Dynamic Problem Research Articles

Eliminating Charge Transfer at Cathode-Electrolyte Interface for Ultrafast Kinetics in Na-Ion Batteries.

Sodium-ion batteries suffer from kinetic problems stemming from sluggish ion transport across the electrode-electrolyte interface, causing rapid energy decay during fast-charging or low-temperature operation. One exciting prospect to enhance kinetics is constructing neuron-like electrodes that emulate fast signal transmission in a nervous system. It has been considered that these bioinspired designs enhance electron/ion transport of the electrodes through carbon networks. However, whether they can avoid sluggish charge transfer at the electrode-electrolyte interface remains unknown. By connecting the openings of carbon nanotubes with the surface of carbon-coated Na3V2O2(PO4)2F cathode nanoparticles, here we use carbon nanotubes to trap Na+ ions released from the nanoparticles during charge. Therefore, Na+ movement is confined only inside the neuron-like cathode, eliminating ion transport between the electrolyte and cathode, which has been scarcely achieved in conventional batteries. As a result, a 14-fold reduction in interfacial charge transfer resistance is achieved when compared to unmodified cathodes, leading to superior fast-charging performance and excellent cyclability up to 200C, and surprisingly, reversible operation at low temperatures down to -60 °C without electrolyte modification, surpassing other Na3V2O2(PO4)2F-based batteries reported to date. As battery operation has relied on charge transfer at the electrode-electrolyte interface for over 200 years, our approach departs from this traditional ion transport paradigm, paving the way for building better batteries that work under harsh conditions.

Quantum Nuclear Dynamics on a Distributed Set of Ion-Trap Quantum Computing Systems.

Quantum nuclear dynamics with wavepacket time evolution is classically intractable and viewed as a promising avenue for quantum information processing. Here, we use IonQ, Inc.'s 11-qubit trapped-ion quantum computer, Harmony, to study the quantum wavepacket dynamics of a shared-proton within a short-strong hydrogen-bonded system. We also provide the first application of distributed quantum computing for chemical dynamics problems, where a distributed set of quantum processes is constructed using a tensor network formalism. For a range of initial states, we experimentally drive the ion-trap system to emulate the quantum nuclear wavepacket as it evolves along the potential surface generated from the electronic structure. Following the experimental creation of the nuclear wavepacket, we extract measurement observables such as its time-dependent spatial projection and its characteristic vibrational frequencies to good agreement with classical results. Vibrational eigenenergies obtained from quantum computation are in agreement with those obtained from classical simulations to within a fraction of a kilocalorie per mole, thus suggesting chemical accuracy. Our approach opens a new paradigm for studying the quantum chemical dynamics and vibrational spectra of molecules and also provides the first demonstration of parallel quantum computation on a distributed set of ion-trap quantum computers.

Multi-body mesh deformation using a multi-level localized dual-restricted radial basis function interpolation

Multi-body dynamic problems with moving boundaries are prevalent in the computational fluid dynamics. In these problems, the original mesh is unsuitable for subsequent solution steps and is needed to be deformed. The radial basis function (RBF) interpolation is a common algorithm for the mesh deformation task. However, the multi-body mesh may contain numerous individuals and volume nodes, which will harm the computational efficiency. In order to optimize this process, we propose a new method for the mesh deformation of the multi-body configuration. In this new approach, each individual is deformed separately. Thus the global problem is decomposed into a series of local mesh deformation problems. As the computational cost to construct the interpolation system in RBF algorithm is proportional to the cube of the number of support points on the surface, converting it into multiple local mesh deformation problems can effectively reduce the CPU cost. To treat each local problem, we employ a dual-restricted RBF interpolation technique which could avoid the influence of moving individual on the other individuals. This new localized approach effectively improves the computational efficiency to construct the interpolation system but sometimes will increase the CPU cost of the mesh updating procedure. To avoid this drawback, the existed multi-level restricted RBF strategy is coupled with the new localized method to further reduce the CPU cost to update the mesh. The combination of the two techniques could enhance their advantages and avoid their drawbacks. Some numerical examples have demonstrated the abilities of the new algorithm. For instance, in the case of the three-dimensional birds flock, the CPU time to construct the interpolation system and deform the volume mesh was respectively reduced by three and two orders of magnitude compared to the global single-level method.

