Computer Simulation Research Articles

Numerical model of N-level cascade systems for atomic Radio Frequency sensing applications

AbstractA ready-to-use numerical model has been developed for the atomic ladder (cascade) systems which are widely exploited in Rydberg Radio Frequency (RF) sensors. The model has been explicitly designed for user convenience and to be extensible to arbitrary N-level non-thermal systems. The versatility and adaptability of the model is validated up to 4-level atomic systems by direct comparison with experimental results from the prior art. The numerical model provides a good approximation to the experimental results and provides experimentalists with a convenient ready-to-use model to optimise the operation of an N-level Rydberg RF sensor. Current sensors exploit the 4-level atomic systems based on alkali metal atoms which require visible frequency lasers and these can be expensive and also suffer from high attenuation within optical fiber. The ability to quickly and simply explore more complex N-level systems offers the potential to use cheaper and lower-loss near-infrared lasers.

Habitat Assessment of Bocachico (Prochilodus magdalenae) in Ciénaga de Betancí, Colombia, Using a Habitat Suitability Index Model

This study evaluates the habitat of the Bocachico fish (Prochilodus magdalenae) in the Ciénaga de Betancí, Colombia, using a habitat suitability index (HSI) model. Wetlands like the Ciénaga de Betancí are under significant pressure from anthropogenic activities, affecting biodiversity and ecosystem health. The Bocachico, a species of immense cultural and economic importance, faces habitat degradation and fragmentation. Using hydrodynamic and water quality data, a numerical model (EFDC+ Explorer 11.5), and field data collected from multiple sampling campaigns, we assessed habitat suitability based on five key parameters: water temperature, dissolved oxygen, ammonia nitrogen, velocity, and depth. The model results indicated that environmental conditions in the wetland remained relatively stable during the dry season, with an average HSI score of 0.67, where 9% of the wetland area displayed acceptable conditions, and the remaining 91% displayed medium conditions. The wet season, on the other hand, had an average HSI score of 0.64, with 7.2% of the area in the acceptable suitability range, and the remaining 92.8% in the medium category. Variations in HSI were primarily driven by ammonia nitrogen levels, water velocity, and depth. Despite limited fluctuations in the HSI, areas of low suitability were identified, particularly in regions impacted by human activities. These findings have practical implications for conservation strategies, providing valuable insights for the sustainable management and conservation of the Ciénaga de Betancí, informing strategies for improving habitat conditions for the Bocachico, and supporting wetland restoration efforts.

Level-Set Method Based Predictive Modeling to Track the Evolution of Surface During Pulsed Laser Surface Melting

Abstract The objective of this study is to investigate the evolution of surface geometry during pulsed laser surface melting (pLSM) via level-set method-based interface tracking numerical framework. Existing models to track surface geometry are inaccurate and computationally expensive. Therefore, they have limited use in gaining understanding of the surface evolution during pLSM. A numerical model, integrating the level-set approach, fluid flow, and heat transfer dynamics, is detailed in this paper. The multi-phase numerical model achieves accurate tracking of interface for a single pulse by implementing the volumetric laser heat source on the moving interface by modifying Beer–Lambert's law. The accuracy of the single pulse model is confirmed by comparing its peak-to-valley height (PVH) to the experimental data. The deviation in PVH is limited to about 15%, with a maximum root mean square error of ∼0.24 µm, highlighting the model's reliability. Additionally, the evolved surface of a single pulse from the model is replicated over an area with dedicated overlaps to generate the predicted textured surface with reasonable accuracy. Some inaccuracies in the predicted surface roughness values were observed because the textures were generated based on a single pulse geometry computed on an initially flat surface. Nonetheless, the results highlight a significant development in numerical frameworks for pLSM and can be used as a tool to gain deeper insights into the process and for process optimization.

