During the conceptual design stages, designers often have incomplete knowledge about the interactions among design parameters. We are developing a methodology that will enable designers to create models with levels of detail and accuracy that correspond to the current state of the design process. Thus, designers can create a rough surrogate model when only a few data points are available and then refine the model as the design progresses and more information becomes available. These surrogates represent the system response when limited information is available and when few realizations of experiments or numerical simulations are possible. This paper presents a covariance-based approach for building multistage surrogates in the conceptual design stages when bounds for the response are not available a priori. We test the methodology using a one-dimensional analytical function and a heat transfer problem with an analytical solution, in order to obtain error measurements. We then illustrate the use of the methodology in a thermal design problem for wearable computers. The surrogate model enables the designer to understand the relationships among the design parameters.
In order to reduce the amount of carbon dioxide (CO2) released into the atmosphere, significant progress has been made into capturing and storing CO2 from power plants and other major producers of greenhouse gas emissions. The compression of the captured carbon dioxide stream requires significant amounts of power and can impact plant availability, and increase operational costs. Preliminary analysis has estimated that the CO2 compression process reduces plant efficiency by 8% to 12% for a typical power plant. This project supports the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL) objective of reducing energy requirements for carbon capture and storage in electrical power production. The primary objective of this study is to boost the pressure of CO2 to pipeline pressures with the minimal amount of energy required. Previous thermodynamic analysis identified optimum processes for pressure rise in both liquid and gaseous states. Isothermal compression is well known to reduce the power requirements by minimizing the temperature of the gas entering subsequent stages. Intercooling is typically accomplished using external gas coolers and integrally geared compressors. For large scale compression, use of straight through centrifugal compressors, similar to those used in oil and gas applications including LNG production, is preferred due to the robustness of the design. However, intercooling between each stage is not feasible. The current research develops an internally cooled compressor diaphragm that removes heat internal to the compressor. Results documenting the design process are presented including 3D conjugate heat transfer CFD studies. Experimental demonstration of the design is performed on a sub scale centrifugal compressor closed loop test facility for a range of suction pressures.
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The Time Transformation Method in ANSYS CFX is investigated as an efficient substitution to Transient Rotor Stator (TRS) analysis for rotating stall prediction in a centrifugal compressor stage. The computational study was performed by varying the number of blade sectors to determine how the circumferential extent of the computational domain affects the accuracy of the stall prediction. The results obtained using a minimum number of blades, approximately one-quarter the full blade count, and approximately one-half the full blade count were compared to both TRS and steady simulations on the same mesh to characterize the predictive capability of each approach. It is shown that both steady and unsteady methods are able to predict the formation of stall cells, but significant qualitative and quantitative differences exist in the flowfield results. The largest mass flow rate at which rotating stall was captured and the number of stall cells were in good agreement with the experimental data.
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