Coupled Lattice‐Expanded Ni and MoO2 Array for Efficient Alkaline Hydrogen Evolution

AbstractTransition metal oxides have attracted much attention due to their good electrical conductivity, high chemical stability, and easy electronic structure modulation. However, it is still a great challenge to solve the problems of sluggish reaction kinetics and high overpotential in the alkaline hydrogen evolution reaction (HER) due to their poor intrinsic activity. Herein, a lattice‐expanded Ni‐decorated MoO2 array (Ni‐MoO2‐700) catalyst is prepared for the alkaline HER. Unlike most of the reported literature, the decoration of metal heteroatoms may lead to lattice expansion of the carrier catalyst. In contrast, lattice expansion of Ni is also achieved by adjusting the annealing temperature in this work. The Ni‐induced lattice‐expanding effect can not only modulate the electronic structure of the catalyst but also accelerate electron transfer during the reaction, thus increasing its intrinsic activity for the HER. As a result, Ni‐MoO2‐700 exhibits significantly enhanced catalytic activity with an overpotential of 74.9 mV at 10 mA cm−2 in 1.0 m KOH, which is far superior to that of most of the reported advanced Ni‐ and Mo‐based materials. This work provides deep intrinsic insight and a great opportunity to design efficient transition metal oxide catalysts for the alkaline HER.

Comparative Analysis of NURBS and Finite Element Method in Computational Fluid Dynamics Applications: Case Study on NACA 2412 Airfoil Aerodynamics

In this research, an attempt was made to employ the Non-Uniform Rational B-Splines (NURBS) method for a challenging computational fluid dynamics (CFD) problem of aerodynamics around NACA 2412 airfoils. The comparison was carried out thoroughly by using the same boundary conditions and geometry, comparing NURBS to standard FEM implementations. Our study was interested in demonstrating the foreseeable functionalities of NURBS for solving complex CFD problems and conducting a comparative effectiveness performance evaluation between them with traditional FEM methodologies.

Forced vibrations of the shaft line based on the model of a two-stage rod with an elastic connection of sections

The paper considers a method for solving the problem of dynamics of forced vibrations of a wall line based on a model of an equivalent elastic two-stage rod with a pliable connection of sections. The model is represented by a system of partial differential equations. The solution of the equations is obtained by the Fourier method for eigenfunctions orthogonal with weight. It is established that the presence of an elastic element at the junction of the sections does not affect the orthogonality of the eigenfunctions. The influence of the elastic coupling compliance coefficient on the natural frequency spectrum of the shaft line is estimated. The obtained forms of natural vibrations of an equivalent elastic two-stage rod are consistent in appearance with the corresponding forms known in the literature for a discrete model of a similar torsional circuit. A partial solution of the equations is found for the case of the action of the harmonic load on the engine and the averaged torque on the propeller. As an example, the dynamics of forced vibrations of the shaft line of a barge of the Sosnovka type is studied. The results of calculating the natural frequencies of the considered model are compared with the classical discrete mass method. The analysis of the obtained plots of the twisting angles and torques showed that the angles are satisfactorily approximated by the first proper oscillation shape, and for moments, high shapes must be taken into account. The comparison of the simulation results with the data of the torsiogram obtained under production testing conditions indicates the need for further development of the model. Further improvement of the torsional circuit, as well as a more detailed study of the nature of the operating loads, are considered as promising areas of research.

