Minimum Constraint Research Articles

A framework for simultaneous design of wind turbines and cable layout in offshore wind

Abstract. An optimization framework for simultaneous design of wind turbines (WTs) and cable layout for a collection system of offshore wind farms (OWFs) is presented in this paper. The typical approach used in both research and practical design is sequential, with an initial annual energy production (AEP) maximization, followed then by the collection system design. The sequential approach is robust and effective. However it fails to exploit the synergies between optimization blocks. Intuitively, one of the strongest trade-offs is between the WTs and cable layout, as they generally compete; i.e. spreading out WTs mitigates wake losses for larger AEP but also results in longer submarine cables in the collection system and higher costs. The proposed optimization framework implements a gradient-free optimization algorithm to smartly move the WTs within the project area subject to minimum distance constraint, while a fast heuristic algorithm is called in every function evaluation in order to calculate a cost estimation of the cable layout. In a final stage, a refined cable layout design is obtained by iteratively solving a mixed integer linear programme (MILP), modelling all typical engineering constraints of this particular problem. A comprehensive performance analysis of the cost estimation from the fast heuristic algorithm with respect to the exact model is carried out. The applicability of the method is illustrated through a large-scale real-world case study. Results shows that (i) the quality of the cable layout estimation is strongly dependent on the separation between WTs, where dense WT layouts present better performance parameters in terms of error, correlation, and computing time, and (ii) the proposed simultaneous design approach provides up to 6 % of improvement on the quality of fully feasible wind farm designs, and broadly, a statistically significant enhancement is ensured in spite of the stochasticity of the optimization algorithm.

Value-at-risk in the presence of asset price bubbles

ABSTRACT In this study, we respond to the criticism that the value-at-risk (VaR) measure fails during financial crises and is only applicable during periods without asset price bubbles. We propose a new dating mechanism that is based on the work of Phillips (2015) to date-stamp the origination and termination of the asset price bubbles. Our method relaxed the minimum bubble duration constraint in the original model, and the empirical application statistically identified the bubbles periods in nine stock markets (Australia, Canada, China, Germany, Spain, Hong Kong, Japan, the United Kingdom, and the United States). We choose the two most widely adopted VaR models (RiskMetrics and RiskMetrics 2006) to test the performance. Our results show that the RiskMetrics model fails in most periods, whereas the RiskMetrics 2006 performs efficiently in the periods with asset price bubbles. These results prove the criticism that all the VaR models fail during crises as invalid.

A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem

A recursive time aggregation-disaggregation (RAD) heuristic is proposed to solve large-scale multidimensional and multiperiod precedence-constrained knapsack problems (MMPKP) in which a profit is maximized by filling the knapsack in multiple periods while satisfying minimum and maximum resource consumption constraints per period as well as precedence constraints between items. An important strategic planning application of the MMPKP in the mining industry is the well-known open-pit mine block sequencing problem (BSP). In the BSP, a mine is modeled as a three-dimensional grid of blocks to determine a block extraction sequence that maximizes the net present value while satisfying constraints on the shape of the mine and resource consumption over time. Large real-life instances of this problem are difficult to solve, particularly with lower bounds on resource consumption. The advantage of the time aggregation-disaggregation heuristic over a rolling-horizon-based time decomposition is twofold: first, the entire horizon is considered for the resource consumption from the first aggregation; and second, only two-period subproblems have to be solved. This method is applied to a well-known integer programming model and a variant thereof in which blocks can be extracted in parts over multiple periods. Tests on benchmark instances show that near-optimal solutions for both of the models can be obtained for extremely large instances with up to 2,340,142 blocks and 10 periods.

Encoding and Decoding With Partitioned Complementary Sequences for Low-PAPR OFDM

In this study, we propose partitioned complementary sequences (CSs) where the gaps between the clusters encode information bits to achieve low peak-to-average-power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) symbols. We show that the partitioning rule without losing the feature of being a CS coincides with the non-squashing partitions of a positive integer and leads to a symmetric separation of clusters. We analytically derive the number of partitioned CSs for given bandwidth and a minimum distance constraint and obtain the corresponding recursive methods for enumerating the values of separations. We show that partitioning can increase the spectral efficiency (SE) without changing the alphabet of the non-zero elements of the CS, i.e., standard CSs relying on Reed-Muller (RM) code. We also develop an encoder for partitioned CSs and a maximum-likelihood-based recursive decoder for additive white Gaussian noise (AWGN) and fading channels. Our results indicate that the partitioned CSs under a minimum distance constraint can perform similar to the standard CSs in terms of average block error rate (BLER) and provide a higher SE at the expense of a limited signal-to-noise ratio (SNR) loss.

