Control of transient thermal response during sequential open-die forging: A trajectory optimization approach

A trajectory optimization approach is applied to the design of a sequence of open-die forging operations in order to control the transient thermal response of a large titanium alloy billet. The amount of time tire billet is soaked in furnace prior to each successive forging operation is optimized to minimize the total process time while simultaneously satisfying constraints on the maximum and minimum values of the billet's temperature distribution to avoid microstructural defects during forging. The results indicate that a "differential" heating profile is the most effective at meeting these design goals.

Optimization of off-oriented Ge substrates for MOVPE-grown GaAs solar cells

GaAs/Ge heterostructures having abrupt interfaces were grown on 2degrees, 6degrees, and 9degrees off-cut Ge substrates and investigated by cross-sectional high-resolution transmission electron microscopy (HRTEM), scanning electron microscopy, photoluminescence spectroscopy and electrochemical capacitance voltage (ECV) profiler. The GaAs films were grown on off-oriented Ge substrates with growth temperature in the range of 600-700degreesC, growth rate of 3-12 mum/hr and a V/III ratio of 29-88. The lattice indexing of HRTEM exhibits an excellent lattice line matching between GaAs and Ge substrate. The PL spectra from GaAs layer on 6degrees off-cut Ge substrate shows the higher excitonic peak compared with 2degrees and 9degrees off-cut Ge substrates. In addition, the luminescence intensity from the GaAs solar cell grown on 6degrees off-cut is higher than on 9degrees off-cut Ge substrates and signifies the potential use of 6degrees off-cut Ge substrate in the GaAs solar cells industry. The ECV profiling shows an abrupt film/substrate interface as well as between various layers of the solar cell structures.

Two timescale analysis of the Alopex algorithm for optimization

Alopex is a correlation-based gradient-free optimization technique useful in many learning problems. However, there are no analytical results on the asymptotic behavior of this algorithm. This article presents a new version of Alopex that can be analyzed using techniques of two timescale stochastic approximation method. It is shown that the algorithm asymptotically behaves like a gradient-descent method, though it does not need (or estimate) any gradient information. It is also shown, through simulations, that the algorithm is quite effective.

Reliability Based Design Optimization for Internal Stability of Reinforced Soil Structures Using Pseudo‐Static Method

Seismic design of reinforced soil structures involves many uncertainties that arise from the backfill soil properties and tensile strength of the reinforcement which is not addressed in current design guidelines. This paper highlights the significance of variability in the internal stability assessment of reinforced soil structures. Reliability analysis is applied to estimate probability of failure and pseudo‐static approach has been used for the calculation of the tensile strength and length of the reinforcement needed to maintain the internal stability against tension and pullout failures. Logarithmic spiral failure surface has been considered in conjunction with the limit equilibrium method. Two modes of failure namely, tension failure and pullout failure have been considered. The influence of variations of the backfill soil friction angle, the tensile strength of reinforcement, horizontal seismic acceleration on the reliability index against tension failure and pullout failure of reinforced earth structure have been discussed.

A Convex Optimization Framework for Almost Budget Balanced Allocation of a Divisible Good

