C/C-SiC component for metallic phase change materials

Thanks to their high energy density and thermal conductivity, metallic Phase Change Materials (mPCM) have shown great potential to improve the performance of thermal energy storage systems. However, the commercial application of mPCM is still limited due to their corrosion behavior with conventional container materials. This work first addresses on a fundamental level, whether carbon-based composite-ceramics are suitable for corrosion critical components in a thermal storage system. The compatibility between the mPCM AlSi₁₂ and the Liquid Silicon Infiltration (LSI)-based carbon fiber reinforced silicon carbide (C/C-SiC) composite is then investigated via contact angle measurements, microstructure analysis, and mechanical testing after exposure. The results reveal that the C/C-SiC composite maintains its mechanical properties and microstructure after exposure in the strongly corrosive mPCM. Based on these results, efforts were made to design and manufacture a container out of C/C-SiC for the housing of mPCM in vehicle application. The stability of the component filled with mPCM was proven nondestructively via computer tomography (CT). Successful thermal input- and output as well as thermal storage ability were demonstrated using a system calorimeter under conditions similar to the application. The investigated C/C-SiC composite has significant application potential as a structural material for thermal energy storage systems with mPCM.     

An efficient approach for prediction of Warburg-type resistance under working currents

In the present study, an efficient approach for the prediction of Warburg-type element is proposed via the analysis of the anode-supported solid oxide fuel cell (SOFC) performance under various working conditions. The details of the performance, polarization curves, impedance behavior, and species distribution profiles within the electrode are investigated via the combination of equivalent circuit model (ECM) analysis and multiphysics numerical simulations. The multiphysics simulation is developed and calibrated with experimental results of SOFC button cells under various working currents. With the complete datasets generated from the calibrated simulations, the trends of the element parameters involved in equivalent circuit model are analyzed. Generalized empirical functions are proposed as well as the procedures of prediction of performance under different conditions. The verification cases show good agreement between the predicted results from proposed model and the reference results. This proposed approach can be utilized to quickly predict the properties for desired performance in the manufacturing processes, and it also has the potential of reducing the computational cost in the simulation of large SOFCs.     

The influence of processing conditions on the mechanical properties of poly(aryl-ether-ketone)/polybenzimidazole blends

Interest in developing high-performance blends for niche applications has grown significantly in efforts to meet ever-increasing harsh environment demands. In this work, four model poly(aryl-ether-ketone)/polybenzimidazole (PAEK/PBI) blends were chosen to study the influence of premixing methods, processing, and matrix polymers, on their mechanical properties. Among the model poly(ether ether ketone) (PEEK) and PBI blends, mechanical properties are greatly enhanced by melt premixing. The molding process mainly affects the matrix crystallinity, which in turn greatly influences fracture toughness of the blend. Poly(ether ketone ketone) (PEKK) and PBI blend exhibits a slightly lower tensile strength and fracture toughness than PEEK/PBI due to the differences in inherent properties of PEEK and PEKK matrices and their interfacial interaction with PBI. The processing−structure–property relationship of PAEK/PBI blends is established to help guide optimal design of high-performance polymer blends for structural applications. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48966.     

Board Oversight of Management’s Risk Appetite and Tolerance: Regulators Claim they Expect it but Change will Not Come Easy

In the aftermath of the 2008 global financial crisis post-mortems were convened in countries around the world to identify what went wrong. A unanimous conclusion was that boards of directors of public companies in general, and financial institutions in particular, need to do more to oversee “management’s risk appetite and tolerance” if future crisis are to be avoided. This finding represents a significant paradigm shift in role expectations while introducing a new concept the Financial Stability Board (FSB) has coined effective “Risk Appetite Frameworks” (RAFs).ⁱ Regulators around the world are now moving at varying speeds to implement these conclusions by enacting new laws and regulations. What regulators appear to be seriously underestimating is the amount of change necessary to make this laudable goal a reality.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Emergency egress for the elderly in care home fire situations

Practitioners are continuing to develop egress modelling software for the design of the built environment. These models require data about human behaviour and factors for calibration, validation and verification. This study aims to address the specific data and knowledge gap: emergency egress of the elderly. Such data are difficult to collect given privacy and consent concerns, with strong relationships generally being required between residences and researchers. Through the observation of nine fire drills at six Canadian long-term care (LTC) and retirement homes, specific evacuation actions and behaviour were observed for 37 staff members and information about the evacuation of 56 residents was collected. These drills demonstrated that emergency egress in LTC and retirement homes is highly staff dependent with 72% of residents recorded requiring full assistance at all stages of movement in evacuation, and that the type of announced/unannounced drill and level of resident care will affect the type of data collected. The development of travel speed and pre-movement is discussed subject to limitation with qualitative behavioural insights of residents that were observed. This study provides valuable methodological discussion on how to conduct behavioural studies in similar highly restricted research environments. Specific attention is given to understanding the considerations that must be made when using fire drills as data sources, and the impact that these can have on using such data for modelling. This study may inform the initial setup and programming of evacuation models from an actions and behavioural perspectives of staff members and residents.     

Bioraffinerien auf Basis schwach verholzter Biomasse

Weakly lignified biomass, like brewers' spent grain and grass silage, is good feedstock for biorefineries. The biomass can be separated and converted into valuable products in different ways. In this study, brewers' spent grain is used to investigate solid state fermentations with Cellulomonas uda. Additionally, hydrothermal pretreatment and subsequent enzymatic saccharification, as well as formation of inhibitory compounds, is demonstrated. Hydrothermal pretreatment combined with simultaneous saccharification and fermentation is shown as an example for the utilization of grass silage.     

Phase equilibria studies in the CaO–SiO2–Al2O3–MgO system with CaO/SiO2 ratio of 0.9

Phase equilibria and liquidus temperatures in the CaO–SiO₂–Al₂O₃–MgO system at a CaO/SiO₂ weight ratio of 0.9 in the liquid phase have been experimentally determined employing high-temperature equilibration and quenching technique followed by electron probe X-ray microanalysis. Isotherms at 1573, 1623, 1673, and 1773 K were determined and the primary phase fields of wollastonite, melilite, olivine, periclase, spinel, and corundum have been located. Compositions of the olivine and melilite solid solutions were analyzed and discussed. Comparisons between the newly constructed diagram, existing data, and FactSage predicted phase diagrams were performed and differences were discussed. The present study will be useful for guidance of industrial practices and further development of thermodynamic modeling.