Cable tray FIRE tests simulations in open atmosphere and in confined and mechanically ventilated compartments with the CALIF3S/ISIS CFD software

Fire hazard in nuclear power plants (NPPs) is particularly often investigated as potential cause of safety equipment failure and confinement loss. Many fire events recorded in NPPs involve electric cables, widely used throughout facilities. IRSN is developing the CALIF3S/ISIS computational fluid dynamics software devoted to fire simulation in large‐scale confined and mechanically ventilated compartments. This paper presents two aspects of the CALIF3S/ISIS code ability to simulate fires. The first one concerns vertical and horizontal spreading of a cable tray fire in open atmosphere using an approach based on the FLASH‐CAT cable fire spread model. Resorting to the suitable parameters of the FLASH‐CAT model based on video fire analyses of tests enables to properly compute the heat release rate of the fire. The second aspect concerns the ability to simulate the evolution and consequences of fires in confined and mechanically ventilated compartments. For these cases, the heat release rate measured during the corresponding experiment is used as input data for the calculations. The predicted evolutions of pressure or gas temperatures are in relatively good accordance with the experiments. The major discrepancy concerns gas concentrations in the fire room which is attributed to a lack of information about the properties of the fuel material.     

A flexible air separation process: 1. Design and steady-state optimizations

The flexible operation of energy-intensive processes, such as cryogenic air separation, has economic potential due to increasing fluctuations of the electricity markets. Multiproduct air separation processes with high ratios of liquid product are very promising for flexible operation due to storable products. We present a process design with an integrated liquefication cycle and liquid assist operation, that facilitates a high liquid product ratio and a flexible process operation. We use a mechanistic dynamic process model in steady-state process optimizations covering the wide operational range of the proposed process. The optimization results show that the power demand can be varied in a range from 3.5 to 28 MW without violating operational constraints by changing the nitrogen and oxygen production rates. Thus, the proposed process is a promising air separation candidate for flexible operation with respect to fluctuating electricity markets.     

Portable Node-Level Parallelism for the PGAS Model

The Partitioned Global Address Space (PGAS) programming model brings intuitive shared memory semantics to distributed memory systems. Even with an abstract and unifying virtual global address space it is, however, challenging to use the full potential of different systems. Without explicit support by the implementation node-local operations have to be optimized manually for each architecture. A goal of this work is to offer a user-friendly programming model that provides portable performance across systems. In this paper we present an approach to integrate node-level programming abstractions with the PGAS programming model. We describe the hierarchical data distribution with local patterns and our implementation, MEPHISTO, in C++ using two existing projects. The evaluation of MEPHISTO shows that our approach achieves portable performance while requiring only minimal changes to port it from a CPU-based system to a GPU-based one using a CUDA or HIP back-end.

     

Differences in seasonal distribution of wild and stocked juvenile lake trout by depth and temperature in Lake Champlain

Lake trout (Salvelinus namaycush) reared in hatcheries are exposed to an environment and feeding regime that is different from wild lake trout, and are stocked at substantially larger sizes with higher lipid reserves. In addition to differences in diet and growth, this early experience may alter habitat use compared to the wild cohort. We used seasonal data on the depth and temperature distribution of wild and stocked juvenile lake trout to test for differences in habitat use and inform sampling strategies to evaluate annual recruitment. Bottom trawling was conducted from 2015 to 2019 in the central basin of Lake Champlain every two to four weeks during the ice-free season. Differences in distribution of wild and stocked lake trout were most pronounced during thermal stratification, when wild juveniles were more abundant than stocked juveniles at shallower depths and warmer temperatures and stocked juveniles were more abundant at deeper depths and colder temperatures. Temperature preferences may be a consequence of different early rearing environments; wild lake trout are acclimated to lake temperatures and forage, whereas stocked fish entered the lake with high lipid content and little foraging experience. Unbiased assessment of the proportion of wild lake trout and growth and survival of the entire juvenile lake trout population using bottom trawl sampling should either take place in the pre- and post-stratification seasons when wild and stocked fish are at the same depths, or include the full range of depths and temperatures that wild and stocked fish occupy during the stratified period.     

Geosynthetic reinforced piled embankment modeling using discrete and continuum approaches

Understanding the load transfer mechanism can support engineers having more economical design of geosynthetic reinforced piled embankments. This study aims to investigate the load transfer mechanisms by two different numerical methods including the Discrete Element Method (DEM) and the Finite Difference Method (FDM). The DEM model adopts (a) discrete particles to simulate the micro-structure of the granular materials and (b) coupled discrete element – finite element method (DEM-FEM) to capture the interaction between granular materials and geotextiles. On the other hand, the FDM model uses an advanced constitutive soil model considering the hardening and softening behaviour of the granular materials. The numerical results show that the geotextiles can only contribute to the vertical loading resistance in cases where the soils between piles are soft enough. In terms of design, an optimum value of the geotextile tensile stiffness can be found considering the load, the soft soil stiffness and the thickness of the embankment. Both the DEM and the FDM show that a high geotextile tensile stiffness is not required since an extra stiffness will slightly contribute to the efficiency of the geosynthetic reinforced piled embankments. Nevertheless, both models are useful to optimize the design of geosynthetic reinforced piled embankments.     

Improvement of Aglycone π-Stacking Yields Nanomolar to Sub-nanomolar FimH Antagonists

Antimicrobial resistance has become a serious concern for the treatment of urinary tract infections. In this context, an anti-adhesive approach targeting FimH, a bacterial lectin enabling the attachment of E. coli to host cells, has attracted considerable interest. FimH can adopt a low/medium-affinity state in the absence and a high-affinity state in the presence of shear forces. Until recently, mostly the high-affinity state has been investigated, despite the fact that a therapeutic antagonist should bind predominantly to the low-affinity state. In this communication, we demonstrate that fluorination of biphenyl α-d -mannosides leads to compounds with perfect π–π stacking interactions with the tyrosine gate of FimH, yielding low nanomolar to sub-nanomolar K_D values for the low- and high-affinity states, respectively. The face-to-face alignment of the perfluorinated biphenyl group of FimH ligands and Tyr48 was confirmed by crystal structures as well as ¹H,¹⁵N-HSQC NMR analysis. Finally, fluorination improves pharmacokinetic parameters predictive for oral availability.     

Nitrogen contributions of alley cropped Trifolium pratense may sustain short rotation woody crop yields on marginal lands

Nutrient Cycling in Agroecosystems - Incorporating nitrogen-fixing cover crops into short rotation woody crop (SWRC) plantations may increase soil and tree nitrogen while sustaining, or even...     

A Dynamic Combinatorial Approach for Identifying Side Groups that Stabilize DNA-Templated Supramolecular Self-Assemblies

DNA-templated self-assembly is an emerging strategy for generating functional supramolecular systems, which requires the identification of potent multi-point binding ligands. In this line, we recently showed that bis-functionalized guanidinium compounds can interact with ssDNA and generate a supramolecular complex through the recognition of the phosphodiester backbone of DNA. In order to probe the importance of secondary interactions and to identify side groups that stabilize these DNA-templated self-assemblies, we report herein the implementation of a dynamic combinatorial approach. We used an in situ fragment assembly process based on reductive amination and tested various side groups, including amino acids. The results reveal that aromatic and cationic side groups participate in secondary supramolecular interactions that stabilize the complexes formed with ssDNA.     

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.