Trade-off between capacitance and cycling at elevated temperatures in redox additive aqueous electrolyte based high performance asymmetric supercapacitors

2017 ◽  
Vol 229 ◽  
pp. 291-298 ◽  
Author(s):  
Arvinder Singh ◽  
Md. Aqueel Akhtar ◽  
Amreesh Chandra
Keyword(s):  
High Performance ◽  
Elevated Temperatures ◽  
Aqueous Electrolyte ◽  
Asymmetric Supercapacitors ◽  
Trade Off

ALCOA 351 SUPRACAST

Alloy Digest ◽  
2020 ◽  
Vol 69 (7) ◽  
Keyword(s):  
Physical Properties ◽  
Tensile Properties ◽  
Copper Alloy ◽  
High Performance ◽  
Elevated Temperatures ◽  
Heat Treating ◽  
Aluminum Silicon

Abstract Alcoa 351 SupraCast is a heat-treatable aluminum-silicon-copper alloy that also contains small amounts of magnesium, manganese, vanadium, and zirconium. It is designed for components exposed to elevated temperatures in high performance engines. This datasheet provides information on composition, physical properties, and tensile properties as well as fatigue. It also includes information on heat treating, machining, and joining. Filing Code: Al-466. Producer or source: Alcoa Corporation.

BERYLCO 25S

Alloy Digest ◽  
1952 ◽  
Vol 1 (3) ◽  
Keyword(s):  
Mechanical Properties ◽  
High Performance ◽  
Precipitation Hardening ◽  
Elevated Temperatures ◽  
Heat Treating ◽  
Good Resistance ◽  
Subzero Temperatures ◽  
Endurance Strength ◽  
Wear And Corrosion ◽  
Resistance To Wear

Abstract Berylco 25S alloy is the high-performance beryllium-copper spring material of 2 percent nominal beryllium content. It responds to precipitation-hardening for maximum mechanical properties. It has high elastic and endurance strength, good electrical and thermal conductivity, excellent resistance to wear and corrosion, high corrosion-fatigue strength, good resistance to moderately elevated temperatures, and no embrittlement or loss of normal ductility at subzero temperatures. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: Cu-3. Producer or source: Beryllium Corporation.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Calculating the optimal hematocrit under the constraint of constant cardiac power

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michal Sitina ◽  
Heiko Stark ◽  
Stefan Schuster
Keyword(s):  
Blood Flow ◽  
High Performance ◽  
Animal Species ◽  
Normal Values ◽  
Mechanical Load ◽  
The Body ◽  
Trade Off ◽  
Cardiac Power ◽  
Optimal Value ◽  
Good Agreement

AbstractIn humans and higher animals, a trade-off between sufficiently high erythrocyte concentrations to bind oxygen and sufficiently low blood viscosity to allow rapid blood flow has been achieved during evolution. Optimal hematocrit theory has been successful in predicting hematocrit (HCT) values of about 0.3–0.5, in very good agreement with the normal values observed for humans and many animal species. However, according to those calculations, the optimal value should be independent of the mechanical load of the body. This is in contradiction to the exertional increase in HCT observed in some animals called natural blood dopers and to the illegal practice of blood boosting in high-performance sports. Here, we present a novel calculation to predict the optimal HCT value under the constraint of constant cardiac power and compare it to the optimal value obtained for constant driving pressure. We show that the optimal HCT under constant power ranges from 0.5 to 0.7, in agreement with observed values in natural blood dopers at exertion. We use this result to explain the tendency to better exertional performance at an increased HCT.

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