Variety Of Atmospheric Conditions Research Articles

Integrating supervised and reinforcement learning for predictive control with an unmodulated pyramid wavefront sensor for adaptive optics

We propose a novel control approach that combines offline supervised learning to address the challenges posed by non-linear phase reconstruction using unmodulated pyramid wavefront sensors (P-WFS) and online reinforcement learning for predictive control. The control approach uses a high-order P-WFS to drive a tip-tilt stage and a high-dimensional mirror concurrently. Simulation results demonstrate that our method outperforms traditional control techniques, showing significant improvements in performance under challenging conditions such as faint stars and poor seeing, and exhibits robustness against variations in atmospheric conditions.

A review on free space optical communication links: 5G applications

Abstract Free space optical (FSO) communications has garnered significant attention as a potential substitute for radio frequency (RF) wireless communication in short and long range transmissions. Free-space optical (FSO) communication is a method where an optical beam is sent across an unguided channel, gaining favour in high-speed wireless networks due to its large optical bandwidth, fast data transmission, lack of licencing requirements, secure nature, and easy setup. Despite several inherent advantages that FSO has over existing radio frequency (RF) communication technologies, its link performance is severely hampered by atmospheric fog and turbulence, which not only reduce optical irradiance but also cause the modulated optical beam to deviate from its intended path, impacting the quality of the received signal. This review paper discusses FSO technology, channel impairments under varied atmospheric attenuation conditions, advantages, and applications. Various channel models in terms of probability density functions are described, which characterise the statistical nature of the irradiance fluctuations of optical transmission through scattering and atmospheric turbulence. Key objectives that need to be addressed in the near future, particularly in fifth-generation (5G) and beyond, includes enhancing capacity, boosting data rates, minimising latency, and enhancing service quality. Last but not the least, several uses of FSO that facilitates 5G and future technologies have also been covered in this study.

Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.

A multi-fidelity approach for wind farm simulations and comparison with field data

The prediction of energy production and structural loads within wind farms is of high interest for wind industries to optimize the wind farm design and control under a variety of atmospheric conditions. However, it is still a key challenge to predict them accurately and efficiently due to the complex interactions between wind turbines and turbulent flows. Nowadays, high-fidelity simulations using Reynolds-Averaged Navier-Stokes (RANS) or Large-Eddy Simulation (LES) coupled with actuator disk or actuator line method are widely developed and used in wind farm analysis, but it still needs a huge computational resource, especially in the application of a commercial wind farm with large number of turbines. In this context, a mid-fidelity simulation tool based on the Dynamics Wake Meandering (DWM) model called FAST.Farm has been developed by National Renewable Energy Laboratory (NREL) in order to tackle this challenge. It allows to capture essential physics at the turbine scale as well as at the farm scale in a computational efficient manner. This study focuses on the calibration of DWM model which is the key to minimize inaccuracies between mid-fidelity and high-fidelity simulations regarding key performance indicators, e.g. thrust and power production. The calibration is made for DTU10MW wind turbine and shows a good improvement in accuracy compared to default parameters. The calibrated DWM model is finally employed to forecast the power generation for two turbines within a reference wind farm. A good improvement on the prediction is also obtained thanks to the calibrated parameters.

Analytical Wake Modeling in Atmospheric Boundary Layers: Accounting for Wind Veer and Thermal Stratification