Inverse modeling of lichen growth curves and implications for lichenometric dating

Lichenometric dating represents a quick and affordable surface exposure dating method that has been widely used to provide a minimum age constraint on tectonic and geomorphic landscape changes as well as buildings and anthropogenic landscape changes in various settings during the late Holocene. Despite its widespread usage, this method has several limitations. Major problems relate to the sampling of lichen population on any given rock surface and the modeling of growth curves. In order to overcome these issues, it has been suggested to subdivide the rock surface into some areas and measure the largest lichen thallus on each one. However, how to express the data in terms of a probability distribution function and link it to an age of last exposure of the rock surface are still a matter of debate. Here, we propose a novel approach to the modeling of lichen growth curves by treating lichen growth as a continuous-time Markov process with a time-varying rate and additive Brownian noise. Given the growth rates, the probability distribution of the lichen population at any time can then be obtained by solving the Fokker–Planck equation. This method is illustrated using a dataset from the Huashan area of eastern China, which consists of measurements of the largest thalli on 12 rock surfaces of known age. We first build up the probability distribution of the lichen population for each rock surface based on extreme value theory and then use these to optimize the growth curve by minimizing the Jensen–Shannon divergence. A new method is also proposed to use the growth curve to map a sample of size data from an undated rock surface to the calendar age domain so as to yield a fully probabilistic estimate of the exposure age of the undated rock surface rather than a point estimate. • We propose a novel approach to lichenometric dating. • Lichen growth is modeled using a continuous-time Markov process. • Extreme-value theory is used to model the size distribution. • A MATLAB package for implementing the model is provided.

Non-Orthogonal Multiple Access: The Case of Improper Gaussian Signaling and Imperfect Successive Interference Cancellation

This paper studies a two-user downlink non-orthogonal multiple access (NOMA) system that adopts an improper Gaussian signaling (IGS) strategy to compensate for the performance loss due to imperfect successive interference cancellation (SIC). Joint optimization problems are formulated to maximize the overall spectral efficiency and energy efficiency of a two-user NOMA system under minimum user-rate requirements and total power constraints. Sub-optimal solutions of IGS circularity coefficients and power allocation are obtained for the formulated problems. Furthermore, improper constellation diagrams are designed using widely linear transformation with predefined optimized IGS coefficients to study the impact of IGS on throughput and error performance. Simulation results show that the performance of IGS-based NOMA systems, where the IGS strategy is adopted by both users, outperforms that of the proper Gaussian signal (PGS)-based NOMA system under imperfect SIC.

Decentralized route-planning for multi-vehicle teams to satisfy a subclass of linear temporal logic specifications

Linear temporal logic (LTL) formulae are widely used to provide high level behavioral specifications on mobile robots. We propose a decentralized route-planning method for a networked team of mobile robots to satisfy a common (global) LTL specification. The global specification is assumed to be a conjunction of multiple formulae, each of which is treated as a task to be assigned to one or more vehicles. The vehicle kinematic model consists of two modes: a hover mode and a constant-speed forward motion mode with bounded steering rate. The proposed method leverages the consensus-based bundle algorithm for decentralized task assignment, thereby decomposing the global specification into local specifications for each vehicle. We develop a new algorithm to assign collaborative tasks: those simultaneously assigned to multiple vehicles. Rewards in the proposed task assignment algorithm are computed using the so-called lifted graph, which ensures the satisfaction of minimum turn radius constraints on vehicular motion. This algorithm synchronizes the vehicles’ routes with minimum waiting durations. The proposed method is compared against a multi-vehicle task assignment algorithm from the literature, which we extend to apply for satisfying LTL specifications. The proposed route-planning method is demonstrated via numerical simulation examples and a hardware implementation on networked single-board computers.

Teeth out of proportion: Smaller horse and cattle breeds have comparatively larger teeth.

