Simulations of Vapor/Ice Dynamics in a Freeze-Dryer Condenser

Abstract. Freeze-drying is a low-pressure, low-temperature condensation pumping process widely used in the manufacture of pharmaceuticals for removal of solvents by sublimation. Key performance characteristics of a freezedryer condenser are largely dependent on the vapor and ice dynamics in the low-pressure environment. The main objective of this work is to develop a modeling and computational framework for analysis of vapor and ice dynamics in such freeze-dryer condensers. The direct Simulation Monte Carlo (DSMC) technique is applied to model the relevant physical processes that accompany the vapor flow in the condenser chamber. Low-temperature water vapor molecular model is applied in the DSMC solver SMILE to simulate the flowfield structure. The developing ice front is tracked based on the mass flux computed at the nodes of the DSMC surface mesh. Verification of ice accretion simulations has been done by comparison with analytical free-molecular solutions. Simulations of ice buildup on the coils of a laboratory-scale dryer have been compared with experiments. The comparison shows that unsteady simulations are necessary to reproduce experimentally observed icing structures. The DSMC simulations demonstrate that by tailoring the condenser topology to the flow-field structure of the water vapor jet expanding into a low-pressure reservoir, it is possible to significantly increase water vapor removal rates and improve the overall efficiency of freeze-drying process

Simulations of Vapor/Ice Dynamics in a Freeze-Dryer Condenser

Abstract. Freeze-drying is a low-pressure, low-temperature condensation pumping process widely used in the manufacture of pharmaceuticals for removal of solvents by sublimation. Key performance characteristics of a freezedryer condenser are largely dependent on the vapor and ice dynamics in the low-pressure environment. The main objective of this work is to develop a modeling and computational framework for analysis of vapor and ice dynamics in such freeze-dryer condensers. The direct Simulation Monte Carlo (DSMC) technique is applied to model the relevant physical processes that accompany the vapor flow in the condenser chamber. Low-temperature water vapor molecular model is applied in the DSMCsolver SMILE to simulate the flowfield structure. The developing ice front is tracked based on the mass flux computed at the nodes of the DSMC surface mesh. Verification of ice accretion simulations has been done by comparison with analytical free-molecular solutions. Simulations of ice buildup on the coils of a laboratoryscale dryer have been compared with experiments. The comparison shows that unsteady simulations are necessary to reproduce experimentally observed icing structures. The DSMC simulations demonstrate that by tailoring the condenser topology to the flow-field structure of the water vapor jet expanding into a low-pressure reservoir, it is possible to significantly increase water vapor removal rates and improve the overall efficiency of freeze-drying process

Range Shortest Unique Substring queries

Let be a string of length n and be the substring of starting at position i and ending at position j. A substring of is a repeat if it occurs more than once in; otherwise, it is a unique substring of. Repeats and unique substrings are of great interest in computational biology and in information retrieval. Given string as input, the Shortest Unique Substring problem is to find a shortest substring of that does not occur elsewhere in. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over answering the following type of online queries efficiently. Given a range, return a shortest substring of with exactly one occurrence in. We present an -word data structure with query time, where is the word size. Our construction is based on a non-trivial reduction allowing us to apply a recently introduced optimal geometric data structure [Chan et al. ICALP 2018]

Efficient data structures for range shortest unique substring queries†

Let T[1, n] be a string of length n and T[i, j] be the substring of T starting at position i and ending at position j. A substring T[i, j] of T is a repeat if it occurs more than once in T; otherwise, it is a unique substring of T. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string T as input, the Shortest Unique Substring problem is to find a shortest substring of T that does not occur elsewhere in T. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α, β], return a shortest substring T[i, j] of T with exactly one occurrence in [α, β]. We present an O(n log n)-word data structure with O(logw n) query time, where w = Ω(log n) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an O(n)-word data structure with O(√ n logɛ n) query time, where ɛ > 0 is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012]

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

Simulations of Vapor/Ice Dynamics in a Freeze-Dryer Condenser

Freeze-drying is a low-pressure, low-temperature condensation pumping process widely used in the manufacture of pharmaceuticals for removal of solvents by sublimation. Key performance characteristics of a freeze-dryer condenser are largely dependent on the vapor and ice dynamics in the low-pressure environment. The main objective of this work is to develop a modeling and computational framework for analysis of vapor and ice dynamics in such freeze-dryer condensers. The direct Simulation Monte Carlow (DSMC) technique is applied to model the relevant physical processes that accompany the vapor flow in the condenser chamber. Low-temperature water vapor molecular model is applied in the DSMC solver SMILE to simulate the flowfield structure. The developing ice front is tracked based on the mass flux computed at the nodes of the DSMC surface mesh. Verification of ice accretion simulations has been done by comparison with analytical free-molecular solutions. Simulations of ice buildup on the coils of a laboratory-scale dryer have been compared with experiments. The comparison shows that unsteady simulations are necessary to reproduce experimentally observed icing structures. The DSMC simulations demonstrate that by tailoring the condenser topology to the flow-field structure of the water vapor jet expanding into a low-pressure reservoir, it is possible to significantly increase water vapor removal rates and improve the overall efficiency of freeze-drying process