Modeling Of Physical Processes Research Articles

BASES OF THE METHOD OF PHYSICAL MODELING OF THE PROCESS OF CRUSHING OF MUNICIPAL SOLID WASTE

A large number of studies are aimed at increasing the energy efficiency of grinding processes of various solid materials, while maintaining the values ​​of other important indicators, such as material consumption, productivity, etc. Based on this trend, a new method for physically modeling the process of grinding municipal solid waste (MSW) was proposed for the first time. Existing physical modeling techniques are designed for homogeneous and isotropic materials (for example, soil, crushed stone, snow, coal, etc.). The strength properties of solid waste vary widely due to the significant heterogeneity of their components. Consequently, when crushing solid waste, traditional crusher designs have low efficiency in terms of energy intensity, material intensity and product quality. The purpose of this work is to develop a new technique for physical modeling of the grinding process, based on the main principles of similarity theory and modeling, considering the properties of waste heterogeneity. As a result of the research, a block diagram of the physical modeling methodology for the interaction of the working bodies of impact crushing machines with solid waste was developed. A list of tasks for the modeling process and similarity criteria have been determined based on the development of rheological models of the “working body - municipal solid waste” system and the laws of mechanics that characterize the waste grinding process. Based on the developed similarity criteria, scale equations for the grinding process are substantiated and formulas are derived for determining the expected parameters of the original based on the parameters measured on the model. The developed methodology makes it possible to create a crusher design with improved energy efficiency indicators with the least material and labor costs.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

Experimental study of a thin thermally grown oxide layer in thermal barrier coatings based on the SWT-BP algorithm and terahertz technology

As a promising nondestructive testing (NDT) technique with a very adaptive physical modeling of wave transmission process, terahertz technology is used for the detection and characterization of nonpolar materials and the evaluation of layered and/or defective structures. THz-TDS can also be used to perform spectroscopic analysis and detect structural defects in thermal barrier coatings (TBCs) of aero-engines. Although it is generally difficult to measure the structure of the thin oxide layer of the thermal barrier coatings whose thickness is generally lower than 30 µm (the current axial resolution of the THz-TDS cannot exceed 30 µm). We were able to complete the detection of the oxide layer within 1–29 µm through simulation by using the SWT-BP algorithm. In this study, the analysis was performed on real-world samples, the fitting degree of the SWT-BP algorithm reached 0.77, and the minimum prediction error was less than 0.1 µm. The paper also put forward some improvement measures about the experimental results.

Nonlinear dynamics and optimal control strategies of a novel fractional-order lumpy skin disease model

Fractional order differential equations are often employed to better and accurately model several natural and physical processes with memory characteristics than the integer-order models. In this work, a proposed fractional-order model is presented to simulate one of the most dangerous viral cattle diseases. The present fractional-order Lumpy Skin Disease model considers the interactions between different compartments of cattle and virus vectors. The solution of the model is examined in terms of existence, uniqueness, positivity and boundedness. Then, the stability analysis of the different equilibrium points in the model is carried out. The basic reproduction number, R0, has been obtained too. The dependence of stability regions on the values of key parameters is demonstrated and the sensitivity of R0 to different parameters in the system is also investigated. Finally, a proposed optimal control scheme is adopted to successfully diminish the disease spread. It is found that the stability of disease-free equilibrium point is verified at small values of the infection rate of cattle caused by infected cattle or insects. In addition, the disease-free equilibrium point maintains its stability in cases where the recovery rate of cattle or the vaccination rate of cattle are increased. Moreover, the high-efficiency vaccination program helps in stabilizing the disease-free equilibrium point. Also, the fractional-order parameter is found to provide additional degree of freedom in the model to represent different scenarios of evolution for solution orbits. The optimal control parameters of the model are examined in different scenarios to limit or suppress the spread of the disease. It is demonstrated that two control parameters are sufficient to achieve the goals of disease elimination and stabilization of an infection-free steady state. Numerical simulations are verified to confirm our theoretical findings and estimations.

