ABSTRACT Flywheel array energy storage systems (FAESS), due to their high power density, rapid response time, and long operational lifespans, have come to be recognized as one of the best alternatives for renewable energy storage on a large scale. However, the scarcity of efficient working energy systems results in impeded performance and reliability of the entire system. This paper presents a new distributed architecture of the Geyser‐Inspired Algorithm (GEA), which allows energy loss minimization using a dynamic load assignment among flywheels. This architecture uses dynamic load sharing among flywheels to minimize energy loss. The algorithm works in a distributed way, with each flywheel unit running its own version of the control logic that was inspired by geyser dynamics, enabling real‐time responses to dynamic load changes and system failures. The effectiveness of the proposed GEA is verified through extensive simulations and experimental validation. In simulations, GEA outperforms conventional control strategies such as Proportional Allocation and Round‐Robin Scheduling, showing a reduction in total energy losses by up to 30%, an average State of Charge (SoC) imbalance improvement to 6.2%, and a significantly enhanced real‐time responsiveness with an average response time of about 0.8 s. Moreover, parameter sensitivity analysis demonstrated robust performance across different operational thresholds, with minimal variations in energy loss and response time, confirming the stability and adaptability of the proposed method. Additional validation scenarios, including random load fluctuations and multiple simultaneous flywheel failures, further confirmed the robustness and fault‐tolerance of GEA. Scalability analysis also indicated efficient computational performance, with execution times increasing modestly from 0.85 ms for four flywheels to 4.60 ms for twenty‐four flywheels, underscoring GEA's applicability in larger‐scale energy storage applications. Through the integration of nature‐influenced heuristics and engineering tools in a consolidated manner, our study highlights an avenue through which the design of robust, scalable, and fault‐tolerant control methods in large‐scale electrical energy storage systems is made possible. Given that the point of distribution of the Geyser‐inspired algorithm allows for lesser losses and greater adaptability in the fast‐changing power grid, the distributed Geyser‐inspired algorithm is key in the development of FAESS, a type of battery energy storage system.

In energy storage systems for autonomous vehicles, flywheel energy storage machines still suffer from high rotating iron consumption, a weak rotor structure, and poor robustness. As a flywheel energy storage device, this study employs a homopolar machine with a doubly salient solid rotor to address these issues. It has a simple design, a strong rotor, and reduced rotational loss at high speeds. It is given a fuzzy incremental proportional integral derivative (FIPID) intelligent control strategy. A simulation experiment is used to implement the novel optimization method. The constant speed sensitivity is enhanced by a factor of 20, and the torque variation is reduced by a factor of 6. The modeling and testing data show that the simulation and experimental results are reasonable. This shows that the proposed improved FIPID controller system and the considered intelligent homopolar machine system are effective, precise, stable, and respond in a dynamic way. It will be considered for applications in flywheel energy storage systems for autonomous vehicles with stored energy up to 500 MJ and power ranges from KW to GW. Its application will enhance the energy storage capacity of autonomous vehicles.Note to Practitioners—In this research we considered the urgent need of flywheel energy-storage machine system of new-energy autonomous vehicle for high-speed machine and found out energy-efficient, environment-friendly and high-efficiency automatic control algorithm. Previous researches on energy-storage machine were mainly focused on heteropolar machines which tend to have defects such as large iron loss in rotation, low rotor structural strength and poor robustness, etc., resulting in limited energy conversion efficiency of the flywheel energy-storage machine of autonomous vehicle during high-speed operation. Very few people ever chose homopolar machine from high-speed machines for energy storage control. In this paper, we chose a new type of homopolar machine with topology structure to minimize the machine’s iron loss in high-speed rotation and to enhance the rotor’s structural strength. We also put forward a new control algorithm matching with this machine, in order to further improve the system control performances including control precision and reliability. The simulation experiment of fuzzy incremental PID control system of homopolar machine for flywheel energy storage of autonomous vehicle and the actual data-driven simulation result showed that this algorithm realized more precise speed regulation and better reduced faults like torque ripple than the classical PID control algorithm, and further improved the machine efficiency within the whole operating speed and power ranges, and realized the dual purposes of quick charge and stable and safe operation of the flywheel energy storage machine. This algorithm provides theoretical and data validation support for the application of the flywheel energy storage into the autonomous vehicle system. And it can be readily implemented and incorporated into large-scale industrial computing systems.