Dynamic path finding for multi-load agent pickup and delivery problem

Recently, the Multi-Agent Pickup and Delivery (MAPD) problem has attracted widespread attention from both academia and industry. In the MAPD problem, each task has its pickup and delivery locations, and the agent needs to pick this task up from the pickup location and deliver it to its delivery location. Therefore, existing works consider the MAPD problem as the core problem in industrial scenarios, e.g., logistics warehouse. Note that the agents considered in the MAPD problem are single-load agents that complete tasks one by one. However, many commercial companies have deployed agents with multi-load instead of single-load agents to improve efficiency and reduce costs. The agents with multi-load can complete multiple tasks at once, so existing solutions cannot work well with the MAPD problem for multi-agents. To solve this issue, we investigate a novel problem in this paper, namely the Multi-Load Agent Pickup and Delivery (MLAPD) problem, where the agents with multi-load not only need to complete assigned real-time tasks but also need to avoid conflicts with each other and the goal is to minimize the total cost in the warehouse. To address this novel problem, we develop a task assignment to complete the assignments between multi-load agents and online tasks in real-time and a dynamic path finding problem that enables multi-load agents to move along conflict-free paths. Finally, extensive experiments in two different warehouses examine the effectiveness of our solutions.

Variational Analysis of a Dynamic Contact Problem with Wear and Damage Involving Viscoelastic Materials with Long-Term Memory

We investigate a mathematical problem involving dynamic interaction between a viscoelastic body with long-term memory loss and an obstruction. The contact is frictional and bilateral, with a moving rigid base, resulting in wear of the contacting surface. The problem is expressed as a coupled system, with a hyperbolic quasi-variational inequality for displacement and a parabolic variational inequality for damage. We define a variational formulation for the model and demonstrate the existence of a single weak solution to the problem. The material behaviour is explained using a viscoelastic constitutive law that includes long-term memory and damage. Elastic deformations induce material deterioration, which is depicted by a parabolic inclusion. The proof is based on classical existence.

A Dynamic Principal-Agent Problem with One-Sided Commitment

In this paper, we consider a principal-agent problem where the agent is allowed to quit by incurring a cost. When the current agent quits the job, the principal will hire a new one, possibly with a different type. We characterize the principal’s dynamic value function, which could be discontinuous at the boundary, as the (unique) minimal solution of an infinite dimensional system of Hamilton-Jacobi-Bellman equations, parameterized by the agent’s type. This dynamic problem is time consistent in a certain sense. Some interesting findings are worth mentioning. First, self-enforcing contracts are typically suboptimal. The principal would rather let the agent quit and hire a new one. Next, the standard contract for a committed agent may also be suboptimal because of the presence of different types of agents in our model. The principal may prefer no commitment from the agent; then, the principal can hire a cheaper one from the market at a later time by designing the contract to induce the current agent to quit. Moreover, because of the cost incurred to the agent, the principal will see only finitely many quittings. Funding: J. Zhang is supported in part by the NSF [Grants DMS-1908665 and DMS-2205972]. Z. Zhu is supported by The Hong Kong University of Science and Technology (Guangzhou) Start-Up Fund [Grant G0101000240] and the Guangzhou-HKUST(GZ) Joint Funding Program [Grant 2024A03J0630].

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Spatially mixed implicit–explicit schemes in hydro-mechanically coupled soil dynamics

Mixed implicit–explicit (IMEX) schemes combine elements of both implicit and explicit time integration methods. This allows to efficiently handle boundary value problems with severe differences in stiffness, such as often encountered in soil dynamics. A spatially mixed IMEX scheme, in which different parts of the computational domain may be treated implicitly or explicitly depending on their behaviour, is discussed in this work. Domain decomposition by means of a mortar contact discretisation is used for this purpose and multiple systems of equations are solved. The merits of the scheme in solving problems of soil dynamics are demonstrated, for which the governing equations are extended to hydro-mechanical coupling.