We address the problem of allocating a single divisible good to a number of agents. The agents have concave valuation functions parameterized by a scalar type. The agents report only the type. The goal is to find allocatively efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria are of interest: the maximum ratio of budget surplus to efficient surplus, and the expected budget surplus, within the class of linear rebate functions. The goal is to minimize them. Assuming that the valuation functions are known, we show that both problems reduce to convex optimization problems, where the convex constraint sets are characterized by a continuum of half-plane constraints parameterized by the vector of reported types. We then propose a randomized relaxation of these problems by sampling constraints. The relaxed problem is a linear programming problem (LP). We then identify the number of samples needed for ``near-feasibility'' of the relaxed constraint set. Under some conditions on the valuation function, we show that value of the approximate LP is close to the optimal value. Simulation results show significant improvements of our proposed method over the Vickrey-Clarke-Groves (VCG) mechanism without rebates. In the special case of indivisible goods, the mechanisms in this paper fall back to those proposed by Moulin, by Guo and Conitzer, and by Gujar and Narahari, without any need for randomization. Extension of the proposed mechanisms to situations when the valuation functions are not known to the central planner are also discussed. Note to Practitioners-Our results will be useful in all resource allocation problems that involve gathering of information privately held by strategic users, where the utilities are any concave function of the allocations, and where the resource planner is not interested in maximizing revenue, but in efficient sharing of the resource. Such situations arise quite often in fair sharing of internet resources, fair sharing of funds across departments within the same parent organization, auctioning of public goods, etc. We study methods to achieve near budget balance by first collecting payments according to the celebrated VCG mechanism, and then returning as much of the collected money as rebates. Our focus on linear rebate functions allows for easy implementation. The resulting convex optimization problem is solved via relaxation to a randomized linear programming problem, for which several efficient solvers exist. This relaxation is enabled by constraint sampling. Keeping practitioners in mind, we identify the number of samples that assures a desired level of ``near-feasibility'' with the desired confidence level. Our methodology will occasionally require subsidy from outside the system. We however demonstrate via simulation that, if the mechanism is repeated several times over independent instances, then past surplus can support the subsidy requirements. We also extend our results to situations where the strategic users' utility functions are not known to the allocating entity, a common situation in the context of internet users and other problems.

Unified Vdd Vth Optimization Based DVFM Controller for a Logic Block

In this paper analytical expressions for optimal Vdd and Vth to minimize energy for a given speed constraint are derived. These expressions are based on the EKV model for transistors and are valid in both strong inversion and sub threshold regions. The effect of gate leakage on the optimal Vdd and Vth is analyzed. A new gradient based algorithm for controlling Vdd and Vth based on delay and power monitoring results is proposed. A Vdd-Vth controller which uses the algorithm to dynamically control the supply and threshold voltage of a representative logic block (sum of absolute difference computation of an MPEG decoder) is designed. Simulation results using 65 nm predictive technology models are given.

A Nash Bargaining Approach to Retention Enhancing Bid Optimization in Sponsored Search Auctions with Discrete Bids

Bid optimization is now becoming quite popular in sponsored search auctions on the Web. Given a keyword and the maximum willingness to pay of each advertiser interested in the keyword, the bid optimizer generates a profile of bids for the advertisers with the objective of maximizing customer retention without compromising the revenue of the search engine. In this paper, we present a bid optimization algorithm that is based on a Nash bargaining model where the first player is the search engine and the second player is a virtual agent representing all the bidders. We make the realistic assumption that each bidder specifies a maximum willingness to pay values and a discrete, finite set of bid values. We show that the Nash bargaining solution for this problem always lies on a certain edge of the convex hull such that one end point of the edge is the vector of maximum willingness to pay of all the bidders. We show that the other endpoint of this edge can be computed as a solution of a linear programming problem. We also show how the solution can be transformed to a bid profile of the advertisers.

Accurate Parameter Estimation of Contaminant Transport Inverse Problem using Paartaicle Swarm Optimization

Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

Robust Aeroelastic Optimization Of Composite Helicopter Rotor

A robust aeroelastic optimization is performed to minimize helicopter vibration with uncertainties in the design variables. Polynomial response surfaces and space-¯lling experimental designs are used to generate the surrogate model of aeroelastic analysis code. Aeroelastic simulations are performed at the sample inputs generated by Latin hypercube sampling. The response values which does not satisfy the frequency constraints are eliminated from the data for model ¯tting. This step increased the accuracy of response surface models in the feasible design space. It is found that the response surface models are able to capture the robust optimal regions of design space. The optimal designs show a reduction of 10 percent in the objective function comprising six vibratory hub loads and 1.5 to 80 percent reduction for the individual vibratory forces and moments. This study demonstrates that the second-order response surface models with space ¯lling-designs can be a favorable choice for computationally intensive robust aeroelastic optimization.