On Extracting Mechanical Properties from Nanoindentation at Temperatures up to 1000∘^{\circ}C

Alloyed MCrAlY bond coats, where M is usually cobalt and/or nickel, are essential parts of modern turbine blades, imparting environmental resistance while mediating thermal expansivity differences. Nanoindentation allows the determination of their properties without the complexities of traditional mechanical tests, but was not previously possible near turbine operating temperatures. Here, we determine the hardness and modulus of CMSX-4 and an Amdry-386 bond coat by nanoindentation up to 1000$^{\circ}$C. Both materials exhibit a constant hardness until 400$^{\circ}$C followed by considerable softening, which in CMSX-4 is attributed to the multiple slip systems operating underneath a Berkovich indenter. The creep behaviour has been investigated via the nanoindentation hold segments. Above 700$^{\circ}$C, the observed creep exponents match the temperature-dependence of literature values in CMSX-4. In Amdry-386, nanoindentation produces creep exponents very close to literature data, implying high-temperature nanoindentation may be powerful in characterising these coatings and providing inputs for material, model and process optimisations

A new Markov-chain-related statistical approach for modelling synthetic wind power time series

The integration of rising shares of volatile wind power in the generation mix is a major challenge forthe future energy system. To address the uncertainties involved in wind power generation, modelsanalysing and simulating the stochastic nature of this energy source are becoming increasinglyimportant. One statistical approach that has been frequently used in the literature is the Markov chainapproach. Recently, the method was identified as being of limited use for generating wind time serieswith time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelationcharacteristics accurately. This paper presents a new Markov-chain-related statistical approach that iscapable of solving this problem by introducing a variable second lag. Furthermore, additional featuresare presented that allow for the further adjustment of the generated synthetic time series. Theinfluences of the model parameter settings are examined by meaningful parameter variations. Thesuitability of the approach is demonstrated by an application analysis with the example of the windfeed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—thegenerated synthetic time series do not systematically underestimate the required storage capacity tobalance wind power fluctuation

Quantification of water content by laser induced breakdown spectroscopy on Mars

International audienc

Quantification of water content by laser induced breakdown spectroscopy on Mars

International audienc

Quantification of water content by laser induced breakdown spectroscopy on Mars

International audienc