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and requires a great deal of time. In these cases, researchers use approximate solutions offered by metaheuristics. This work involved the design of a new metaheuristic called Open Competency Optimization (OCO), which was inspired by human behavior during the learning process and based on the competency approach. The aim is the construction of solutions that represent learners’ ideas in the context of an open problem. The candidate solutions in OCO evolve over three steps. Concerning the first step, each learner builds a path of learning (finding the solution to the problem) through self-learning, which depends on their abilities. In the second step, each learner responds positively to the best ideas in their group (the construction of each group is based on the competency of the learners or the neighbor principle). In the last step, the learners interact with the best one in the group and with the leader. For the sake of proving the relevance of the proposed algorithm, OCO was tested in dynamic vehicle-routing problems along with the Generalized Dynamic Benchmark Generator (GDBG).

Hierarchical Equations of Motion for Quantum Chemical Dynamics: Recent Methodology Developments and Applications.

ConspectusQuantum effects are critical to understanding many chemical dynamical processes in condensed phases, where interactions between molecules and their environment are usually strong and non-Markovian. In this Account, we review recent progress from our group in development and application of the hierarchical equations of motion (HEOM) method, highlighting its ability to address some challenging problems in quantum chemical dynamics.In the HEOM method, the bath degrees of freedom are represented using effective modes from exponential decomposition of the bath correlation function. Complex spectral densities and low temperature simulations often require a larger number of modes, making the simulations very expensive. Recent advances, such as the barycentric spectral decomposition (BSD) technique, can significantly reduce the number of effective modes, allowing to handle complex spectral densities and enabling simulations at very low temperatures, including near-zero temperature dynamics.Another key improvement in the computational efficiency is the use of tensor network methods like matrix product states and hierarchical tensor networks. These techniques allow for efficient HEOM propagation with thousands of effective modes, crucial for simulating large molecular systems interacting with multiple baths. This combination enables simulations of excitation energy transfer (EET) in systems like the Fenna-Matthews-Olson (FMO) complex and even larger systems with experimentally determined spectral densities.The versatility of the HEOM method is demonstrated through applications to a wide range of chemical dynamics problems. Simulations of EET and related ultrafast spectroscopy are first briefly covered. Applications of the HEOM to quantum tunneling effects in proton transfer reactions are then presented. Early works have studied the non-Kramers dependence of the rate constant as a function of bath friction due to deep tunneling and revealed vibrationally nonadiabatic dynamics within the so-called nontraditional view of proton transfer reactions. A recent work on the large kinetic isotope effects in soybean lipoxygenase also indicated that many quantum correction approximations to classical transition-state theory may fall short in describing deep tunneling effects.Charge transport and separation dynamics in organic semiconductors are another area where the HEOM method has been instrumental. We first demonstrate that the HEOM provides a unified description of both band-like and thermally assisted charge carrier transport in organic materials. The effect of non-nearest neighbor transitions is then investigated by combining generalized master equations with exact memory kernels. The HEOM method also enables simulation of charge separation in organic photovoltaics (OPVs) and reveals how factors such as external electric fields, entropy, and charge delocalization influence the charge separation barrier and dynamics.Moreover, HEOM has been applied to investigate hydrogen atom scattering on the Au(111) surface and vibrational energy relaxation at molecule-metal interfaces. These studies provide deeper insights into how electron-hole pair excitations and temporary charge transfer states influence the nuclear motion, offering a new framework for simulating nonadiabatic dynamics on metal surfaces.In summary, the HEOM method has developed into a robust tool for simulating quantum effects in condensed phases. Future developments in algorithm efficiency and computational power will likely expand its applicability to even more complex systems.

Heat and mass transfer in MHD flow of SWCNT and graphene nanoparticles suspension in Casson fluid