Mathematical modeling of the creation process and aging of polymer composite materials

Based on the analysis of the literature on the possibility of using neural networks to create new materials with high functional properties, the article considers a solution to the problem of determining the operational stability of polymeric composite materials by creating physical and chemically sound mathematical prediction models. Epoxy resins of the UP-637 and EA brands with an isophorone diamine hardener were chosen as the matrix of the model composite material, and oligobutadiene rubber of the SKN-10 KTR brand was chosen as the modifier. It justifies directions of work necessary for development of new materials creation methodology with optimal characteristics, building a model for changing the properties of materials at variation of composition and implementation of full-scale mathematical modeling of physical and chemical processes of polymer composite materials aging at changing level and time of climatic factors influence. Verification of the obtained dependence of service characteristics on the composition of the material and the level of influencing climatic factors was carried out on the basis of data from full-scale tests in a temperate climate. The proposed methodology for modelling the properties of polymer composite materials will reduce the development time of new materials and allow creation of polymer composites based on epoxy resin containing fillers of various natures (carbon, mineral and polymer) with high performance parameters.

On the behaviors of solutions of a nonlinear diffusion system with a source and nonlinear boundary conditions

We study the global solvability and unsolvability of a nonlinear diffusion system with nonlinear boundary conditions in the case of slow diffusion. We obtain the critical exponent of the Fujita type and the critical global existence exponent, which plays a significant part in analyzing the qualitative characteristics of nonlinear models of reaction-diffusion, heat transfer, filtration, and other physical, chemical, and biological processes. In the global solvability case, the key components of the asymptotic solutions are obtained. Iterative methods, which quickly converge to the exact solution while maintaining the qualitative characteristics of the nonlinear processes under study, are known to require the presence of an appropriate initial approximation. This presents a significant challenge for the numerical solution of nonlinear problems. A successful selection of initial approximations allows for the resolution of this challenge, which depends on the value of the numerical parameters of the equation, which are primarily in the computations recommended using an asymptotic formula. Using the asymptotics of self-similar solutions as the initial approximation for the iterative process, numerical calculations and analysis of the results are carried out. The outcomes of numerical experiments demonstrate that the results are in excellent accord with the physics of the process under consideration of the nonlinear diffusion system.

OBTAINING THE NECESSARY MECHANICAL PROPERTIES OF BLANKS OF PARTS MADE OF ALUMINUM ALLOY 7075 BY PHYSICAL MODELING

The article considers physical modeling for aluminum alloy 7075, widely used in mechanical engineering, in particular for elements of aircraft structures. This alloy differs from other rolled aluminum-based alloys not only by high values of strength properties, but also by the presence of a large number of intermetallic compounds of alloying elements. The workpiece for the parts is a round rolled product with regulated mechanical properties. Thus, the purpose of this work was to determine the effect of the deformation pattern that occurs in metal during bar rolling on the mechanical properties of blanks of parts made of aluminum alloy 7075. The present study draws attention to the importance of physical modeling in determining the characteristics of materials necessary for effective management of plastic processing processes. Analyzing the results of physical modeling, we identify the optimal parameters of material processing, such as microstructure, mechanical properties and chemical composition. The results obtained allow a deeper understanding of the physical mechanisms underlying the production of blanks (rods) from aluminum rods of alloy 7075 by rolling, as well as optimizing it to achieve the required technical characteristics of products. Thus, as a result of physical modeling of the rolling process of bars made of aluminum alloy 7075, it was found that when the samples are deformed at a temperature of 250 ° C, the value of the plasticizing stress decreases by about 18%, and a more uniform distribution of microhardness over the transverse and longitudinal sections was obtained compared with deformation at a temperature of 200 °C. The research is of practical importance for the metalworking industry, and can also be used for educational purposes to train specialists in the field of materials science and metalworking. Keywords: workpiece parts, physical modeling, aluminum alloy 7075, metalworking, aluminum alloy microstructure, deformation of samples.

Comparative analysis of two-dimensional and three-dimensional modeling of heat transfer during operation of a gas infrared heater indoor