There are different descriptions of allometric relationships between important components of the mammalian skull. Craniofacial evolutionary allometry describes a pattern of increasing facial cranium in larger skulls. Another body of literature describes disproportionately larger teeth in smaller species or specimens, matching anecdotal observations with dental problems in dwarf breeds whose teeth appear "too large for their skulls." We test the scaling of tooth row length with body size and skull length in a data set comprising 114 domestic horses (representing 40 breeds)and in another data set of 316 domestic cattle (of>60 breeds). We demonstrate that smaller skulls have a relatively longer tooth row in both horses and cattle; larger specimens have relatively shorter tooth rows. Whereas in horses, larger skulls have a relatively longer diastema,the distance of the mesial maxillary premolar to the premaxilla was proportional to cranium length in cattle. While the reasons for these patterns remain to be detected, they support the hypothesis that tooth size might be less "evolvable," in terms of time required for changes, than body size. The pattern may affect (i) the selective breeding for dwarf breeds by setting minimum constraints for skull size, as described previously for domestic horses with the same data set; (ii) the susceptibility of small breeds for dental problems; and (iii) differences in chewing efficiency between breeds of different sizes. The findings support the existing concept that scaling of tooth to body size across taxa becomes more isometric the longer these taxa are separated in evolutionary time.

Power Optimization of IRS-Assisted D2D System Based on Imperfect Channel

Intelligent reflecting surface (IRS) is a promising technology that can help wireless communications achieve efficient spectrum and energy efficiency. However, because of its weak signal processing ability, it is difficult to get ideal channel state information (CSI). Under the imperfect channel state information hypothesis, we investigate a device-to-device (D2D) offload network. And an IRS is used to help calculate offloading from one set of task-intensive users to another set of idle users. We aim to jointly optimize transmit beamforming and IRS phase shifts to minimize system transmit power while requiring each user’s rate to meet the minimum rate constraint in the presence of channel errors. Unfortunately, the problem presented is nonconvex, and the imperfection of CSI makes it even more difficult to solve. Therefore, we apply the S-Procedure to convert the original problem to two effectively solvable semidefinite programming (SDP) subproblems and then solve them through the convex-concave procedure (CCP) algorithm and the alternate optimization method. Numerical results show the effectiveness of the algorithm and verify that the assistance of the IRS can greatly save the system transmit power.

Renewable energy targets and unintended storage cycling: Implications for energy modeling

SummaryTo decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in energy models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.

Research on self-reconfiguration strategy of modular spherical robot

Self-reconfigurable robot is a complex system composed of multiple modular robots. Aiming at high efficiency and low energy consumption of self-reconfigurable robot configuration transformation, a self-reconfiguration strategy based on module mapping of the common parts is proposed. This strategy describes the configuration of the robot in the form of a graph, and a method to determine the central node of configuration is proposed. The central node module as the starting node for comparison of different configurations, and the common part between the two configurations is reserved. Then the module closest to the target module is searched, the target configuration is reconfigured from the inside to the outside with the minimum energy consumption constraint. Finally, based on the experiment results, compared with other self-reconfiguration strategies, the proposed self-reconfiguration strategy reduces the times of reconfiguration operations and improves the reconfiguration efficiency.

T- and Cascaded Pi-Shaped 1H T/R Switches with Realistic Trace Width for 7 Tesla MRI

In this work, two designs of Transmit/Receive (T/R) switches are introduced for 7-Tesla magnetic resonance imaging. The T-shaped and the two cascaded Pi-shaped microstripline-based T/R switches for 1H MRI are designed with the minimum trace width constraint that handles the required power and assures a compact size of the switch. When the peak power of the Radio Frequency (RF) amplifier signal is 1 kW with 10% duty cycle, the average current that will pass through 50 Ω input impedance circuit is 2 A. Based on IPC 2221 standard for PCB traces, the minimum trace width that handles such a current is of 0.513 mm. The ADS software is used in designing the T as well as two sections pi-shaped switches. All the proposed 7-Tesla 1H MRI switches have revealed good matching (<-20 dB), low insertion loss (<0.34 dB), and high isolation (>60 dB) during transmit and receive. The designed switches are compact in size by around 40% reduction in dimensions compared to a conventional switch size (100 mm × 100 mm) without folding.