AbstractThe exceptional mechanical properties of carbon nanotubes (CNTs) and graphene nanoparticles, particularly their high aspect ratio and flexibility, position them as highly promising materials for the next generation of armor technology. Their incorporation into protective wear, such as bullet‐proof vests, offers the potential for significant improvements in both performance and cost efficiency. By replacing traditional materials with CNTs and graphene, the durability and maintenance of these protective devices could be substantially enhanced, leading to more effective and sustainable solutions for personal safety. This study focuses on the complex phenomena of heat and mass transport within a suspension of CNTs and graphene nanoparticles dispersed in a Casson fluid, particularly under the influence of an applied magnetic field. The analysis begins by reformulating the governing equations using similarity variables to transform them into a dimensionless form to simplify the problem. The resulting dimensionless equations are then solved using the three‐stage Lobatto IIIa finite difference method, a robust numerical technique well‐suited for solving boundary value problems in fluid dynamics. The results of this investigation highlight a significant 78.41% reduction in skin friction when compared with traditional CNT‐water nanofluid systems. This considerable decrease in skin friction not only reveals the superior performance of the CNT‐graphene nanofluid suspension but also opens up new possibilities for the design and development of advanced protective materials. These findings contribute to the growing body of knowledge on nanofluid applications and offer valuable insights for future research in the field of advanced armor technology.

Seal Pipeline: Enhancing Dynamic Object Detection and Tracking for Autonomous Unmanned Surface Vehicles in Maritime Environments

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

New and Modified Homotopy Perturbation Methods for Addressing Burger’s Non-linear Equation

Objectives: The aim of this study is to find a novel solution procedure for solving a fluid dynamical problem, especially to solve two-dimensional coupled Burger’s non-linear equation. Methods: New and Modified homotopy perturbation techniques are used to solve the two-dimensional coupled Burger’s equation. The methods intend to make homotopy perturbation method a more robust and trustworthy tool for fluid dynamics researchers by addressing convergence concerns, improving solution accuracy and allowing it to handle a broader range of problems. Findings: The solution for two-dimensional coupled Burger’s non-linear equation is obtained by constructing homotopies. Numerical results thus obtained are compared with the exact solution. The obtained results and the exact solutions to the stated problem are closely related. Novelty: The New and Modified Homotopy Perturbation Method proposed in this paper are effective mathematical tools for getting an exact solution to the coupled Burgers' equations. It is also a potential approach for solving various linear and nonlinear partial differential equations. Keywords: Perturbation, Homotopy, New homotopy perturbation, Modified homotopy perturbation, Two­dimension, Linear, Coupled burgers' equation

A class of evolutionary history-dependent variational-hemivariational inequalities with strong and weak solutions

AbstractIn this paper we examine the well-posedness of evolutionary variational-hemivariational inequalities involving a constraint set and history-dependent operators. The strong and weak formulations of such inequalities are studied. First, the existence and uniqueness of solutions to both formulations are proved, and results on the dependence of solution on functional parameters are delivered. Next, the well-posedness is established for a general form of history-dependent variational-hemivariational inequalities with constraints by using a fixed point theorem. Finally, the results are applied to a dynamic frictional contact problem in viscoelasticity in which the contact is described by Signorini-type unilateral boundary condition with a nonmonotone Clarke’s relation.