Reliable characterization of wind turbine wakes in the presence of Atmospheric Boundary Layer (ABL) flows is crucial to accurately predict wind farm performance. Wind veering in the ABL shears the wake in the lateral direction, and wind veer strength depends on the thermal stability of the ABL. Analytical wake modeling approaches must capture these ABL effects to ensure correct prediction of the wake structure under varied atmospheric conditions. To this end, a new physics-based analytical wake model is developed in this study that is capable of predicting the shape of wakes influenced by wind veer and thermal stratification effects. This model combines a novel ABL wind field model with the Gaussian wake model. The new ABL wind model is capable of predicting both the streamwise and spanwise velocity components in conventionally neutral (CNBL) and stable (SBL) ABL flows. The analytical expressions for both of these horizontal velocity components adhere to Monin-Obukhov Similarity Theory (MOST) in the surface layer, while capturing wind veering in the outer layer of the ABL. Incorporating this ABL model with the Gaussian wake model predicts laterally deflected wake shapes in a fully predictive and self-consistent fashion for a wide range of atmospheric conditions. The results also demonstrate that the enhanced wake model gives improved predictions relative to Large Eddy Simulations of power losses due to wake interactions under strongly stably stratified atmospheric conditions, where wind veer effects are dominant.

Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation

Abstract The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m2, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.

Maximum power point tracking using unsupervised learning for photovoltaic power systems

ABSTRACT The Maximum Power Point Tracking (MPPT) technique in the solar energy field optimises the performance of solar panels in different atmospheric conditions and variable loads. In this study, we present a new method that uses unsupervised learning (K-means clustering) to identify the atmospheric clusters of solar irradiance and cell temperature (G, T) and delimit homogeneous atmospheric zones or clusters to reduce the search space of the optimal parameters ( V mp , I mp , and P mp ). The data collected for one year is segregated into 12 clusters; in every cluster, 04 regions are defined based on every cluster’s centroid ( G c , T c ). A local search of the reference voltage/Duty cycle per cluster region is initiated for every sensed (G, T). Variable atmospheric conditions and resistive loads are tested. The results show that the efficiency of the DC/DC converter is 97.5% with a settling time (4.013 ms/5.577 ms) compared to the Perturb and Observe (P&O) the conventional tracking method and the Particle Swarm Optimisation (PSO), both applied locally inside a cluster and a deviation of 2% from the global maximum.

Exploring FSO link performance in varied atmospheric conditions to optimize 5G communication with a polarized quasi-diffuse transmitter

Abstract Free space optical (FSO) communication is an innovative technology that holds immense promise for numerous applications thanks to its high data transmission rates, rapid scalability, cost-effectiveness, superior security, and comprehensive capacity access techniques for Gbps data transmission. FSO communication relies on the Earth’s atmosphere as the medium, which introduces atmospheric disturbances that have driven the development of spatial diversity techniques aimed at enhancing the technology’s performance. Our proposed system employs a polarized quasi-diffused system with a fork as a spatial diversity scheme, which eliminates the need for multiple transmitters. We have developed a model that integrates both simulation designs to achieve optimal results. The proposed model has demonstrated excellent performance under various atmospheric turbulences, including rain, fog, and haze, exhibiting a very high maximum quality factor, improved received power, and better bit error rate (BER). Finally, we have obtained, analyzed, and extensively discussed the simulation results to provide a comprehensive understanding of the proposed model’s potential benefits. Our simulations show a maximum quality factor of 23 compared to existing models in much better, a 34 % increase in received power, and a 31 % decrease in BER compared to existing models. These results highlight the potential benefits of the proposed model for FSO communication systems.

Fingerprinting dissolved organic compounds: a potential tool for identifying the surface infiltration environments of meteoric groundwaters