Toward an Optimal Selection of Constraints for Terrestrial Reference Frame (TRF)

Given that the observations from current space geodetic techniques do not carry all the necessary datum information to realize a Terrestrial Reference System (TRS), and each of the four space geodetic techniques has limits, for instance: Very Long Baseline Interferometry (VLBI) ignores the center of mass and satellite techniques lack the TRS orientation, additional constraints have to be added to the observations. This paper reviews several commonly used constraints, including inner constraints, internal constraints, kinematic constraints, and minimum constraints. Moreover, according to their observation equations and normal equations, the similarities and differences between them are summarized. Finally, we discuss in detail the influence of internal constraints on the scale of VLBI long-term solutions. The results show that there is a strong correlation between the scale parameter and the translation parameter introduced by the combination model at the Institut National de l’Information Géographique et Forestière (IGN), and internal constraints force these two groups of parameters to meet certain conditions, which will lead to the coupling of scale and translation parameters and disturbing the scale information in VLBI observations. The minimum or kinematic constraints are therefore the optimum choices for TRF.

Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

A symmetry-based hybrid precoder and combiner is a high spectral efficiency structure in millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system. To improve the spectral efficiency of the mmWave mMIMO-NOMA system, we first propose a user grouping scheme to suppress the strong inter-user interference caused by NOMA, then the hybrid precoder based on user channel alignment and the zero-forcing algorithm is constructed to further improve the signal-to-interference-plus-noise ratio (SINR) of the receiver. Subsequently, the non-convex spectral efficiency optimization problem is transformed into a convex optimization problem of inter-cluster power allocation and the closed-form solution for the optimal power under the minimum rate constraint is obtained by solving the KKT condition to further improve the spectral efficiency. The simulation results show that the proposed scheme can achieve higher spectral efficiency compared to orthogonal multiple access (OMA), fixed power allocation (FPA), K-means, and cluster head selection (CHS).

Semi-Active Cable Damping to Compensate for Damping Losses Due to Reduced Cable Motion Close to Cable Anchor

The relative motion of transverse cable dampers is smaller than predicted by the taut string model because of the effects of bending stiffness and fixed support conditions. As a result of the reduced damper motion, the dissipated energy per cycle is reduced as well, which may explain why damping measurements on real stay cables with transverse dampers often show lower cable damping ratios than expected from the taut string theory. To compensate for the reduced damper motion and damper efficiency, respectively, a semi-active cable damper is proposed. The controllable damper is realized by a hydraulic oil damper with real-time controlled bypass valve whereby the resulting damper force is purely dissipative. The proposed control law is clipped viscous damping with negative stiffness. The viscous coefficient is adjusted in real time to the actual frequency of vibration to generate optimum modal damping while the negative stiffness component partially compensates for the reduced damper motion due to the flexural rigidity and fixed support conditions of the cable. The measurements of the prototype semi-active hydraulic damper are used to derive a precise model of the semi-active damper force including the control force constraints due to the fully open and fully closed bypass valve. This model is used to compute the cable damping ratios of the first four cable modes, for typical damper positions, for a taut string model and for a cable model with flexural rigidity and fixed supported ends. The obtained cable damping ratios are compared to those resulting from the passive linear viscous damper being optimized to the first four cable modes. The results demonstrate that the proposed semi-active cable damper with the consideration of the minimum and maximum control force constraints significantly enhances the cable damping of the first four modes compared to the linear viscous damper.

Double Intelligent Reflecting Surface-Assisted Multi-User MIMO Mmwave Systems With Hybrid Precoding

This work investigates the effect of double intelligent reflecting surface (IRS) in improving the spectrum efficient of multi-user multiple-input multiple-output (MIMO) network operating in the millimeter wave (mmWave) band. Specifically, we aim to solve a weighted sum rate maximization problem by jointly optimizing the digital precoding at the transmitter and the analog phase shifters at the IRS, subject to the minimum achievable rate constraint. To facilitate the design of an efficient solution, we first reformulate the original problem into a tractable one by exploiting the majorization-minimization (MM) method. Then, a block coordinate descent (BCD) method is proposed to obtain a suboptimal solution, where the precoding matrices and the phase shifters are alternately optimized. Specifically, the digital precoding matrix design problem is solved by the quadratically constrained quadratic programming (QCQP), while the analog phase shift optimization is solved by the Riemannian manifold optimization (RMO). The convergence and computational complexity are analyzed. Finally, simulation results are provided to verify the performance of the proposed design, as well as the effectiveness of double-IRS in improving the spectral efficiency.