Relevance. It is proposed to heat local work areas with systems based on gas infrared heaters, capable of directing radiative heat flow to reduce heating costs in large premises. However, the widespread use of gas infrared heaters is hampered by the existing difficulties with the preliminary assessment of convective-radiative heat flows movement, on which the number and location of heating devices depends. The preliminary assessment is complicated by the need in some cases for 3D modeling of complex physical processes. It is necessary to evaluate the possibility of replacing labor-intensive 3D modeling with a method for calculating a heating system using gas infrared emitters based on a 2D approach to reduce the time spent on calculations. Aim. To prove that the use of a two-dimensional model of the processes under consideration makes it possible to obtain the main characteristics of the thermal regime of the premises, making it possible to replace spatial modeling. Objects. Heating system with a light-type gas infrared heater and an air exchange system. Methods. Two-dimensional and three-dimensional mathematical modeling of conjugate heat transfer processes using the finite element method. Mathematical modeling was carried out in the COMSOL Multiphysics software environment using the modules: “Heat Transfer Interface in Liquids”, “Radiation between Surfaces” and “Turbulent Flow, k-ε Interface”. Results. The article presents the results of mathematical modeling performed in three-dimensional and two-dimensional formulations. The distribution of temperatures in the air and enclosing structures, as well as the flow lines of heated air and air, which was heating, in the volume of the premise are presented. The results of two-dimensional and three-dimensional modeling were compared. Satisfactory similarity of the calculated average air temperatures in the local working area was established based on the results. The difference was less than 2℃ for different spatial modeling approaches.

Release potential, neglected leakage and reduction countermeasures of COD and Ammonia in MSWLs

In order to eliminate the impact of the industrial revolution on the environment and improve the water ecological environment, pollutant discharge reduction is imperative. With the acceleration of global discharge reduction process, the huge pollutant release potential and potential environmental effects of municipal solid waste landfills gradually appear, but its release amount and intensity have not been quantitatively revealed. We propose a coupling method of parameter stochastic simulation and physical process model simulation to estimate the hidden leakage of large-scale regional municipal solid waste landfills, and provide a methodology for estimating the hidden leakage of landfills in other countries and even in the whole world by taking China, which has the largest amount of waste generation among developing countries, as an example. Prior to the implementation of stringent construction quality control and assurance management requirements, the average annual leachate generation potential over the entire life cycle of 2600 landfills in China was estimated to be 4.66 × 108 m3, in which the concentrations of COD and NH3-N are 5.38 × 102–6.48 × 104 mg/L and 6.10–3.50 × 103 mg/L, respectively, and the total amounts are 5.21 × 103–7.81 × 108 t and 8.09 × 102–6.65 × 107 t, respectively. About 14 % of these pollutants may leak into the environmental media through the landfill liner with the average number of holes of 21.5/ha. For different regions, the overall release, discharge and leakage of COD and NH3-N in East China account for 35.70 %, 36.68 % and 29.60 % respectively, making it the region with the highest potential for discharge and risk of leakage. Meanwhile, the implementation of mandatory regulations related to leachate generation and control has led to a significant reduction in the leakage of pollutants. For instance, comprehensively detecting and repair of holes in the impermeable liner has reduced the number of holes to 2/ha, resulting in a reduction of >90 % in the leakage of pollutants.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.

Research on Prediction Model of Particulate Matter in Dalian Street Canyon

In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyons by developing and applying a prediction model based on the mathematical modeling of physical processes. The prediction model considered factors such as the influence of traffic wind, natural wind, traffic flow, and other relevant variables influencing particle mass concentrations in street canyons. Field measurements were conducted in Dalian, China, to verify the feasibility of the model. Particle mass concentrations, traffic flow, temperature, relative humidity, and wind speed were measured on Shichang Street (a two-lane one-way road), Tangshan Street (a four-lane two-way road), and Shengli Road (a six-lane two-way road). The results indicated that the majority of traffic peaks occurred around 19:00 on all road types. The PM1.0 mass concentration was well diluted on the four-lane two-way road, with the least dilution observed on the two-lane one-way road. A strong correlation between the particle mass concentrations and traffic flow was discovered. Furthermore, a prediction model was established, accurately predicting the particle mass concentrations when the prediction step was from 5 to 15 s. The coefficient of determination (R2) between the predicted and measured values on the two-lane one-way road, four-lane two-way road, and six-lane two-way road was 0.9319, 0.6582, and 0.9238, respectively. Additionally, the prediction model allowed for a detailed analysis of traffic flow limitations, corresponding to the recommended World Health Organization (WHO) PM2.5 values. Overall, the findings of this study offer valuable insights for forecasting particle exposure levels in street canyons.