Quantifying vertical borehole heat exchanger sustainability from a geothermal resource perspective in the UK

Low-temperature geothermal energy is viewed as a renewable resource that could contribute to the decarbonization of residential heating in the UK. Here, we constrain the geothermal potential of the shallow subsurface to provide the heating requirements of a typical single-family house via a vertical borehole heat exchanger (BHE), to provide a preliminary framework for the heat resource licensing of such systems. Through analytical and numerical models, we calculate the heat balance for a typical house with a garden located in a Carboniferous sedimentary basin, which represents a large proportion of the UK land mass and the regions where energy poverty is more common. Neglecting the heat recharge via groundwater flow, our results indicate that geothermal, solar heat flux and radiogenic heat production can only replenish up to 5% of the heat extracted annually by a 100 m-long BHE in heat extraction-only mode. The lack of natural recharge causes the continuous expansion of the thermal footprint at an average rate of 18 m 2 a −1 over 30 years, considering a ground thermal conductivity of 2.2 W m −1 °C −1 , with the area cooled by 1°C extending beyond the current minimum BHE spacing guidelines of 6 m. In Scotland, reinjecting c . 35% of the yearly heat requirements, which could be accomplished through borehole thermal energy storage, is shown to safeguard against resource over-exploitation and interference risks between neighbouring users in the majority of the high-demand areas. In view of a large deployment of BHEs, it is crucial to incorporate such reinjection strategies into a regulatory framework to ensure a resilient and sustainable utilization of shallow geothermal resources.

Strong error bounds for Trotter and strang-splittings and their implications for quantum chemistry

Efficient error estimates for the Trotter product formula are central in quantum computing, mathematical physics, and numerical simulations. However, the Trotter error's dependency on the input state and its application to unbounded operators remains unclear. Here, we present a general theory for error estimation, including higher-order product formulas, with explicit input state dependency. Our approach overcomes two limitations of the existing operator-norm estimates in the literature. First, previous bounds are too pessimistic as they quantify the worst-case scenario. Second, previous bounds become trivial for unbounded operators and cannot be applied to a wide class of Trotter scenarios, including atomic and molecular Hamiltonians. Our method enables analytical treatment of Trotter errors in chemistry simulations, illustrated through a case study on the hydrogen atom. Our findings reveal the following: (i) for states with fat-tailed energy distribution, such as low-angular-momentum states of the hydrogen atom, the Trotter error scales worse than expected (sublinearly) in the number of Trotter steps; (ii) certain states do not admit an advantage in the scaling from higher-order Trotterization and, thus, the higher-order Trotter hierarchy breaks down for these states, including the hydrogen atom's ground state; (iii) the scaling of higher-order Trotter bounds might depend on the order of the Hamiltonians in the Trotter product for states with fat-tailed energy distribution. Physically, the enlarged Trotter error is caused by the atom's ionization due to the Trotter dynamics. Mathematically, we find that certain domain conditions are not satisfied by some states so higher moments of the potential and kinetic energies diverge. Our analytical error analysis agrees with numerical simulations, indicating that we can estimate the state-dependent Trotter error scaling genuinely. Published by the American Physical Society 2024

A Study on the Attenuation Patterns of Underground Blasting Vibration and Their Impact on Nearby Tunnels

The natural caving method, as a new technique in underground mining, has been promoted and applied in several countries worldwide. The destruction of the bottom rock mass structure directly impacts the structural stability of underground engineering, resulting in damage and collapse of underground tunnels. Therefore, based on the principles of explosion theory and field monitoring data, a scaled three-dimensional numerical simulation model of underground blasting was constructed using LS-DYNA19.0 software to investigate the attenuation patterns of underground blasting vibrations and their impact on nearby tunnels. The results show that the relative error range between the simulated blasting vibration velocities based on the FEM-SPH (Finite Element Method–Smoothed Particle Hydrodynamics) algorithm and the measured values is between 7.75% and 9.85%, validating the feasibility of this method. Significant fluctuations in blasting vibration velocities occur when the blast center increases to within a range of 10–20 m. As the blast center distance exceeds 25 m, the vibration velocities are increasingly influenced by the surrounding stress. Additionally, greater stress results in higher blasting vibration velocities and stress wave intensities. Fitting the blasting vibration velocities of various measurement points using the Sadovsky formula yields fitting correlation coefficients ranging between 0.92 and 0.97, enabling the prediction of on-site blasting vibration velocities based on research findings. Changes in propagation paths lead to localized fluctuations in the numerical values of stress waves. These research findings are crucial for a deeper understanding of underground blasting vibration patterns and for enhancing blasting safety.