Joint Power and User Grouping Optimization in Cell-Free Massive MIMO Systems

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

Three-dimensional regularized inversion of magnetotelluric data with a minimum support gradient constraint

Regularization is an effective technique to obtain stable solutions for 3D magnetotelluric (MT) inversion. Smooth constraints are commonly used in traditional inversion algorithms. It is unsuitable if anomalies occur in block patterns. In some cases, it is necessary to obtain sharp boundaries. Some studies have proposed stabilizing functionals to obtain clear and sharp boundaries, such as minimum support (MS) and minimum gradient support (MGS) functionals; however, the optimal value of the focusing parameter should be selected to produce reliable inversion results. In this study, we expanded a new stabilizing functional that we called minimum support gradient (MSG) stabilizing functional as a stabilizer in 3D MT inversion. We developed a Gauss-Newton (GN) algorithm based on the edge-based finite-element method (FEM) to invert the MT data. In the inversion process, the forward modeling and the adjoint problem are solved by the Math Kernel Library (MKL) PARDISO parallel direct solver. we also parallelized the inversion program over frequencies by using Message Passing Interface (MPI) to further speed up the inversion process. Two synthetic models are tested to compare the inversion results for different stabilizing functionals. Compared to traditional smooth inversion, the MSG inversion can clearly distinguish the boundaries of the anomalies. The inverted resistive targets are closer to the true test model. Additionally, the MSG inversion results are stable and robust for a wide range of focusing parameter values, indicating that our method is effective and easy to implement. Finally, the proposed algorithm is applied to the long MT data from the USArray component of EarthScope project in the United States.

Formation control for networked multiagent systems with a minimum energy constraint

Minimum-energy formation achievement problems for networked multiagent systems are investigated, where information networks with leaderless and leader-follower structures are respectively addressed and information networks are randomly switching. The critical feature of this work is that the energy constraint is minimum in the sense of the linear matrix inequality, but limited-budget control and guaranteed-cost control cannot realize a minimum-energy formation. Firstly, the leaderless minimum-energy formation control problem is converted into an asymptotic stability one via a nonsingular transformation and state space decomposition, and based on linear matrix inequality techniques, sufficient conditions for analysis and design of leaderless minimum-energy formation achievement are proposed, respectively, which can be solved by the generalized eigenvalue method. Then, main results of minimum-energy formation achievement of leaderless networked multiagent systems are extended leader-follower networked multiagent systems, where the asymmetric property of the leader-follower information network is well dealt with by two nonsingular transformations. Finally, two simulation examples are shown to verify the main results for minimum-energy formation achievements of leaderless and leader-follower networked multiagent systems, respectively.

Multirow Adjoint-Based Optimization of NICFD Turbomachinery Using a Computer-Aided Design-Based Parametrization

Abstract Currently, most of the adjoint-based design systems documented in the open literature assume that the fluid behaves as an ideal gas, are restricted to the optimization of a single row of blades, or are not suited to impose geometric constraints. In response to these limitations, this paper presents a gradient-based shape optimization framework for the aerodynamic design of turbomachinery blades operating under nonideal thermodynamic conditions. The proposed design system supports the optimization of multiple blade rows, and it integrates a computer-aided design (CAD)-based parametrization with a Reynolds-averaged Navier–Stokes (RANS) flow solver and its discrete adjoint counterpart. The capabilities of the method were demonstrated by performing the design optimization of a single-stage axial turbine that employs isobutane (R600a) as working fluid. Notably, the aerodynamic optimization respected the minimum thickness constraint at the trailing edge of the stator and rotor blades and reduced the entropy generation within the turbine by 36%, relative to the baseline, which corresponds to a total-to-total isentropic efficiency increase of about 4 percentage points. The analysis of the flow field revealed that the performance improvement was achieved due to the reduction of the wake intensity downstream of the blades and the elimination of a shock-induced separation bubble at the suction side of the stator cascade.