Development and Validation of a Thermo-Economic Model for Design Optimisation and Off-Design Performance Evaluation of a Pure Solar Microturbine

The aim of this paper is to present a thermo-economic model of a microturbine for solar dish applications, which demonstrates the applicability and accuracy of the model for off-design performance evaluation and techno-economic optimisation purposes. The model is built using an object-oriented programming approach. Each component is represented using a class made of functions that perform a one-dimensional physical design, off-design performance analysis and the component cost evaluation. Compressor, recuperator, receiver and turbine models are presented and validated against experimental data available in literature, and each demonstrated good accuracy for a wide range of operating conditions. A 7-kWe microturbine and solar irradiation data available for Rome between 2004 and 2005 were considered as a case study, and the thermo-economic analysis of the plant was performed to estimate the levelised cost of electricity based on the annual performance of the plant. The overall energy produced by the plant is 10,682 kWh, the capital cost has been estimated to be EUR 27,051 and, consequently, the specific cost of the plant, defined as the ratio between the cost of components and output power in design condition, has been estimated to be around EUR 3980/kWe. Results from the levelised cost of electricity (LCOE) analysis demonstrate a levelised cost of electricity of EUR 22.81/kWh considering a plant lifetime of 25 years. The results of the present case study have been compared with the results from IPSEpro 7 where the same component characteristic maps and operational strategy were considered. This comparison was aimed to verify the component matching procedure adopted for the present model. A plant sizing optimisation was then performed to determine the plant size which minimises the levelised cost of electricity. The design space of the optimisation variable is limited to the values 0.07–0.16 kg/s. Results of the optimisation demonstrate a minimum LCOE of 21.5 [EUR/kWh] for a design point mass flow rate of about 0.11 kg/s. This corresponds to an overall cost of the plant of around EUR 32,600, with a dish diameter of 9.4 m and an annual electricity production of 13,700 [kWh]

CFD model-based analysis and experimental assessment of key design parameters for an integrated unglazed metallic thermal collector façade

Active façade systems incorporating solar thermal collectors currently offer very promising energetic solutions. From among the available systems, a simple solution is the unglazed heat collector for potential integration in low-temperature applications. However, when adopting system definitions, the modification of some design parameters and their impact has to be fully understood. In this study, the case of an unglazed collector integrated into a sandwich panel is assessed and a specific analysis is performed for a proper assessment of the influence of key design parameters. Based on that case study of the real built system, a CFD model is developed and validated and a parametric assessment is then performed, by altering the configurations of both the panel and the hydraulic circuit. In this way, the potential of each measure to harness solar energy can be evaluated and each parameter with its different level of impact can be highlighted, to identify those of higher relevance. A characterization of the real solution completes the study, by providing the efficiency curves and the total energy collected during the experimental campaign. The maximum estimate of the efficiency of a 6 m2 façade was within a range between 0.47 and 0.34 and the heat loss factor was between 4.8 and 7.5. The case study exercises reveal the real energy efficiency and solar production patterns. There was also an opportunity to consider significant improvements to increase the output of the active façade. The main conclusions concerned the different criteria that improved the definition of the system and greater comprehension of alternative designs that may be integrated in the underlying concept.The authors are grateful to the Basque Government for fundingthis research through projects IT781-13 and IT1314-19 and to allthose involved in the different stages for their guidance andinvaluable help.The authors would also like to thank all those companies andresearchers participating in the BASSE project for their stronginvolvement during that research. Results from BASSE project haveinspired present research. The BASSE project received funding fromthe European Union, RFCS Program, Research Fund for Coal and Steel project Building Active Steel Skin (BASSE, Grant Agreement noRFSR-CT-2013-00026

A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance

An overview of knot Floer homology

Knot Floer homology is an invariant for knots discovered by the authors and, independently, Jacob Rasmussen. The discovery of this invariant grew naturally out of studying how a certain three-manifold invariant, Heegaard Floer homology, changes as the three-manifold undergoes Dehn surgery along a knot. Since its original definition, thanks to the contributions of many researchers, knot Floer homology has emerged as a useful tool for studying knots in its own right. We give here a few selected highlights of this theory, and then move on to some new algebraic developments in the computation of knot Floer homology

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time

Adaptive Grey-Box Models for Model Predictive Building Control Using the Unscented Kalman Filter

Model predictive control (MPC) for buildings is a promising approach to reduce the energy consumption of buildings while at the same time the thermal user comfort can be improved. The core of this control strategy consists of building models that can describe the thermal behavior of particular zones accurately. Grey-box models are frequently used modeling approaches for control-oriented models, however, these models often have limitations regarding their general applicability. Furthermore, the modeling and identification of models used in MPC still require significant effort and is one of the main obstacles for the actual practical implementation of building predictive control. This paper addresses these issues and presents a framework for the online state and parameter estimation of grey-box models. The results show that (1) this online simultaneous state and parameter estimation highly increases the multi-steps-ahead (up to 48 h) prediction performance, (2) this approach enables the models to adapt to changing environmental conditions and (3) it is possible to use only one pre-defined initial model to describe the thermal behavior of several different zones