Current methods for tracing decades-old groundwaters rely on isotope geochemistry to determine groundwater age and altitude at the point of infiltration. Temporal and spatial variability in atmospheric conditions, and water–rock interactions, can make the interpretation of isotopes uncertain. Here, we propose a new method of groundwater tracing based on the fingerprinting of natural dissolved organics. We present our initial findings from the Grimsel Test Site in Switzerland, located within a fractured granite. Using 2D gas chromatography, we derive detailed organic fingerprints from surface soils at several locations and show that different soils produce distinctly different dissolved organic signatures. We then compare the soils with groundwater and lake water using a non-targeted approach employing principal component analysis and hierarchical cluster analysis. Our analysis finds three statistically significant clusters. Most groundwaters are clustered with the lake-water samples but two are clustered with soil from the highest altitude surface sampling location. We hypothesize that for samples to form a significant cluster, they must have been derived from a common environment, with a unique combination of organic compounds. For groundwaters to cluster with soil samples or lake water, we theorize there must be a hydraulic connection between the type of infiltration environment and the groundwater sampling locations within each cluster. Our research demonstrates that organic molecules derived from the surface environment can be used to discriminate near-surface environment(s) through which meteoric groundwater has infiltrated. Organic fingerprinting could prove a powerful tool for improved understanding of groundwater flow systems, particularly when combined with other complementary techniques. Supplementary material : Compound alignment data set and supplementary tables are available at https://doi.org/10.6084/m9.figshare.c.7129987 Thematic collection: This article is part of the Sustainable geological disposal and containment of radioactive waste collection available at: https://www.lyellcollection.org/topic/collections/radioactive

Mitigating attenuation effects in free-space optics using WDM under variable atmospheric conditions

Abstract This research delves into the optimization of wavelength division multiplexing (WDM) within free space optical (FSO) communication systems, aiming to enhance the system’s resilience against atmospheric disruptions. FSO communication, known for its high bandwidth capabilities in unlicensed frequency bands, often encounters reliability issues due to weather-related signal degradation. By integrating a novel WDM-FSO approach, this study seeks to extend the operational range by using switching technique, thereby ensuring consistent and reliable communication. The introduction of a sophisticated switching and fork mechanism is pivotal in this context, facilitating dynamic signal routing and the strategic placement of optical amplifiers to counteract the impact of weather fluctuations and geographical challenges on signal integrity. This system, engineered to support a data transmission rate of 25 Gbps, caters to the needs of applications requiring high bandwidth. Through rigorous performance evaluation based on the Q-factor and BER under various atmospheric conditions, the effectiveness of the WDM-FSO system is demonstrated. The findings are detailed through graphs and tables, providing a comprehensive understanding of the system’s performance. This study makes a significant contribution to the field of wireless optical communication by presenting an optimized WDM-FSO system capable of overcoming weather-related obstacles especially at places where weather conditions are changed very frequently, marking a step forward in establishing more reliable FSO communication networks.

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

Wind Tunnel Characterization of a Graphene-Enhanced PEDOT:PSS Sensing Element for Aircraft Ice Detection Systems.

This study details the development and validation of a graphene-based ice detection system, designed to enhance flight safety by monitoring ice accumulation on aircraft surfaces. The system employs a semiconductive polymer (PEDOT:PSS) with graphene electrodes, interpreting resistance changes to detect water impact and ice formation in real time. The sensor's performance was rigorously tested in a wind tunnel under various temperature and airflow conditions, focusing on resistance signal dependency on air temperature and phase change. The results demonstrate the sensor's ability to distinguish water droplet impacts from ice formation, with a notable correlation between resistance signal amplitude and water droplet impacts leading to ice accretion. Further analysis shows a significant relationship between air temperature and the resistance signal amplitude, particularly at lower temperatures beneficial to ice formation. This underlines the sensor's precision in varied atmospheric conditions. The system's compact design and accurate detection highlight its potential for improving aircraft ice monitoring, offering a path toward a robust and reliable ice detection system.

A New Design and Embedded Implementation of a Low‐Cost Maximum Power Point Tracking Charge Controller for Stand‐Alone Photovoltaic Systems

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method.

