For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators ...
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For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough so that each spacecraft could use its own reference frequency. The conversion between reference frequencies and their distribution between all spacecrafts for the synchronization of the different metrology systems is the job of the inter-spacecraft frequency setting strategy, which is important for continuously acquiring scientific data and suppressing measurement noise. We propose a hierarchical optimization algorithm to solve the frequency setting strategy. The optimization objectives are minimum total readout displacement noise and maximum beat-note frequency feasible range. Multiple feasible parameter combinations were obtained for the Taiji program. These optimized parameters include lower and upper bounds of the beat note, sampling frequency, pilot tone signal frequency, ultrastable clock frequencies, and modulation depth. Among the 20 Pareto optimal solutions, the minimum total readout displacement noise was 4.12 pm/Hz, and the maximum feasible beat-note frequency range was 23 MHz. By adjusting the upper bound of beat-note frequency and laser power transmitted by the telescope, we explored the effects of these parameters on the minimum total readout displacement noise and optimal local laser power in greater depth. Our results may serve as a reference for the optimal design of laser interferometry system instrument parameters and may ultimately improve the detection performance and continuous detection time of the Taiji program.
Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multi...
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Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate's optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://***/quanshuzhang/***.
The time delays in the HVAC system are common, which seriously affect time series prediction and pre-control command implementation, so identifying them is promising research. The existing methods of identifying timed...
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The time delays in the HVAC system are common, which seriously affect time series prediction and pre-control command implementation, so identifying them is promising research. The existing methods of identifying timedelays mainly focus on establishing physical or dynamical models first and then conducting control variable experiments, which don't facilitate engineering. Therefore, we develop a model-free identification method rooted in an information-theoretic framework by introducing transfer entropy (TE) into the HVAC field. The suggested multivariate TE method can pick out each time-delay characteristic by mining monitoring data. Compared with correlation coefficients, it can filter redundant information between variables and discern the nonlinearity. Among them, to estimate multivariate TE well, a kernel estimator is improved. It performs the precise detection ability, low computational burden and strong robustness, compared with the original and knearest-neighbor (KNN) estimator. Besides, for an unacquainted HVAC system, a hierarchical optimization algorithm combining the Nash-optimizationalgorithm with a second-order oscillatory particle swarm optimization (SOPSO) algorithm is proposed to identify its time delays, where the accuracy and time cost are improved. Lastly, the above-mentioned methods are validated with simulated and real time series. This work is enlightening and has a further reference to identifying time delays in HVAC systems.
Radiant floor heating (RFH) is an advanced technology that can couple with renewable low-temperature sources and improve occupant thermal comfort. However, due to the large thermal inertia, the conventional control st...
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Radiant floor heating (RFH) is an advanced technology that can couple with renewable low-temperature sources and improve occupant thermal comfort. However, due to the large thermal inertia, the conventional control strategies based on heuristic rules have difficulty handling a room temperature response to changes in weather or set-points. Because of the excellent coping ability, model predictive control (MPC) has aroused increasing attention as a promising solution to manage RFH. This research is focused on the MPC scheme for RFH systems, especially large-scale RFH systems with hydraulic coupling. First, we develop a novel control-oriented thermodynamic model for an RFH system. A new calculating method for the floor surface temperature is adopted to avoid defining the thermal resistance and thermal capacity of the virtual core temperature layer, and the heat transfer along water flow and weather elements like solar radiation and wind are considered. Then an experiment is conducted to validate that the accuracy can be adequate for the prediction of MPC. Next, we design a distributed MPC scheme for RFH systems with hydraulic coupling. A supply reset rule for central equipment is improved, and a hierarchical optimization algorithm is proposed to implement the distributed MPC scheme. Then a simulation test is carried out with results showing that the scheme can achieve hydraulic decoupling between occupants, thus avoiding room temperature fluctuations and decreasing the root-mean-square error by 0.24 degrees C. An appropriate supply set-point is recommended that meets thermal comfort demands with minimum energy consumption.
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