Top-level Optimization Research Articles

Automated Scheduling of Doppler Exoplanet Observations at Keck Observatory

Precise Doppler studies of extrasolar planets require fine-grained control of observational cadence, i.e., the timing of and spacing between observations. We present a novel framework for scheduling a set of Doppler campaigns with different cadence requirements at the W. M. Keck Observatory. For a set of observing programs and allocated nights on an instrument, our software optimizes the timing and ordering of ∼1000 observations within a given observing semester. We achieve a near-optimal solution in real-time using a hierarchical Integer Linear Programming framework. Our scheduling formulation optimizes over the roughly 103000 possible orderings. A top level optimization finds the most regular sequence of allocated nights by which to observe each host star in the request catalog based on a frequency specified in the request. A second optimization scheme minimizes the slews and downtime of the instrument. We have assessed our algorithms performance with simulated data and with the real suite of Doppler observations of the California Planet Search in 2023.

Highly tunable and ultrasensitive hybrid plasmonic platform for multi-spectral sensing: Non-invasive diabetes surveillance and global warming directives

The development of tunable and extremely sensitive devices, as well as the emergence of metamaterials, has tremendously aided the evolution of plasmonic sensing technologies. However, the natural property of metamaterials severely restricts the scalability and application potential of mono-functional sensor systems. To address this shortcoming, we have investigated an ultranarrow five-band plasmonic sensor with structural tunability and high sensing behaviors in compatibility with multi-purpose application scenarios, using the finite-difference time-domain approach rigorously. Meanwhile, we theoretically formulated a hybridization model for the mutual induction of Fabry–Pérot resonances and localized surface plasmon polariton resonance, providing a theoretical basis for the utilization and conception of the highly-sensitive tunable plasmonic sensor. Most notably, we discovered and interpreted far-field phase extinction inducing separation of polarization resonance peaks in physical mechanisms. The tunability of its mutual inductance effect at the intersection distances from two-intersecting silver nanogroove was adequately exploited to further validate and effectively attain five ultra-narrow absorptivities of 96.70%, 93.93%, 99.73%, 100.00%, and 89.72% by top-level optimization. In addition, the redundant proximity between nearby peaks is compensated, extending the operating band and enhancing the practicability in multi-purpose scenarios. As a reliable source for future optical sensing, it performs well in the visible to near-infrared region, enabling detection response to non-invasive diabetes mellitus, global climate warming, and unrestricted temperature and pressure measurement. Meanwhile, minimum FWHM = 4.806 nm and maximum FOM = 115.13 RIU−1 are obtained at 293.15 K, with an associated highest sensitivity of 557.93 nm/RIU. Moreover, the fabrication technologies development of electron beam lithography and focused ion beam etching technologies enables large-scale mass production at low cost and long-term stability.

Smart management system in electric market considering customer demand response

The case of a disruptive agent that tries to maximize damage to the system (determined by total network operating costs), while the system operator is taking the appropriate steps to minimize the consequences of the attack, is considered in the present study. A bi-level optimization problem is used to model the interaction between the two agents. One aspect of this is that the destructive factor is involved in the top-level optimization problem and should choose that lines and generators to disable (with a restricted destructive budget). Therefore, a new scheme is proposed for maximizing the system's resilience under intentional attacks, by implementing distributed energy resources (DERs) such as distributed generation, and demand response. Also, the grey wolf optimization algorithm is used to solve the optimization problem in the paper. By allocating DERs in islands produced by destroyed lines and generators, three metrics have been suggested in order to evaluate resilience.

