Numerical analysis of Lattice Boltzmann Methods for the heat equation on a bounded interval

Lattice Boltzmann methods are a promising approach for the numerical solution of fluid-dynamic problems. We consider the one-dimensional Goldstein-Taylor model with the aim to answer some of the questions concerning the numerical analysis of lattice Boltzmann schemes. Discretizations for the solution of the heat equation are presented for a selection of boundary conditions. Stability and convergence of the solutions are proved by employing energy estimates and explicit Fourier representations

Numerical analysis of Lattice Boltzmann Methods for the heat equation on a bounded interval

Lattice Boltzmann methods are a promising approach for the numerical solution of fluid-dynamic problems. We consider the one-dimensional Goldstein-Taylor model with the aim to answer some of the questions concerning the numerical analysis of lattice Boltzmann schemes. Discretizations for the solution of the heat equation are presented for a selection of boundary conditions. Stability and convergence of the solutions are proved by employing energy estimates and explicit Fourier representations

High-performance and hardware-aware computing: proceedings of the second International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2711), San Antonio, Texas, USA, February 2011 ; (in conjunction with HPCA-17)

High-performance system architectures are increasingly exploiting heterogeneity. The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full hardware potential if all features on all levels are taken into account in a holistic approach

High-performance and hardware-aware computing: proceedings of the first International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2708)

The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full hardware potential if all features on all levels are taken into account in a holistic approach

A longitudinal framework for predicting nonresponse in panel surveys

Nonresponse in panel studies can lead to a substantial loss in data quality due to its potential to introduce bias and distort survey estimates. Recent work investigates the usage of machine learning to predict nonresponse in advance, such that predicted nonresponse propensities can be used to inform the data collection process. However, predicting nonresponse in panel studies requires accounting for the longitudinal data structure in terms of model building, tuning, and evaluation. This study proposes a longitudinal framework for predicting nonresponse with machine learning and multiple panel waves and illustrates its application. With respect to model building, this approach utilizes information from multiple waves by introducing features that aggregate previous (non)response patterns. Concerning model tuning and evaluation, temporal cross-validation is employed by iterating through pairs of panel waves such that the training and test sets move in time. Implementing this approach with data from a German probability-based mixed-mode panel shows that aggregating information over multiple panel waves can be used to build prediction models with competitive and robust performance over all test waves

Predicting nonresponse in future waves of a probability-based mixed-mode panel with machine learning

Nonresponse in panel studies can lead to a substantial loss in data quality owing to its potential to introduce bias and distort survey estimates. Recent work investigates the usage of machine learning to predict nonresponse in advance, such that predicted nonresponse propensities can be used to inform the data collection process. However, predicting nonresponse in panel studies requires accounting for the longitudinal data structure in terms of model building, tuning, and evaluation. This study proposes a longitudinal framework for predicting nonresponse with machine learning and multiple panel waves and illustrates its application. With respect to model building, this approach utilizes information from multiple waves by introducing features that aggregate previous (non)response patterns. Concerning model tuning and evaluation, temporal crossvalidation is employed by iterating through pairs of panel waves such that the training and test sets move in time. Implementing this approach with data from a German probability-based mixed-mode panel shows that aggregating information over multiple panel waves can be used to build prediction models with competitive and robust performance over all test waves

Development of a 10 kW class axial impulse single stage turboexpander for a micro-CHP ORC unit

Development of micro ORC systems with 1-15 kW power output for micro-cogeneration and waste heat recovery at the Czech Technical University in Prague, University Centre for Energy Efficient Buildings (CTU UCEEB) has over ten years of history with many successes. These include 6 different ORC units, all with in-house designed rotary vane expanders (RVE) of many versions throughout this development. Among main advantages of the RVE belong relatively simple and robust design at low cost even at very small series of single-unit production and all that with acceptable efficiency. The ORC units operate with hexamethyldisiloxane (MM) working fluid at high pressure ratios and expansion ratios and the isentropic efficiency of RVE has a limit at these conditions around 60%, often however only at values around 50%. While this might be enough on a cost side for commercialization of this technology, in pursuit of higher efficiency solutions, different expander technology needs to be selected. A turbo-expander is a logical choice with prospect of higher efficiency. At the same time, a literature review has found a lack of reported detailed experimental data for micro (5-50 kW) turbo-expanders, possibly hindering global development towards economically feasible solutions. A project named Dexpand, “Optimised expanders for small-scale distributed energy systems” aims at these issues by objectives in designing, optimizing, manufacturing and testing several ORC expanders with MM and isobutane and their subsequent performance mapping and comparison. One major task is a design of a turboexpander for a 120 kWth biomass fired microcogeneration ORC unit currently operated at the CTU UCEEB. An axial impulse single stage turboexpander was selected as a suitable choice, providing a prospect of a decent efficiency at technically manageable rotational speed and size. This paper provides a detail of currently performed design activities, starting from boundary conditions specification, over development and optimization of a 1D model, preliminary 2D CFD calculations and finishing in a state of a robust and detailed 3D CFD model with a real gas model. Note that the working fluid, high molar mass organic vapour, is highly non-ideal in its behaviour and the flow conditions with pressure design ratio around 13 is highly supersonic (nozzle outlet isentropic Mach number exceeds 2). The current results based on 3D CFD indicate a prospect of an isentropic efficiency 71% at mechanical power output of 11 kW. Lastly, ongoing and future work is outlined, which includes aerodynamic optimization based on the developed 3D CFD model and construction design of the entire turbine assembly