A reduced order model to predict the machining time and cost of small-scale radial-inflow turbines

This paper proposes a novel approach to predict the machining time of small-scale radial-inflow turbine impellers for organic Rankine cycle power systems based on preliminary design parameters. The time prediction method uses machining databases of different milling tools for the estimation of the cutting parameters, on which basis the flow channel volume and surfaces are discretized for the generation of preliminary milling toolpaths. The parameter selection comprises the material selection, the volume removed, the demanded surface quality and the tool selection governed by the accessibility due to geometrical limitations induced by the blades. The model is verified using computer-aided manufacturing software for two radial-inflow turbines cases: a state-of-the-art turbine using air and a turbine using the working fluid Novec 649 for a heat recovery application. The results highlight the governing role of the tool slenderness ratio on the actual manufacturing time. The relative time distribution among roughing, semi-finishing and finishing operations remain similar when considering the same production chain. The tool roughing strategy holds potential for optimization by contributing to approximately 60 % of the total milling time. Furthermore, the results indicate that the turbine manufacturing time can be improved by up to approximately 44 % when the maximum permissible load limits of the tools are exploited by customized cutting data. The presented machining time model can be applied in future works to predict the manufacturing costs of impellers and to quantify the influence of individual design features on the total cost by performing a sensitivity analysis. Moreover, the design space for optimal designs of small-scale radial-inflow turbines for organic Rankine power applications considering both the manufacturing time and the performance of the expander can be explored.

Modeling and Experimental Investigation of an Oil-Free Microcompressor-Turbine Unit for an Organic Rankine Cycle Driven Heat Pump

Jonathan Demierre, Antonio Rubino, Jürg Alexander Schiffmann

Domestic heating and cooling will more and more have to rely on heat pumps (HPs) in order to support a more rational use of primary energy consumption. The HP market is mainly dominated by electrically driven vapor compression cycles and by thermally driven sorption processes. The drawback of electrically driven vapor compression cycle is their dependence on an electrical grid and the fact that they increase the winter or summer electricity peak demands. Hence, a thermally driven vapor compression cycle would offer substantial advantages and flexibility to the end user for heating and cooling applications. This paper presents the investigation of an oil-free compressor-turbine unit (CTU) used for a thermally driven HP (TDHP) based on the combination of a HP compression cycle and an organic Rankine cycle (ORC). The CTU consists of a radial inflow turbine and a centrifugal compressor of the order of 2 kW each, directly coupled through a shaft supported on gas lubricated bearings. The CTU has been tested at rotor speeds in excess of 200 krpm, reaching compressor and turbine pressure ratios up to 2.8 and 4.4, respectively, and isentropic efficiencies around 70%. Comparisons between the experimental data and predictions of models, that are briefly described here, have been carried out. A sensitivity analysis based on the experimentally validated models shows that tip clearance, for both compressor and turbine, and surface roughness of the compressor are key parameters for further improving performance.

American Society of Mechanical Engineers2015

Optimal design methods applied to solar-assisted hydrothermal gasification plants

Alberto Mian

The gasification of highly watered waste residues may help to reduce fossil fuels consumption, as well as emissions, all the while improving efficiency. The integration of supercritical gasification with solar energy may improve the performances of both new and existing plants. The design and optimization of such kinds of energy conversion plants requires to take into account the following aspects: renewable sources are intermittent, prices of fuels and materials are continuously fluctuating and are uncertain, the energy efficiency should be maximized and the capital and opeating costs should be minimized. The main goal of this thesis is to develop mathematical programming frameworks in order to solve the design, the scheduling and the Heat Exchanger Network Synthesis (HENS) of any industrial plant. In particular, the final goal is to apply the proposed methods to the design of a solar-assisted supercritical gasification plant. The methods are developed in order to propose strategies to account for uncertainty, sizing constraints, scheduling options and multi-period HENS. In order to apply the frameworks elaborated in this dissertation, a set of mathematical models is suggested. The goal is to describe the thermodynamics of the considered process units: supercritical water gasification, gas separation, solar energy conversion technologies and storage utilities are studied. The pre-design problem is first tackled by coupling an energy targeting model with a Global Sensitivity Analysis (GSA) method and a Monte Carlo (MC) simulation. Given a set of energy conversion options, the most promising energy conversion paths with respect to a set of uncertain parameters are identified while applying energy integration principles. The design problem is then tackled and the design, the scheduling, as well as HENS for multi-period operations are investigated. The result is a sequential mathematical programming algorithm for solving the problem. A decomposition approach is applied using a bi-level optimization strategy, where derivative-free optimization algorithms are coupled with derivative based, mixed integer linear and non linear models. The framework is tested on a literature example and on a large scale example in order to prove its effectiveness. The integration of thermal storage options is included in order to highlight the improvements that can be achieved with process heat recovery. The computation time problem is addressed and an alternative strategy to solve the same problem is put forward. Finally, the developed methods are coupled with a Multi-Objective Optimization algorithm and the multi-period design problem for designing a solar-assisted super-critical gasification plant is solved. It is thus demonstrated how the proposed methods can be successfully applied to industrial-scale problems. It remains that computational issues are problem-dependent and sensitive to the problem definition.

EPFL2016

Optimal design methods applied to solar-assisted hydrothermal gasification plants

Alberto Mian

EPFL2016