Bi-level planning for integrated energy systems incorporating demand response and energy storage under uncertain environments using novel metamodel

The optimal planning and design of an integrated energy system (IES) is of great significance to facilitate distributed renewable energy (DRE) technology and improve the overall energy efficiency of the energy system. With the increased penetration of distributed generation (DG), the power supply and load sides of an IES present more increased levels of uncertainties. Demand response (DR) and the energy storage system (ESS) serve as important means to shift energy supply and use across time to counter the indeterminate variations. However, the current IES planning methods are unable to effectively deal with the uncertainties of DREs and loads, and to optimize the operations of DG-DR-ESS due to the enormous possible combinations. In this paper, a new method for the optimal planning and design of an integrated energy system has been introduced and verified. The new method consists of three integrated elements. First, the method of the probability scenario has been used to model the uncertainties of the DREs and loads so as to better characterize the impact of uncertainty on the planning and design of the IES. Secondly, the optimal operation of the IES under different probability scenarios is ensured using the second-order cone optimization for quick solutions due to the simplicity of this sub-problem, serving as the bottom-level optimization. Thirdly, the optimal planning and design of IES through optimal sizing of the power generating components and ESS are performed using a special meta-model based global optimization method due to the complex, black-box, and computation intensive nature of this top-level optimization in a nested, bi-level global optimization problem. The combined approach takes full account of the interrelated operations of DG-DR-ESS under different design configurations to support a better optimal planning and design of the IES. The simulation has been carried out on an IES system modified from the IEEE 33-node distribution system. The simulation results show that the proposed method and model are effective.

Weight and mechanical performance optimization of blended composite wing panels using lamination parameters

In this paper, a lamination parameter-based approach to weight optimization of composite aircraft wing structures is addressed. It is a bi-level procedure where at the top level lamination parameters and numbers of plies of the pre-defined angles (0, 90, 45 and −45°) are used as design variables, the material volume is treated as an objective function to be minimized subject to the buckling, strength and ply percentage constraints. At the bottom level the optimum stacking sequence is obtained subject to the requirements on blending and preservation of mechanical properties. To ensure composite blending, a multi-stage optimization is performed by a permutation genetic algorithm aiming at matching the lamination parameters passed from the top level optimization as well as satisfying the layup rules. Two new additional criteria, the 90° ply angle jump index and the stack homogeneity index, are introduced to control the uniformity of the three ply angles (0, 90, 45 and −45) spread throughout the stack as well as improve the stack quality and mechanical performance by encouraging 45° angle change between neighbouring groups of plies. The results of the application of this approach are compared to published results to demonstrate the potential of the developed technique.

Bilevel Optimization Strategy for Aircraft Wing Design Using Parallel Computing

A new bilevel optimization strategy for wing design is developed, in which the optimizations of the wing-planform and wing-airfoil shapes are decoupled from each other. The design of the wing-planform shape and the shape of the airfoils in several spanwise positions are considered as the goal of the optimization. In the new approach, the design problem is decomposed into a series of subproblems based on the design variables. The design variables defining the wing-planform shape are optimized in a top-level optimization, and the design variables defining the shape of airfoils in several spanwise positions are optimized in several sublevel optimizations. To take into account the influence of the airfoil shape in a specific spanwise position on the shape of the airfoils in other spanwise positions, a series of design variables are added to the design vector of the top-level optimization. The top-level optimizer is responsible for the consistency of the optimization. Using this approach, the number of design variables in the top-level optimization is reduced; the airfoils in several spanwise positions are optimized in parallel; and, instead of complex three-dimensional aerodynamic and structural solvers, much simpler and faster two-dimensional airfoil analysis tools can be used.

A lamination parameter-based strategy for solving an integer-continuous problem arising in composite optimization

A bi-level optimization strategy for finding the optimal ply numbers and stacking sequence in composite structures has become one of the most popular techniques in recent years. When the optimization technique is based on the use of lamination parameters, the top level optimization has two subsets of design variables for each substructure (e.g., a panel in wing design): lamination parameters treated as continuous design variables, and three integers that define the number of plies of 0°, 90° and ±45° orientation. When a continuous optimizer is used at the top level, there is a need for an algorithm to find an integer representation of the obtained continuous number of plies that, ideally, does not alter the mechanical performance of a panel. The focus of this paper is on solving the top level optimization problem whereas the description of local level optimization problem that arranges the stacking sequence can be found in the authors’ previous work. In order to determine the integer values of the ply numbers, two schemes based on the lamination parameter matching are introduced in this paper. The strategy is to use a binary code controlling the integer representation of ply numbers in order to obtain a discrete number of plies of each orientation per composite panel. An optimization problem is formulated where the objective function (to be minimized) defines how close the lamination parameter values and the panel thickness, obtained in the top level optimization, are to their values when integer ply numbers are considered. Such an optimization problem is solved by a permutation GA for each individual panel. A wing box benchmark problem is used to demonstrate the potential of these methods.