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen, completely thawed, and F/T transition states. Firstly, the calibrated Cyclone GNSS (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of Soil Moisture Active and Passive F/T data and in-situ data, the detection accuracies of CYGNSS reach 75.1 and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the completely frozen and completely thawed states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing, last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil in the daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.

Photoacoustic microscopy (PAM) is a promising imaging modality because it is able to reveal optical absorption contrast in high resolution on the order of a micrometer. It can be applied in an endoscopic approach by implementing PAM into a miniature probe, termed photoacoustic endoscopy (PAE). Here we develop a miniature focus-adjustable PAE (FA-PAE) probe characterized by both high resolution (in micrometers) and large depth of focus (DOF) via a novel optomechanical design for focus adjustment. To realize high resolution and large DOF in a miniature probe, a 2-mm plano-convex lens is specially adopted, and the mechanical translation of a single-mode fiber is meticulously designed to allow the use of multi-focus image fusion (MIF) for extended DOF. Compared with existing PAE probes, our FA-PAE probe achieves high resolution of [Formula: see text] within unprecedentedly large DOF of 3.2 mm, more than 27 times the DOF of the probe without performing focus adjustment for MIF. The superior performance is first demonstrated by imaging both phantoms and animals including mice and zebrafish in vivo by linear scanning. Further, in vivo endoscopic imaging of a rat's rectum by rotary scanning of the probe is conducted to showcase the capability of adjustable focus. Our work opens new perspectives for PAE biomedical applications.

Global Navigation Satellite System Reflectometry (GNSS-R) is a passive technique for remote sensing of soil moisture, which has continuous all-day and all-weather applicability on different platforms. New GNSS signals with advanced modulation and higher power are expected to improve the performance of GNSS-R. In this study, we performed a ground-based dual-antenna GNSS-R experiment on farmland and collected 15-min raw intermediate frequency data with central frequency of 1175.42 MHz hourly over two different 24-h periods. The power ratio between the direct and reflected signals from QZSS satellites were computed using a self-developed software-defined receiver with 1-ms coherent integration and 200-ms incoherent adds. Then, soil moisture was resolved using a semiempirical model based on the power ratios. Solutions were evaluated using measurements obtained using a time-domain reflectometry probe. Results demonstrated that signal power-ratio-based QZSS signals can be used to retrieve soil moisture under bare soil conditions. Moreover, for signal power-ratio-based case, results from geostationary orbit (GEO) satellite signals (STD: 0.013 m3/m3 and 0.007 m3/m3) performed better than those from inclined geosynchronous orbit (IGSO) satellite signals (STD: 0.033–0.071 m3/m3).

Compared with tide gauges, Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) can provide low-cost, long-term sea-level data that are not susceptible to crustal loading. Signal-to-noise ratio (SNR) observables in GNSS files are commonly used for GNSS-MR; however, these observables are not always present, especially in early GNSS files. Several different combinations of codes and carrier-phases for GNSS-MR as substitutes to extract sea level have been proposed; however, the requirement of these methods for application of cycle slip detection or multi-frequency observations to isolate multipath signals reduces their applicability. Here, we propose a new method for sea-level estimation using Signal Strength Indicator (SSI) data in GNSS observation files, which is an alternative to existing methods because SSI data always exist. To verify the proposed method, we used four multi-GNSS data from three stations to monitor sea level. Sea-level estimations with root-mean-square errors of 7–8, 5–9, 12–15 and 9–13 cm relative to in-situ data were retrieved, and the correlation coefficients for these stations were bigger than 0.98, 0.98, 0.93 and 0.96, respectively. Moreover, the proposed method measures sea levels with precision similar with the traditional SNR method. In addition, sea-level results derived from the proposed method at these stations were further applied to estimate ocean tides. Ocean-tide coefficients for several main tides determined by different data were in good agreement.