DEVELOPMENT OF THE THEORY OF GAS FUEL COMBUSTION TAKING INTO ACCOUNT MODERN KINETIC MECHANISMS OF COMBUSTION

An actuality of modernizing advancement the modern adopted combustion theory is due to a significant change in the composition of fuels at the market of developed countries by rejection of basic mineral (organic) fuels (fossil fuels), which have been used in the world for 250 years. The transformation of traditional fuels’ orientation towards an application of carbon-free fuels, primarily to hydrogen and its mixtures with natural gas. Each way of mentioned activity is connected with to ensure an environmental decarbonisation. Criteria for evaluating the CO2 emissions by fuels’ choice and selection have been proposed while numerical calculations of the specific CO2 content in the combustion products of the hydrocarbon-hydrogen mixture jʹ and j have been evaluated. It is possible to reduce these values when adding the shared H2 in the fuels (m3 CO2/ MJ; kg CO2/ MJ). Considering the combustion process in combustion systems (combustion chambers, boilers, furnaces) at constant pressure (p =const, dp =0 — isobaric process), the enthalpy of the reactants’ flow of the mixture (total (chemical) enthalpy I) can serve as a measure of the specific energy of the reacting mixture. An approach is proposed, according to which the main equations of heat and mass transfer in the torch (premixed flame) are composed basing upon the principle of conservation the total (chemical) enthalpy representing energy as basic function. The basic approach to the development of a modern combustion theory is focused on the concept of T. von Karman, according to which the combustion process is considered as “aerothermochemistry”, i.e. the description of physical and chemical processes is based on the combination of heat and mass transfer equations with chemical kinetics’ equations. At the current period, the problem of chemical kinetics (burning process) is considered through the combustion mechanisms of basic fuels, which consist of a large number of equations for molecules and particles involved in process at various stages of the combustion — from the preheating of the reaction mixture and through the stage of ignition (or self-ignition) — to the final temperature of flue gases. The laminar combustion velocity SL is a determinative characteristic of the combustible properties of gas fuels and, accordingly, of the safety restrictions by the fuel choice. Quantitative values of SL for the separate basic fuels, including the hydrogen, both the organic (fossil fuels) and alternative ones could be differed by an order of magnitude and more. The problem of estimable forecasting the SL value provides an option of the composition of the fuel-oxidant mixture in the industry, power and municipal energetics. The SL value’s prediction makes an especially important action at the current stage of modelling completion with an account of the global warming and by preventing an excess of CO2 emissions, including involvement of hydrogen-containing gases by substitution the carbon-friendly fuels, firstly the fossil fuels. The contemporary theory of combustion has been developed by extensive use the adequate kinetic mechanism GRI-Mech 3.0, the universality of which has been confirmed by our calculations of SL values for a wide range of the gas fuels — from methane CH4 to hydrogen H2 and their mixtures. An advanced modern theory of combustion using the adequate and approved GRI-Mech 3.0 combustion kinetic mechanism has been developed. The universality of this mechanism is confirmed by our calculations of SL values for a wide range of gaseous fuels — from methane CH4 to hydrogen H2 and their mixtures (mixed gases MG). For further analysis and development, the model of combustion in the reacting flow by Y.B. Zeldovich and D.A. Frank-Kamenetsky was used, considering the transfer processes of total enthalpy I as the energy characteristic of the combustible mixture. The kinetic component (on the example of natural gas) is taken into account using the combustion mechanism for GRI-Mech 3.0. The latter summarizes the combustion process of through 325 constituent reactions for 53 components. The adequacy of the combustion model proposed in the paper is confirmed by numerical examples of the coincidence of numerical SL values: ours — calculated; literary — experimental. The validity of experimental values of SL is provided by the use of different methods of measuring the SL. Calculations of the specific total enthalpy I of the reacting mixture along the length x º l (thickness of the front for a homogeneous flame in a one-dimensional formulation) have been performed as a result of researches confirmed the initial position: I(x) = const with deviations for CH4 +7 % / –2 %; for H2 +9,2 % / –2,8 % in relation to the heat of combustion. Bibl. 52, Fig. 9, Tab. 3.

A Solution to Dynamic Weapon Assignment Problem Based on Game Theory for Naval Platforms

Weapon target assignment is a critical challenge in military contexts. Traditionally, commanding officers manually decide weapon assignments, but the problem’s complexity has grown over time. To address this, automated systems have been introduced. These systems fall into two categories, which are static (time-independent) and dynamic (considering changes over time). Static systems solve the problem for a single time step without considering temporal changes. Dynamic systems incorporate time as a variable, adapting to evolving scenarios. Two main approaches exist, which are asset-based and target-based. Asset-based approach maximizes the survival probability of assets, which is our focus in this study. We propose a solution using game theory that spans the entire area and all time frames. We employ game theory, treating continuous functions of time as utility functions for vessels. Continuous probability-to-kill values for weapons are defined across the area. Threat trajectories yield continuous kill probabilities for the weapons, translating to vessel utility. To avoid inefficiencies, we align individual vessel utility with global utility. The Nash Equilibrium provides the optimal weapon assignment strategy. Our study uses a naval environment for analysis. In summary, our research leverages game theory to dynamically assign weapons to naval vessels, aiming to maximize asset survival.