New reference day atmosphere for analyses of quiet supersonic overflight

New reference day atmospheric conditions have been defined for future supersonic aircraft en route noise certification procedures. As field measurements are acquired, in a variety of atmospheric conditions, it is important to relate those measurements back to a common reference atmosphere to provide uniformity in method across applicants for noise certification. Because of the long propagation distances involved between the aircraft flight altitude and the ground, en route noise certification measurements can be substantially affected by the atmosphere. Properties, such as pressure, density, temperature, humidity, and winds, vary with altitude. Therefore, the definition of reference atmospheric profiles as a function of altitude are needed instead of the coarse layered atmosphere and homogeneous humidity used in subsonic aircraft landing and takeoff noise certification. Ground loudness levels from overflight of a quiet supersonic demonstrator concept, calculated with atmospheric data from sites across the world, were compared with the results from several candidate reference atmospheres to identify a reference that would minimize the required adjustment to certification test measurements. The existing ICAO 7488/3 profiles for temperature and pressure, combined with a modified ISO 5878 humidity profile, were found to offer the best results.

Techno-economic analysis on prospects and implementation of employing radiative cooling technology across broader atmospheric conditions

Achieving a transition to maximize the utilization of radiative cooling as a passive cooling strategy is challenging for several regions worldwide, mainly due to unawareness, lack of government support, and extreme seasonal climatic variations. Being characterized by a variety of atmospheric conditions from tropical coastline to arid regions along with humid highland areas to regions with continental atmospheric conditions, Pakistan faces significant seasonal and daily variations in atmospheric parameters, such as solar irradiance, temperature, precipitation patterns, humidity, etc., making it a steppe country with diverse atmospheric conditions and an energy-intensive economy dominated by fossil fuels. Therefore, following United Nation's Sustainable Development Goals, here we investigate the radiative cooling potential resource map across Pakistan, providing it as a sustainable alternative to lower the cooling demand by maximizing the emitted thermal radiation to the deep space and minimizing solar irradiance absorption by utilizing the materials' inherent properties of being highly reflective within solar spectrum range and extremely emissive within the atmospheric window. A comprehensive comparison between annual, seasonal, diurnal, and nocturnal radiative cooling capability is conducted and analyzed across all provinces of Pakistan based on the variation in regional climatic conditions. The yearly average radiative cooling capacity varies from 69.39 to 96.41 W/m2 due to swift variation from humid continental to arid climatic regions, with a 24.27 % discrepancy in daytime and nighttime radiative cooling potentials owing to the significant influence of solar irradiance. An office building model incorporating radiative cooling coating at the rooftop is further implemented to illustrate the real-case scenarios of using radiative cooling coatings as a cool roof. Compared to traditional roofs, applying radiative cooling coatings on the rooftop of a single-storey office building can reduce the annual cooling load by 76.01 to 112.77 kWh/(m2.year) for various cities under diverse climatic conditions. The annual electricity savings in several cities are estimated to be 30.40 to 45.11 kWh/(m2.year) based on the refrigerator COP of 2.5. The application of radiative cooling coatings on rooftops induces the incremental cost of 24 $/m2, with a simple payback period of 5.32 to 7.89 years in response to annual electricity savings across varying meteorological conditions. Besides, the maximum carbon emissions reduction is estimated to be up to 27.77 kg/(m2.year) based on a carbon emission factor of 0.606 kgCO2/kWh. In the end, valuable insights are discussed based on policy recommendations and limitations for developing strategies to adapt the radiative cooling technology on a broader scale based on innovative solutions, spreading public awareness, and offering financial incentives.

