Optical system materials selection using performance indices in a simultaneous optimization approach

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships.

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Optimization studies of stir casting parameters and mechanical properties of TiO2 reinforced Al 7075 composite using response surface methodology

Scientific Reports ◽

10.1038/s41598-021-99168-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Adeolu A. Adediran ◽

Abayomi A. Akinwande ◽

Oluwatosin A. Balogun ◽

Bayode J. Olorunfemi ◽

Saravana Kumar M.

Keyword(s):

Response Surface ◽

Stir Casting ◽

Simultaneous Optimization ◽

Optimization Approach ◽

Al 7075 ◽

Contour Plots ◽

And Performance ◽

Work Done ◽

Quadratic Models ◽

Microstructure And Performance

AbstractStir casting is a common metallurgical route in the casting of aluminum composites. Series of work done in this aspect considered the development of the composites with fixed stir casting parameters without applying an optimization approach. These parameters affect the microstructure and performance of the composites. The study is focused on the optimization of the stir casting parameters in the production of Al 7075 reinforced with TiO2 microparticles for performance improvement. Three stir casting parameters of stirring temperature, speed, and time were varied and optimized using the central composite design technique of the response surface method. Properties evaluated were ultimate tensile strength, hardness, impact strength, elastic modulus, and compressive strength. ANOVA results showed that the three stir casting parameters had a significant impact on the property responses. Five quadratic models were established for the properties linking them to the factors. The models were confirmed to be statistically significant at a confidence level of 95% and variations were observed to be < 5%. The interaction profile of the parameters as per response surface was analyzed. Contour plots associated with each interaction gave different ranges of stirring parameters in which each property can be maximized. Simultaneous optimization of the properties using Minitab 19 software showcased 779.3 °C, 574.2 rpm, and 22.5 min as the optimal stir casting parameters for temperature, speed and time respectively.

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