governing fractional viscoelastic models. Finally we have predicted the behavior of viscoelastic fluids that can be used directly for the simulations of industrial processes.
گذشتہ مارچ میں دنیا کی سب سے مشہور ایکٹرس سارہ برنہارڈ کی ۷۴ سال کی عمر میں موت ہوئی۔ مرتے وقت اس نے اپنے کفن کے متعلق بھی دریافت کیا جو ۴۰ سال سے ہر وقت اس کے پاس رہتا تھا۔ (مئی ۱۹۲۳ء)
Sistem Pendukung Keputusan adalah bagian dari sistem informasi berbasis komputer termasuk sistem berbasis pengetahuan atau manajemen pengetahuan yang di pakai untuk mendukung pengambilan keputusan di dalam suatu organisasi atau perusahaan. Saat ini pengelolaan data penilaian karyawan perusahaan masih dilakukan dengan manual, sehingga semakin besar resiko kesalahan dalam mengelola data dan membutuhkan waktu yang relatif lama. Untuk mempermudah perhitungan penentuan kinerja karyawan terbaik maka penulis menggunakan metode Simple Additive Weighting (SAW). Metode simple additive weighting ini di pilih karena metode ini menentukan nilai bobot untuk setiap atribut, kemudian dilanjutkan dengan proses perangkingan yang akan menyeleksi alternatif-alternatif yang sudah di tentukan seperti etika atau kepribadian, kedisplinan, absensi, tanggung jawab, kerja sama, kemampuan memimpin, kecepatan kerja, ketelitian kerja dan kualitas hasil kerja. Dengan metode perangkingan tersebut, diharapkan penilaian akan lebih tepat karena didasarkan pada nilai kriteria dan bobot yang sudah ditentukan sehingga akan mendapatkan hasil yang lebih akurat terhadap siapa yang akan menerima reward/penghargaan tersebut.
Intuitionistic fuzzy sets, neutrosophic sets, rough sets and soft sets are different mathematical tools to deal the problem of how to understand and manipulate imperfect knowledge. The intuitionistic fuzzy rough framework and soft rough neutrosophic framework are made by combining these models, which are more flexible and expressive for modeling and processing incomplete information in information systems. The notions of intuitionistic fuzzy rough sets and soft rough neutrosophic sets are applied to graph theory in this thesis. Novel hybrids models, namely, intuitionistic fuzzy rough graphs and soft rough neutrosophic influence graphs are introduced. Certain methods of their construction are developed. Further, efficient algorithms are developed to solve decision-making problems. The time complexity of the algorithms is computed. Moreover, MATLAB coding of the algorithms is given for the implementation of the proposed hybrid models.