Assessment and Ensemble-Based Analysis of the Landfalling Typhoon Muifa (2022)

By considering the uncertainties in the initial field, model physical processes, and lateral boundary conditions, the Shanghai Weather And Risk Model System-Ensemble Prediction System (SWARMS-EN) is constructed. According to the prediction results of typhoon Muifa (2022), the daily track error of SWARMS-EN within 5 days is 70.6 km, 142.2 km, 129.1 km, 174.5 km, and 203.5 km, respectively. When compared with the Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) and the Global Ensemble Forecast System (GEFS) of the National Centers for Environmental Prediction (NCEP) in homogeneous conditions, SWARMS-EN performs better than TEDAPS within 72 h and better than GEFS beyond 72 h in track forecasting. This indicates an improvement in forecasting accuracy. The ensemble spread within two days is less than the root mean square error (RMSE), according to an analysis of the relationship between ensemble RMSE and spread, which shows that SWARMS-EN has no apparent systematic bias overall. The system has improved the ensemble RMSE and spread, indicating that it can better represent the uncertainty of the forecast and produce more reliable forecasts. Additionally, SWARMS-EN provides the landfall forecast five days in advance. The ensemble-based analysis suggests that the large-scale circulation is the primary factor contributing to the forecast differences among members, and the strong steering flow provides an indication of the landfalling forecast. The analysis of the ensemble characteristics of the initial field indicates that the initial perturbation between the wind field and the temperature field in the dynamically unstable region (such as near a tropical cyclone) exhibits flow dependence, and the small perturbation shows continuity throughout the entire troposphere. The distribution of ensemble spread and disturbance energy exhibited a reasonable growth stage as the forecast lead time increased. Disturbance internal energy dominated the lower troposphere, while the upper troposphere was mainly characterized by disturbance kinetic energy. Disturbance kinetic energy played a leading role in the evolution process. This conclusion further confirms the importance of paying attention to the initial small perturbations near TC in order to optimize the initial perturbation.

Changes in filtration and capacitance properties of highly porous reservoir in underground gas storage: CT-based and geomechanical modeling

The paper presents the results of comprehensive studies of filtration and capacitance properties of highly porous reservoir rocks of the aquifer of an underground gas storage facility. The geomechanical part of the research included studying the dependence of rock permeability on the stress-strain state in the vicinity of the wells, and physical modeling of the implementation of the method of increasing the permeability of the wellbore zone - the method of directional unloading of the reservoir. The digital part of the research included computed tomography (CT)-based computer analysis of the internal structure, pore space characteristics, and filtration properties before and after the tests. According to the results of physical modeling of deformation and filtration processes, it is found that the permeability of rocks before fracture depends on the stress-strain state insignificantly, and this influence is reversible. However, when downhole pressure reaches 7-8 MPa, macrocracks in the rock begin to grow, accompanied by irreversible permeability increase. Porosity, geodesic tortuosity and permeability values were obtained based on digital studies and numerical modeling. A weak degree of transversal anisotropy of the filtration properties of rocks was detected. Based on the analysis of pore size distribution, pressure field and flow velocities, high homogeneity and connectivity of the rock pore space is shown. The absence of pronounced changes in pore space characteristics and pore permeability after non-uniform triaxial loading rocks was shown. On the basis of geometrical analysis of pore space, the reasons for weak permeability anisotropy were identified. The filtration-capacitance properties obtained from the digital analysis showed very good agreement with the results of field and laboratory measurements. The physical modeling has confirmed the efficiency of application of the directional unloading method for the reservoir under study. The necessary parameters of its application were calculated: bottomhole geometry, stage of operation, stresses and pressure drawdown value.

Teaching the asynchronous general physics course in a crisis context: a case study at Igor Sikorsky Kyiv Polytechnic Institute

The imposition of martial law and power outages in Ukraine made the first semester of the 2022/2023 academic year at Igor Sikorsky Kyiv Polytechnic Institute a challenge for lecturers and students, especially in learning the general physics course. An asynchronous learning mode became the only possible form in such a situation, so the problem of increasing the efficiency of conducting a lecture, problem sessions, and laboratory work in general physics arose. This study aims to describe the implementation of the discipline of general physics (part 1) promoted in an asynchronous mode by lecturers of the Department of General Physics and Modeling of Physical Processes. We verified that in crisis conditions, it is essential to: (i) provide students with all the necessary material on Moodle course and YouTube channel, allowing them to learn the theoretical material, solve the homework problems, and analyse the data from laboratory work, (ii) give feedback and consultations to students via Zoom/Telegram chat upon request, (iii) give the possibility to perform the laboratory work both in the virtual regime and as YouTube demonstration. The results of the survey of 82 students who studied the general physics course taking into account the recommendations mentioned above revealed that the majority of the surveyed students were completely/to a greater extent satisfied with the level of organisation of asynchronous learning, as well as the level of knowledge obtained, which undoubtedly indicates the effectiveness and efficiency of the proposed approach.