Cation Modified Highly Soluble Active Materials for Redox Flow Batteries

Renewables such as solar and wind energy are making increased penetration into modern electrical grids.1 However, the reliability of such grids is challenged by the intermittent nature of renewable sources and variable atmospheric conditions, posing a major roadblock in transition toward carbon-neutral energy sources. Grid level energy storage has long been seen as a solution to the intermittency problem.2 Among various storage technologies, redox flow batteries (RFBs) stand out, due to their ability to accommodate various needs of renewable-powered grids.3 RFBs based on non-aqueous chemistry (NRFBs) have a potential to greatly increase the energy density, however, a major setback in NRFB implementation arises from instability and low solubility of active materials.4,5 Herein, we present a strategy to design a flow battery utilizing anionic active materials which exhibit high solubility and high stability. To this end, we utilized a highly stable bio-inspired complex, vanadium-bis-hydroxyiminodiacetate (VBH) as a catholyte and anthraquinone-sulfonate (AQS) as an anolyte scaffold. Cycling performance of a flow battery based on VBH/AQS active materials was investigated in a full cell and a detailed chemical and electrochemical analysis of electrolytes before and after cycling was performed. Post-cycling analysis of electrolyte revealed that the active materials are highly stable even under deep charge-discharge cycling. However, lack of suitable membrane resulted in modest capacity fade. To overcome the crossover issue, a full cell comprising compositionally symmetric electrolytes, made by mixing anolyte and catholyte, was performed and various performance metrics of such cell were evaluated.

Speed sensorless Adaptive Power Control for photovoltaic-fed water pump using Extended Kalman–Bucy filter

In this study, a speed sensorless control method is proposed for a photovoltaic water-pump system operating under variable atmospheric conditions. The developed system not only continuously determines the maximum power point of the photovoltaic system but also generates a reference speed appropriate for each power value produced. The MATLAB/Simulink model of the driver system incorporates Adaptive Power Control, Direct Torque Control, a 3-phase induction motor, and the power plant. The determination of the maximum power point of the photovoltaic system and the input reference speed information for the Direct Torque Control under varying atmospheric conditions is accomplished through the application of the Adaptive Power Control algorithm. Notably, the control process operates as a closed-loop system without the requirement of a speed sensor. Instead, the Extended Kalman–Bucy estimator is employed for speed estimation. Empirical evaluation of the proposed method was conducted under constant temperature conditions and varying irradiance values. The results clearly illustrate the consistent operation of the motor, with a constant torque production aligned with each identified maximum power point corresponding to distinct irradiance levels. The control system demonstrates robust performance and operational efficiency across the entire range of operating points. The efficacy of the Kalman estimator in monitoring the reference speed, the utilization of a single solar radiation sensor, and the successful implementation of sensorless operation are significant advantages of the developed adaptive approach. The demonstrated performance of the proposed system showcases its potential for enhancing the efficient and reliable control of photovoltaic water-pump systems in real-world applications.

A novel global MPPT technique to enhance maximum power from PV systems under variable atmospheric conditions

A novel global MPPT technique to enhance maximum power from PV systems under variable atmospheric conditions

Dynamics of Separation of Unmanned Aerial Vehicles from the Magnetic Launcher Cart during Takeoff

Most aircraft launchers exhibit a rapid acceleration of the launching aircraft, often exceeding ten times the acceleration due to gravity. However, only magnetic launchers offer flexible control over the propulsion force of the launcher cart, enabling precise control over the aircraft’s acceleration and speed during its movement on the launcher. Consequently, extensive research is being conducted on magnetic launchers to ensure the repeatability of launch parameters, protect against aircraft overloads, and ensure operator safety. This article describes the process of modeling and analyzing the dynamical properties of a launch cart of an innovative prototype launcher, which employs a passive magnetic suspension with high-temperature superconductors, developed under the GABRIEL project. The developed mathematical model of the magnetic catapult cart was employed to conduct numerical studies of the longitudinal and lateral movement of the cart, as well as the configuration of the UAV–cart system during UAV takeoff under variable atmospheric conditions. An essential aspect of the research involved experimentally determining the magnetic levitation force generated by the superconductors as a function of the gap. The results obtained demonstrate that the analyzed catapult design enables safe UAV takeoff. External factors and potential vibrations resulting from uneven mass distribution in the UAV–cart system are effectively balanced by the magnetic forces arising from the Meissner effect and the flux pinning phenomenon. The primary advantage of the magnetic levitation catapult, in comparison to commercial catapults, lies in its ability to provide a reduced and consistent acceleration throughout the entire takeoff process.