Fachbereich - Maschinenbau und Verfahrenstechnik
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The organic Rankine cycle (ORC) is an established thermodynamic process that converts waste heat to electric energy. Due to the wide range of organic working fluids available the fluid selection adds an additional degree-of-freedom to the early design phase of an ORC process. Despite thermodynamic aspects such as the temperature level of the heat source, other technical, economic, and safety aspects have to be considered. For the fluid selection process in this paper, 22 criteria were identified in six main categories while distinguishing between elimination (EC) and tolerance criteria (TC). For an ORC design, the suggested method follows a practical engineering approach and can be used as a structured way to limit the number of interesting working fluids before starting a detailed performance analysis of the most promising candidates. For the first time, the selection process is applied to a two-stage reference cycle, which uses the waste heat of a large reciprocating engine for cogeneration power plants. It consists of a high temperature (HT) and a low temperature (LT) cycle in which the condensation heat of the HT cycle provides the heat input of the LT cycle. After the fluid selection process, the detailed thermodynamic cycle design is carried out with a thermodynamic design tool that also includes a database for organic working fluids. The investigated ORC cycle shows a net thermal efficiency of about 17.4% in the HT cycle with toluene as the working fluid and 6.2% in LT cycle with isobutane as the working fluid. The electric efficiency of the cogeneration plant increases from 40.4% to 46.97% with the both stages of the two-stage ORC in operation.
Computational models for the personalized analysis of human femurs contain uncertainties in bone material properties and loads, which affect the simulation results. To quantify the influence we developed a probabilistic framework based on polynomial chaos (PC) that propagates stochastic input variables through any computational model. We considered a stochastic E-ρ relationship and a stochastic hip contact force, representing realistic variability of experimental data. Their influence on the prediction of principal strains (ϵ1 and ϵ3) was quantified for one human proximal femur, including sensitivity and reliability analysis. Large variabilities in the principal strain predictions were found in the cortical shell of the femoral neck, with coefficients of variation of ≈40%. Between 60 and 80% of the variance in ϵ1 and ϵ3 are attributable to the uncertainty in the E-ρ relationship, while ≈10% are caused by the load magnitude and 5-30% by the load direction. Principal strain directions were unaffected by material and loading uncertainties. The antero-superior and medial inferior sides of the neck exhibited the largest probabilities for tensile and compression failure, however all were very small (pf<0.001). In summary, uncertainty quantification with PC has been demonstrated to efficiently and accurately describe the influence of very different stochastic inputs, which increases the credibility and explanatory power of personalized analyses of human proximal femurs.