Analysis and Improvement of Virtual Work Criteria for Structure Optimization
An improvement of virtual work criteria for structure optimization is presented to overcome the shortcomings caused by the neglect of dead weight and inertial load of structure in solution of criteria equations. Deduction of criteria equations is analyzed to prove that the shortcomings lead to the error of structure optimization. Therefore, new criteria equations are formulated using the displacement representation of algebraic sum and matrices. According to the characteristics of the improved criteria equations, an improved Aitken algorithm is applied to solve the equations in order to implement the complete automatization of iterative solution for structure optimization. The algorithm is proved to be an accelerated iterative method, and its convergence order is estimated. The feasibility of improved criteria equations and the merits of improved Aitken algorithm are demonstrated by an illustrative example showing five-bar truss. The results show that the improvement of virtual work criteria provides a structure optimization method for precision machine.
An algorithm for grouping members in a structure
Abstract An automated method for grouping discrete structural members is presented in this paper. The number of groups is specified by the user, and members are grouped according to their mass per unit length. The method first optimizes a structure assuming that every member can have a different section. This is done using the Virtual Work Optimization (VWO) method, but any method can be used. The initial solution is the lightest possible, but the number of sections required makes it impractical and uneconomical to construct. Next, an exhaustive search of all possible grouping permutations is carried out. The mass of the structure is predicted for each permutation. The permutation which results in the lightest structure is selected. The structure is passed through the optimization process one last time. The solution produced satisfies all strength and deflection criteria. A method for reducing computational cost is proposed to address very large structures. Four case studies are presented to demonstrate the effectiveness of the grouping algorithm. A stepped cantilever, a 15 storey frame, a truss and a warehouse are investigated. As an example of results, the 195 members of the 15 storey frame are placed into 25 groups to produce a structure only 2.4% heavier than the ungrouped solution. The configuration computed is 5.9% lighter than grouping the structure using the method found in the literature.
Fully Integrated Bidirectional CMOS-MEMS Flow Sensor With Low Power Pulse Operation
Leveraging More-than-Moore technology, we demonstrate an integrated CMOS MEMS flow sensor via a very compact system on chip (SoC) that can sense the bidirectional N2 gas flow. Our SoC features a very low-noise instrumentation amplifier that is implemented on a CMOS wafer and serves as a readout integrated circuit for a thermoresistive micro-calorimetric flow sensor which is fabricated on a MEMS wafer. A compact heterogeneous integration is achieved by combining the two wafers via the proprietary InvenSense AlN process. The measured sensitivity of our CMOS MEMS flow sensor is 98 mV/sccm which is three times better than the state-of-the-art counterpart. The flow range measured by our SoC is from −26 to 26 m/s (from −50 to 50 sccm). Moreover, the pulsed heater operation makes this proposed SoC flow sensor a very low power (<9 mW) and, thus, a promising candidate for the Internet-of-Things applications in smart home/green buildings.