Convergent neural signatures of speech prediction error are a biological marker for spoken word recognition

AbstractWe use MEG and fMRI to determine how predictions are combined with speech input in superior temporal cortex. We compare neural responses to words in which first syllables strongly or weakly predict second syllables (e.g., “bingo”, “snigger” versus “tango”, “meagre”). We further compare neural responses to the same second syllables when predictions mismatch with input during pseudoword perception (e.g., “snigo” and “meago”). Neural representations of second syllables are suppressed by strong predictions when predictions match sensory input but show the opposite effect when predictions mismatch. Computational simulations show that this interaction is consistent with prediction error but not alternative (sharpened signal) computations. Neural signatures of prediction error are observed 200 ms after second syllable onset and in early auditory regions (bilateral Heschl’s gyrus and STG). These findings demonstrate prediction error computations during the identification of familiar spoken words and perception of unfamiliar pseudowords.

Permeability Enhancement Induced by Fracture Shear Dilation During Close-Range Coal Seam Mining

The advance of the working face in coal seams alters the local stress field and may give rise to fractures in the vicinity of the excavation. In this study, a constitutive model in which damage is defined as a function of volumetric strain was established and utilized in a numerical model to prognosticate the fracture development around the excavation. The predicted fractures that emerged in the overlying rock mass were found to exhibit hybrid characteristics. A permeability model was also constructed, taking into account both tension- and shear-induced fracture development. The permeability increase of the upper adjacent coal seam is most notable within 40 m from the goaf boundary. As the working face progresses, the permeability of the upper adjacent coal seam is further enhanced while that of the lower adjacent coal seam remains unaffected. The permeability at the goaf boundary is high and reaches its maximum at the rear of the working face, indicating that for the permeability change, the effect of shear-induced dilation plays a more crucial role than that of pressure-dependent compaction. This study can be used to guide the design of coal seam methane drainage for the mining of closely spaced coal seams.

Modeling and Parameter Selection of the Corn Straw–Soil Composite Model Based on the DEM

During corn harvesting operations, machine–straw–soil contact often occurs, but there is a lack of research related to the role of straw–soil contact. Therefore, in this study, a composite contact model of corn straw‒soil particles was established based on the discrete element method (DEM). First, the discrete element Hertz‒Mindlin method with bonding particle contact was used to establish a numerical model of the double-bonded bimodal distribution of corn straw, and bonding particle models of the outer skin‒outer skin, inner pulp‒inner pulp, and outer skin‒inner pulp were developed. The nonhomogeneous and deformable material properties were accurately expressed. The straw compression test combined with simulation calibration was used to determine some of the bonding contact parameters by means of the PB (Plackett–Burman) test, the steepest ascent test, and the BB (Box–Behnken) test. Additionally, Additionally, the Hertz-Mindlin with JKR (Johnson-Kendall-Roberts) + bonding key model was used to establish the numerical model of the soil particles, which was used to describe the irregularity and adhesion properties of the soil particles. The geometric model of the soil particles was established using the multisphere filling method. Finally, a composite contact model of corn straw‒soil particles was established, the contact parameters between straw and soil were calibrated via collision tests, inclined tests and inclined rolling tests, and the established composite contact model was further verified through direct shear tests between straw and soil. A theoretical foundation for the optimal design of equipment linked to maize harvesting is provided by this work.

