AXAIRQ User`s Manual

Simpkins, A.A.

doi:10.2172/218236

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…Other critical interfaces in planar SOC stacks are those between the cell and the interconnect. On the fuel electrode side Ni meshes or foams can be used to ensure good adhesion and current distribution (7)(8)(9). However, the adherence between the oxygen electrode and the interconnect is more challenging.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Robustness of Brittle Solid Oxide Cell Stack Components

et al. 2019

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I.; Khajavi, P.; Talic, B.; Kiebach, R.; Hendriksen, P. V. AbstractFor long-term durability and robustness to withstand thermal cycles, the planar solid oxide cell (SOC) stack technologies are challenged by the use of brittle components. With the current trend of increasing the footprint of the SOC stacks, even larger thermal gradients and thermal stresses can be expected. In this overview paper, we present recent advances from our group on improving the fracture energy of three critical materials/interfaces. The fracture energy of the fuel electrode support is increased by ~50 % by tailoring composition and further optimizing the phase transformation toughening. The fracture energy of the air-side contact layer is increased by a factor of ~10 by using metallic pre-cursors that are transformed to spinels through in-situ reactive oxidative bonding. Finally, the fracture energy of the seal-interconnect interface is improved by a factor of 5 by combining an optimized sealing glass with aluminum based coatings for the interconnect.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Robustness of Brittle Solid Oxide Cell Stack Components

et al. 2019

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“…A total friction welding process has seven phases, such as fast-forward, work into, preliminary top, withdrawal, friction, upset forging and fast-backward. In the AMESim mode [9][10], construction model of applying common friction welding machine hydraulic pressure system is shown in Figure 1. The main simulation parameter is set to the speed of dosing pumps 1500r/min, with the displacement 76mL/r, the pressure relief valve 15MPa, the force cylinder piston diameter 180mm, the piston rod diameter 80mm, the piston stroke 870mm, the moving parts quality 191kg.…”

Section: The Normal Friction Welder Hydraulic Force Application Systemmentioning

confidence: 99%

“…W use is the actual use of the work and W in is the total input power formula (9). When the friction welding process and the parameters are determined, W use is a constant value on base and has little contribution to the system work efficiency, so in order to improve the work efficiency of hydraulic power of friction welding system, W in must decrease from the start.…”

Section: The Hydraulic Force Application System Of Efficiency Analysismentioning

confidence: 99%

Energy Consumption Model of the Force Application Hydraulic System for Friction Welding Machine

Zheng

2014

AMM

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For the problem of low efficiency of the ordinary hydraulic systems of the friction welding machine, operating characteristics and efficiency model of the force application hydraulic system of friction welding machine are studied. The energy consumption characteristics of traditional welding machine are simulated, the efficiency characteristics of the force application hydraulic system of friction welding machine is studied, and the energy consumption and efficiency model are deduced. According to theoretical analysis, the system efficiency can be improved by changing pump output flow and relief output flow. The simulation results show that the working efficiency of the new system can be enhanced to 24%. The new friction welding hydraulic system was used in actual production and the system efficiency can be improved to 22%.

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“…cerevisiae that our labs have recently generated, covering more than 13,000 compounds from the 22 RIKEN NPDepo and several NCI, NIH, and GlaxoSmithKline (GSK) compound collections. 23 Supportive of the idea that chemical-genetic interaction data provide an unbiased proxy for 24 biological functions, we found that many commonly used structural similarity measures were 25 able to predict the compounds that exhibited similar chemical-genetic interaction profiles, 26 although these measures did exhibit significant differences in performance.…”

mentioning

confidence: 99%

“…We generated, in our labs, the chemical-genetic interaction profiles for 13,524 compounds 117 from several diverse compound collections [20]. We used a subset of these screened compounds 118 that exhibited high confidence predictions for the annotated processes and biological pathways 119 based upon our chemical-genetic interaction profiles [20,22]. We included in our benchmarking 120 system two compound sets independently: (1) 826 compounds from the RIKEN Natural Product 121 Depository (NPDepo) compound collection, which we called RIKEN high confidence collection.…”

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confidence: 99%

Improving prediction of compound function from chemical structure using chemical-genetic networks

Safizadeh

Nelson

et al. 2017

Preprint

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11The drug discovery process can be significantly improved through understanding how the 12 structure of chemical compounds relates to their function. A common paradigm that has been 13 used to filter and prioritize compounds is ligand-based virtual screening, where large libraries of 14 compounds are queried for high structural similarity to a target molecule, with the assumption 15 that structural similarity is predictive of similar biological activity. Although the chemical 16 informatics community has already proposed a wide range of structure descriptors and similarity 17 coefficients, a major challenge has been the lack of systematic and unbiased benchmarks for 18 biological activity that covers a broad range of targets to definitively assess the performance of 19 the alternative approaches. 20We leveraged a large set of chemical-genetic interaction data from the yeast Saccharomyces 21 cerevisiae that our labs have recently generated, covering more than 13,000 compounds from the 22 RIKEN NPDepo and several NCI, NIH, and GlaxoSmithKline (GSK) compound collections. 23Supportive of the idea that chemical-genetic interaction data provide an unbiased proxy for 24 biological functions, we found that many commonly used structural similarity measures were 25 able to predict the compounds that exhibited similar chemical-genetic interaction profiles, 26 although these measures did exhibit significant differences in performance. Using the chemical-27genetic interaction profiles as a basis for our evaluation, we performed a systematic 28 benchmarking of 10 different structure descriptors, each combined with 12 different similarity 29 coefficients. We found that the All-Shortest Path (ASP) structure descriptor paired with the 30 Braun-Blanquet similarity coefficient provided superior performance that was robust across 31 several different compound collections. 32We further describe a machine learning approach that improves the ability of the ASP metric to 33 capture biological activity. We used the ASP fingerprints as input for several supervised machine 34 learning models and the chemical-genetic interaction profiles as the standard for learning. We 35 found that the predictive power of the ASP fingerprints (as well as several other descriptors) 36 could be substantially improved by using support vector machines. For example, on held-out 37 data, we measured a 5-fold improvement in the recall of biologically similar compounds at a 38 precision of 50% based upon the ASP fingerprints. Our results generally suggest that using high-39 dimensional chemical-genetic data as a basis for refining chemical structure descriptors can be a 40 powerful approach to improving prediction of biological function from structure. 41

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AXAIRQ User`s Manual

Cited by 4 publications

References 2 publications

Enhancing the Robustness of Brittle Solid Oxide Cell Stack Components

Enhancing the Robustness of Brittle Solid Oxide Cell Stack Components

Energy Consumption Model of the Force Application Hydraulic System for Friction Welding Machine

Improving prediction of compound function from chemical structure using chemical-genetic networks

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