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A constrained and unconstrained mass optimization study is performed to identify the sensitivity of global optimization to yield surface choice.
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For optimization study, CPG was tested at different MIF (mass increase factors): MIF = 0% (minimum configuration), MIF = 25% (middle configuration) and MIF = 50% (maximum configuration) by using DOE (design of experiment).
An optimization study using Derringer's desired function methodology was performed and the optimal conditions based on both individual and combinations of all responses (extraction temperature of 40 °C, time of 115 min and mass of 1.44 g) were determined.
To measure this effectiveness, General McChrystal in recent weeks has launched a "force optimization" study, according to senior Pentagon and military officials, who spoke on the condition of anonymity because the study has not been made public.
Finally, the analytic equation for mass optimization is presented to minimize the system mass.
Next, an optimization study was conducted with the experimentally validated and adjusted mathematical model, determining the optimal geometry for minimum energy consumption by the tunnel, identifying, as a physical constraint, the total tunnel volume (or mass of material).
This paper presents an optimization study of the design parameters for houses using rammed earth walls, including window sizes, window shading, the amount of thermal mass and the amount of insulation in the external walls.
Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems.
Optimization studies, therefore, must implement a complete mass balance-based process analysis and not only focus on single unit operations isolated from the respective others in the process.
Thus definitive optimization studies may be postponed until the system is better understood.
However, the expected mass transport benefits in the high current density regime were constrained by high ohmic cell resistance, which could likely be resolved through future optimization studies.
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