Abstract C008: New insights from a novel mouse model of synovial sarcoma

Abstract Mouse models are the basis of preclinical and translational research in solid tumors. Multiple methods exist to induce tumor formation in mice, including genetically engineered mouse models, chemotoxic agents, injection of tumor cells, and xenograft approaches. Synovial sarcoma, a rare highly malignant soft-tissue tumor, poses a serious threat to patients due to its aggressive nature and associated high rates of mortality and morbidity. This disease's underlying cause and development remain poorly comprehended, and existing treatment approaches have yielded unsatisfactory outcomes. In the last two decades, numerous effective animal models of soft tissue sarcoma (STS) have been created, primarily utilizing genetically engineered mice and zebrafish, however long latency of tumor development for several months in these models creates a barrier for practical in-vivo experiments for drug testing or manipulating the cellular composition of the tumor microenvironment. In studying cancer immunotherapy, it is important to consider aspects of antitumor immune responses and to produce a model that mimics the complexity of the immune system We have generated a new mouse model of synovial sarcoma of exceptionally fast tumor development. Intramuscular delivery of Cre recombinase protein in mice which drives conditional knockdown in PTEN, stabilization of β-catenin, and formation of fusion oncogenes of SS18-SSX2 was sufficient to initiate high-grade synovial sarcomas with myofibroblastic differentiation in 3 weeks. As immunotherapy is increasingly applied to sarcomas, our mouse model will serve as a great tool for preclinical data. Flow cytometry-based analysis of peripheral blood shows significant changes in immune cell populations as early as five days after Tat-Cre injection and continues throughout the development of synovial sarcoma. These results suggest a significant systemic phenotypic alteration of circulating immune cells, which may impact other major organs in this genetic mouse model of synovial sarcoma. Single- cell RNA sequencing of the CD45+ leukocytes in the tumor microenvironment reveals subsets that may promote tumor progression. Our work promises to significantly enhance our understanding of the pathogenesis of this disease and establish a reliable preclinical platform for devising and assessing therapeutic interventions. Citation Format: Hesham Mohei, Donn Van Deren, Irene S. Molina, Malay Haldar. New insights from a novel mouse model of synovial sarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C008.

Reliability and durability assessment of bridge stay cables

AbstractAn algorithm for the reliability and durability assessment of stay cables in bridges is presented in this study enabling their probability of failure and a safe working period to be determined under various loading scenarios. The algorithm was originally developed based on data collected from an extensive structural monitoring campaign of the biggest single-pylon concrete cable-stayed bridge in Poland and used to assess the durability of its suspension system. It was then modified to be suitable for the evaluation of stay-cables subjected to wind excitation and structural reliability of the suspension system in a real steel bridge where permanent plastic deformations occurred in the anchor zones of the stay cables. The algorithm takes into account analytical models describing the stay cables and their numerical finite element models (FEM). As such, it is a universal tool having a wide range of applications, also beyond stay cables often encountered in medium- and long-span bridges forming a critical part of the civil engineering infrastructure.

Data‐Driven Tools to Evaluate Support Pressure, Radial Displacements, and Face Extrusion for Tunnels Excavated in Elastoplastic Grounds

ABSTRACTTwo‐dimensional analysis of tunnel design based on the convergence–confinement method, although commonly used in tunnel design, may not always be applied. For example, in squeezing grounds, if the support is installed very close to the tunnel face, three‐dimensional numerical modeling is required but is computationally expensive. Therefore, it is usually performed before or after tunnel excavation. A machine learning approach is presented here as an alternative to costly computations. Two surrogate models are developed based on synthetic data. The first model aims to assess the support pressure and the radial displacement at equilibrium in the lining and the radial displacement occurring close to the face at the installation distance of the support. The second model is intended to compute the extrusion of the core considering an unlined gallery. It is assumed a circular tunnel excavated in a Mohr–Coulomb elastoplastic perfectly plastic ground under an initial isotropic stress state. In particular, the bagging method is applied to neural networks to enhance the generalization capability of the models. A good performance is obtained using relatively scarce datasets. The modeling of the surrogate models is explained from the creation of the synthetic datasets to the evaluation of their performance. Their limitations are discussed. In practice, these two machine learning tools should be helpful in the field during the excavation phase.

A numerical study of deep excavations adjacent to existing tunnels: Integrating CPTU and SDMT to calibrate soil constitutive model

Accurate determination of parameters for an advanced soil constitutive model highly relies on laboratory testing, even though it is notoriously difficult to obtain undisturbed samples for soft soils. This study explores the potential use of in-situ tests, such as piezocone penetration tests (CPTU) and seismic dilatometer tests (SDMT), to estimate the constitutive model parameters. Based on a case history of a deep excavation adjacent to existing tunnels in silt/sand-dominated sediments, a calibration approach of a set of the HSSmall (Hardening Soil Model with Small Strain Stiffness) model parameters is presented, and the derived parameters are used to numerically compute the interactive responses of tunnels and deep excavations. Several comparisons against field monitoring data indicate that the numerical model with the CPTU/SDMT-interpreted HSSmall model parameters adequately reproduces observed deformation responses of deep excavations adjacent to tunnels. However, the use of laboratory tests with disturbed samples to estimate the stiffness parameters of the HSSmall model may lead to an overconservative solution. This finding supports the use of CPTU/SDMT to provide representative parameters for a range of soil layers, leading to the conclusion that tunnel linings may be beneficial to mitigate ground movements and wall deflections due to a barrier effect.