A theory of stochastic fluvial landscape evolution

Geometries of eroding landscapes contain important information about geologic, climatic, biotic and geomorphic processes. They are also characterized by variability, which makes disentangling their origins challenging. Observations and physical models of fluvial processes, which set the pace of erosion on most continents, emphasize complexity and variability. By contrast, the spectral content of longitudinal river profiles and similarity of geometries at scales greater than approximately 100 km highlight relatively simple emergent properties. A general challenge then, addressed in this manuscript, is development of a theory of landscape evolution that embraces such scale-dependent insights. We do so by incorporating randomness and probability into a theory of fluvial erosion. First, we explore the use of stochastic differential equations of the Langevin type, and the Fokker–Planck equation, for predicting migration of erosional fronts. Second, analytical approaches incorporating distributions of driving forces, critical thresholds and associated proxies are developed. Finally, a linear programming approach is introduced, that, at its core, treats evolution of longitudinal profiles as a Markovian stochastic problem. The theory is developed essentially from first principles and incorporates physics governing fluvial erosion. We explore predictions of this theory, including the natural growth of discontinuities and scale-dependent evolution, including local complexity and emergent simplicity.

Specialists Training for the Radiation Type of Non-Destructive Testing Based on Digital Technologies

The purpose of research is the development of application software, including VR-technologies, to increase the number of trained specialists in radiation type of nondestructive testing by reducing the per capita load on radiographic laboratories while maintaining the amount of practical training, acquired competencies and formed relevant professional skills.Methods. Prerequisites for virtualization of the process of training specialists in nondestructive testing of structural materials and fabricated products are defined. The main factors of demand for industrial radiography simulator in relation to the practical part of the educational cycle on radiation type of nondestructive testing are considered. As a result of the analysis the most significant aspects of practical training, available for their virtual realization, are singled out. The results of software implementation of mathematical models of physical and physicochemical processes of ionizing radiation, attenuation and absorption of penetrating radiation within the scope of digital twin of radiographic image generation as a result of simulation of the technological process of radiation type of nondestructive testing are presented.Results. Performed practical implementation of didactic materials in the form of hardware and software VR-simulator as part of digital educational environment to solve the problems of training, retraining, preparation for certification of personnel and providing admission of specialists to work on radiation type of nondestructive testing at facilities. Physical and technological aspects are analyzed, modeling, algorithmization and software implementation of VR-simulator are performed. A comprehensive approach to the creation of a learning environment with the use of digital technologies is presented. Considered are the main components, their goals and objectives. The structure of a practical lesson and illustrations of the training process in a virtual environment are given. The results of approbation of training programs with integration of classes in VR-simulator of industrial radiography have shown that the total time of practice of trainees increases by 40-55% with simultaneous reduction of per capita radiographic laboratory load by more than 25%. The main directions of development and scaling of software and hardware solution using VR-technologies are given.Conclusion. The developed and tested hardware-software solution together with the results of scientific research suggests the possibility of application and development directions of the digital educational system with VR-environment. The proposed approach application will allow to reduce environmental and biomedical risks by reducing the requirements to safety of training with the use of software and hardware solution. Automation of testing task generation and knowledge control procedures on the basis of software tools and mathematical models application, including methods of artificial intelligence, will allow to achieve training term reduction and will contribute to further increase in the number of trained specialists in various types and methods of nondestructive testing. With the functional capabilities expansion and evolutionary development of hardware-software solution (industrial radiography simulator) on the basis of unified modular approach, software libraries and hardware-software interfaces, virtual simulator simplifies organization of the process of specialists training and knowledge control. Practical value of the digital virtual environment (VR-environment) development is revealed due to the transfer and scaling of the obtained hardware-software solution to other types and methods of nondestructive testing according to GOST R 56542-2019 " Non-destructive testing. Classification of types and method".