Bilevel Optimization of Blended Composite Wing Panels

Two approaches are examined for finding the best stacking sequence of laminated composite wing structures with blending and manufacturing constraints: smeared-stiffness-based method and lamination-parameter-based method. In the firstmethod, thematerial volume is the objective function at the global level, and the stack shuffling to satisfy blending and manufacturing constraints is performed at the local level. The other method introduced in this paper is to use lamination parameters and numbers of plies of the predefined angles (0, 90, 45, and 45 deg) as design variables with buckling, strain, and ply percentage constraints while minimizing the material volume in the top-level optimization run.Given lamination parameters from the top-level optimization as targets for the local level, an optimal stacking sequence is determined to satisfy the global blending requirements. On a benchmark problem of an 18-panel wing box, the results from these two approaches are compared to published results to demonstrate their potential.

A Multidisciplinary Design Optimization Approach to Relating Affordability and Performance in a Conceptual Submarine Design

A multidisciplinary design optimization (MDO) framework is used for a conceptual submarine design study. Four discipline-level performances—internal deck area, powering, maneuvering, and structural analysis—are optimized simultaneously. The four discipline-level optimizations are driven by a system level optimization that minimizes the manufacturing cost while at the same time coordinates the exchange of information and the interaction among the discipline-level optimizations. Thus, the interaction among individual optimizations is captured along with the impact of the physical characteristics of the design on the manufacturing cost. A geometric model for the internal deck area of a submarine is created, and resistance, structural design, and maneuvering models are adapted from theoretical information available in the literature. These models are employed as simulation drivers in the discipline-level optimizations. Commercial cost-estimating software is leveraged to create a sophisticated, automated affordability model for the fabrication of a submarine pressure hull at the system level. First, each one of the four discipline optimizations and also the cost-related top level optimization are performed independently. As expected, five different design configurations result, one from each analysis. These results represent the "best" solution from each individual discipline optimization, and they are used as reference for comparison with the MDO solution. The deck area, resistance, structural, maneuvering, and affordability models are then synthesized into a multidisciplinary optimization statement reflecting a conceptual submarine design problem. The results from this coordinated MDO capture the interaction among disciplines and demonstrate the value that the MDO system offers in consolidating the results to a single design that improves the discipline-level objective functions while at the same time produces the highest possible improvement at the system level.

Decomposition-Based Simultaneous Stabilization with Optimal Control

for aircraft flight control operating under different conditions, when they are represented by a collection of linear dynamic models. The single controller brings continuity and a level of reliability. This paper presents a decomposition approach for the solution to the simultaneous stabilization problem. A bilevel design optimization architecture is adopted in which design of each individual plant (flight condition) is taking place at the bottom level, and the top-level optimization aims for single-control convergence of those individual controllers. Furthermore, performance requirements can be taken into account concurrently with the stabilization process, thanks to the separatebileveldecompositionconcept.Theeffectivenessoftheproposedapproachisillustratedbydifferentaircraft control system design test cases.

ALADDIN: an optimized ground-based precursor for DARWIN

The ALADDIN concept is an integrated Antarctic-based L-band experiment whose purpose is to demonstrate nulling interferometry and to prepare the DARWIN mission. Because of their privileged location, the relatively modest collectors (1 m) and baseline (up to 40 m) are sufficient to achieve a sensitivity (in terms of detectable zodi levels) which is about twice better than that of a nulling instrument on a large interferometer (such as GENIE at the VLTI), and to reach the 20-zodi threshold value identified to carry out the DARWIN precursor science. These numbers are based on a preliminary design study by Alcatel Alenia Space and were obtained using the same simulation software as the one employed for GENIE. The integrated design enables top-level optimization and full access to the light collectors for the duration of the experiment, while reducing the complexity of the nulling breadboard.