Large deformation simulation of uprooting of trees with complex root system architectures using material point method with embedded truss elements

Abstract Background and aims Urban trees in coastal cities like Hong Kong may suffer from an uprooting failure when subjected to extreme winds. A proper numerical model for tree uprooting simulation can help to select tree species or soil types that better resist uprooting failure. However, modeling tree uprooting is challenging as it is a cross-disciplinary problem involving complex root system architectures (RSAs) and large deformation of both roots and soils. This study aims to develop a hybrid numerical model that combines truss elements and material point method (MPM) to simulate the entire large-deformation uprooting process of trees with complex RSAs. Methods The tree uprooting model is developed by coupling truss elements in finite element method (FEM) with MPM. Laboratory pull-out tests using artificial roots and real root cuttings are adopted to validate the developed model. A comparative study is performed to investigate the difference between using complex and simplified RSAs in tree uprooting simulations. Results The developed model provides consistent predictions of peak load, critical displacement and failure mode when compared with results from laboratory tests. Moreover, the comparative study shows that the uprooting resistance obtained with a complex RSA is higher than that with a simplified RSA. The difference varies with the soil and root mechanical properties. Conclusion The developed hybrid model offers a novel way for simulating an entire tree uprooting process involving large deformations and complex RSAs. The study shows that using a simplified RSA to approximate the complex RSA might result in misleading failure modes.

Emergent biaxiality in chiral hybrid liquid crystals.

Biaxial nematic liquid crystals are fascinating systems sometimes referred to as the Higgs boson of soft matter because of experimental observation challenges. Here we describe unexpected states of matter that feature biaxial orientational order of colloidal supercritical fluids and gases formed by sparse rodlike particles. Colloidal rods with perpendicular surface boundary conditions exhibit a strong biaxial symmetry breaking when doped into conventional chiral nematic fluids. Minimization of free energy prompts these particles to orient perpendicular to the local molecular director and the helical axis, thereby imparting biaxiality on the hybrid molecular-colloidal system. The ensuing phase diagram features colloidal gas and liquid and supercritical colloidal fluid states with long-range biaxial orientational symmetry, as supported by analytical and numerical modeling at all hierarchical levels of ordering. Unlike for nonchiral hybrid systems, dispersions in chiral nematic hosts display biaxial orientational order at vanishing colloid volume fractions, promising both technological and fundamental research utility.

An Analysis and Comparison of the Hydrodynamic Behavior of Ships Using Mesh-Based and Meshless Computational Fluid Dynamics Simulations

This paper presents a comparison of two turbulence models implemented in two different frameworks (Eulerian and Lagrangian) in order to simulate the motion in calm water of a displacement hull. The hydrodynamic resistance is calculated using two open-source Computational Fluid Dynamics (CFD) software packages: OpenFOAM and DualSPHysics. These two packages are employed with two different numerical treatments to introduce turbulence closure effects. The methodology includes rigorous validation using a Wigley hull with experimental data taken from the literature. Then, the validated frameworks are applied to model a ship hull with a 30 m length overall (LOA), and their results discussed, outlining the advantages and disadvantages of the two turbulence treatments. In conclusion, the resistance calculated with OpenFOAM offers the best compactness of results and a shorter simulation time, whereas DualSPHysics can better capture the free-surface deformations, preserving similar accuracy.