A full circuit-based quantum algorithm for excited-states in quantum chemistry

Utilizing quantum computer to investigate quantum chemistry is an important research field nowadays. In addition to the ground-state problems that have been widely studied, the determination of excited-states plays a crucial role in the prediction and modeling of chemical reactions and other physical processes. Here, we propose a non-variational full circuit-based quantum algorithm for obtaining the excited-state spectrum of a quantum chemistry Hamiltonian. Compared with previous classical-quantum hybrid variational algorithms, our method eliminates the classical optimization process, reduces the resource cost caused by the interaction between different systems, and achieves faster convergence rate and stronger robustness against noise without barren plateau. The parameter updating for determining the next energy-level is naturally dependent on the energy measurement outputs of the previous energy-level and can be realized by only modifying the state preparation process of ancillary system, introducing little additional resource overhead. Numerical simulations of the algorithm with hydrogen, LiH, H2O and NH3 molecules are presented. Furthermore, we offer an experimental demonstration of the algorithm on a superconducting quantum computing platform, and the results show a good agreement with theoretical expectations. The algorithm can be widely applied to various Hamiltonian spectrum determination problems on the fault-tolerant quantum computers.

Intermittent Deployment for Large-Scale Multi-Robot Forage Perception: Data Synthesis, Prediction, and Planning

Monitoring the health and vigor of grasslands is vital for informing management decisions to optimize rotational grazing in agriculture applications. To take advantage of forage resources and improve land productivity, we require knowledge of pastureland growth patterns that is simply unavailable at the state of the art. In this paper, we propose to deploy a team of robots to monitor the evolution of an unknown pastureland environment to fulfill the above goal. To monitor such an environment, which usually evolves slowly, we need to design a strategy for rapid assessment of the environment over large areas at a low cost. Thus, we propose an integrated pipeline comprising data synthesis, deep neural network training, and prediction along with a multi-robot deployment algorithm that monitors pasturelands intermittently. Specifically, using expert-informed agricultural data coupled with novel data synthesis in ROS Gazebo, we first propose a new neural network architecture to learn the spatiotemporal dynamics of the environment. Such predictions help us to understand pastureland growth patterns on large scales and make appropriate monitoring decisions for the future. Based on our predictions, we then design an intermittent multi-robot deployment policy for low-cost monitoring. Finally, we compare the proposed pipeline with other methods, from data synthesis to prediction and planning, to corroborate our pipeline’s performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Pasturelands are an integral part of agricultural production in the United States. To take full advantage of the forage resource and avoid environmental degradation, pastureland must be managed optimally. This paper focuses on the question of how to deploy robot teams to sense and model physical processes over varying timescales. The goal of this work is to develop a new integrated pipeline for the long-term deployment of heterogeneous robot teams grounded in the problem of autonomous monitoring in precision grazing to improve land productivity. By using the proposed pipeline in grassland ecosystem management, we will have a better understanding of the physical environment while respecting energy budgets.

Coupling the COST reference plasma jet to a microfluidic device: a computational study

The use of microfluidic devices in the field of plasma-liquid interaction can unlock unique possibilities to investigate the effects of plasma-generated reactive species for environmental and biomedical applications. So far, very little simulation work has been performed on microfluidic devices in contact with a plasma source. We report on the modelling and computational simulation of physical and chemical processes taking place in a novel plasma-microfluidic platform. The main production and transport pathways of reactive species both in plasma and liquid are modelled by a novel modelling approach that combines 0D chemical kinetics and 2D transport mechanisms. This combined approach, applicable to systems where the transport of chemical species occurs in unidirectional flows at high Péclet numbers, decreases calculation times considerably compared to regular 2D simulations. It takes advantage of the low computational time of the 0D reaction models while providing spatial information through multiple plug-flow simulations to yield a quasi-2D model. The gas and liquid flow profiles are simulated entirely in 2D, together with the chemical reactions and transport of key chemical species. The model correctly predicts increased transport of hydrogen peroxide into the liquid when the microfluidic opening is placed inside the plasma effluent region, as opposed to inside the plasma region itself. Furthermore, the modelled hydrogen peroxide production and transport in the microfluidic liquid differs by less than 50% compared with experimental results. To explain this discrepancy, the limits of the 0D–2D combined approach are discussed.