Modeling and Monitoring of the Tool Temperature During Continuous and Interrupted Turning with Cutting Fluid

In metal cutting, a large amount of mechanical energy converts into heat, leading to a rapid temperature rise. Excessive heat accelerates tool wear, shortens tool life, and hinders chip breakage. Most existing thermal studies have focused on dry machining, with limited research on the effects of cutting fluids. This study addresses that gap by investigating the thermal behavior of cutting tools during continuous and interrupted turning with cutting fluid. Tool temperatures were first measured experimentally by embedding a thermocouple in a defined position within the tool. These experimental results were then combined with simulations to evaluate temperature changes, heat partition, and cooling efficiency under various cutting conditions. This work presents novel analytical and numerical models. Both models accurately predicted the temperature distribution, with the analytical model offering a computationally more efficient solution for industrial use. Experimental results showed that tool temperature increased with cutting speed, feed, and cutting depth, but the heat partition into the tool decreased. In continuous cutting, cooling efficiency was mainly influenced by feed rate and cutting depth, while cutting speed had minimal impact. Interrupted cutting improved cooling efficiency, as the absence of chips and workpieces during non-cutting phases allowed the cutting fluid to flow over the tool surface at higher speeds. The convective cooling coefficient was determined through inverse calibration. A comparative analysis of the analytical and numerical simulations revealed that the analytical model can underestimate the temperature distribution for complex tool structures, particularly non-orthogonal hexahedral geometries. However, the relative error remained consistent across different cutting conditions, with less error observed in interrupted cutting compared to continuous cutting. These findings highlight the potential of analytical models for optimizing thermal management in metal turning processes.

Influence of material orientation, loading angle, and single-shot repetition of laser shock peening on surface roughness properties

Titanium alloy Ti6Al4V is extensively utilized in biomedical applications due to its excellent biocompatibility, corrosion resistance, and mechanical properties. The design of dental implant surface textures has changed throughout time to address issues with oral rehabilitation in both healthy and damaged bones. The longevity of an implant is significantly impacted by surface roughness. This study examines the use of laser shock peening (LSP) as a surface modification technique to improve the mechanical properties of implants. A numerical model is developed using the commercial finite elements software in ABAQUS/Explicit for simulating dynamic conditions. The aim of the study is to develop surface roughness parameters using computational methods such as studies have not yet been contemplated. The single shot angle, shot repeat, time, material orientation, and laser power are applied for the first time simultaneously to evaluate the impact of material orientation and loading angles on surface roughness parameters. The study showed that the developed computational model’s compressive residual stress was −578.45 MPa, while the experimental samples were −592.18 MPa. Consequently, the difference between the computational and experimental results was 2.32%. Without regard to material orientation or angle, the compressive residual stress of the samples under examination was found to be −578.450 MPa after three repetitions and to decreased to −1.620 MPa after four. These results demonstrate that by varying the material orientation and loading angle, the Ra value may be increased four times.

Simulated inter-filament fusion in embedded 3D printing

In embedded 3D printing (EMB3D), a nozzle extrudes continuous filaments inside of a viscoelastic support bath. Compared to other extrusion processes, EMB3D enables softer structures and print paths that conform better to the shape of the part, allowing for complex structures such as tissues and organs. However, strategies for high-quality dimensional accuracy and mechanical properties remain undocumented in EMB3D. This work uses computational fluid dynamics simulations in OpenFOAM to probe the underlying physics behind two processes: deformation of the printed part due to nearby nozzle motion and fusion between neighboring filaments during printing. Through simulations, we disentangle yielding from viscous dissipation, and we isolate interfacial tension effects from rheology effects, which are difficult to separate in experiments. Critically, these simulations find that disturbance and fusion are controlled by the flow of support fluid around the nozzle. To avoid part deformation, the nozzle must remain far from existing parts during non-printing moves, moreso when traveling next to the part than above the part and especially when the interfacial tension between the ink and support is non-zero. Additionally, because support can become trapped between filaments at zero interfacial tension, the spacing between filaments must be tight enough to produce over-printing, or printing too much material for the designed space. In non-Newtonian fluids, spacings for vertical walls must be even tighter than spacings for horizontal planes. At these spacings, printing a new filament sometimes creates and sometimes mitigates shape defects in the old filament. While non-zero ink-support interfacial tensions produce better inter-filament fusion than zero interfacial tension, interfacial tension also produces shape defects. Slicing algorithms that consider these unique EMB3D defects are needed to improve mechanical properties and dimensional accuracy of bioprinted constructs.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.