Continuous Simulation Research Articles

Research on the Hydrodynamic Characteristics of a Rectangular Otter Board in Different Work Postures Based on a Dynamic Model

This paper investigates the hydrodynamic characteristics of a rectangular otter board in different working postures by using a dynamic model. Dynamic models are mainly based on dynamic mesh techniques. The results of the dynamic model are, compared to the model test, carried out in a flume tank. Furthermore, different rotation speeds of dynamic model were analyzed. The research results are as follows: compared to flume tank results, the maximum error of the dynamic model is 23.77%. Moreover, the influence of rotation speed on the hydrodynamic board is not obvious, and 2 deg./s was chosen as the rotation speed. When the board is tilted slightly (including four working postures), its lift-to-drag ratio first increases slightly and then gradually decreases. Compared with the other three working postures, the pressure center coefficient of the board does not change significantly when it is tilted inward. When studying different working angles (including AOA and tilt angle) of the otter board, the numerical dynamic model significantly reduces repetitive setup work, making simulations more efficient. Its ability to provide continuous curves and a large volume of results offers researchers a more detailed and comprehensive understanding of the board’s hydrodynamics. Additionally, the dynamic model supports innovative fishery equipment development by allowing more accurate and continuous numerical simulations.

Enhancing Information Exchange in Ship Maintenance through Digital Twins and IoT: A Comprehensive Framework

This article proposes a comprehensive framework for enhancing information exchange in ship maintenance through the integration of Digital Twins (DTs) and the Internet of Things (IoT). The maritime industry faces significant challenges in maintaining ships due to issues like data silos, delayed information flow, and insufficient real-time updates. By leveraging advanced technologies such as DTs and IoT, this framework aims to optimize maintenance processes, improve decision-making, and increase the operational efficiency of maritime vessels. Digital Twins create virtual replicas of physical assets, allowing for continuous monitoring, simulation, and prediction of ship conditions. Meanwhile, IoT devices enable real-time data collection and transmission from various ship components, facilitating a seamless flow of information. This integrated approach enhances predictive maintenance capabilities, reduces downtime, and improves resource allocation. The article will delve into the architecture of the proposed framework, implementation steps, and potential challenges, supported by case studies that demonstrate its practical application and benefits. By addressing these aspects, the framework aims to provide a robust solution for modernizing ship maintenance operations and ensuring the longevity and reliability of maritime assets.

A Novel Application of Computational Contact Tools on Nonlinear Finite Element Analysis to Predict Ground-Borne Vibrations Generated by Trains in Ballasted Tracks

Predictive numerical models in the study of ground-borne vibrations generated by railway systems have traditionally relied on the subsystem partition approach (segmented). In such a method, loads are individually applied, and the cumulative effect of the rolling stock is obtained through superposition. While this method serves to mitigate computational costs, it may not fully capture the complex interactions involved in ground-borne vibrations—especially in the frequency domain. Recent advancements in computation and software have enabled the development of more sophisticated vibrational contamination prediction models that encompass the entire dynamics of the system, from the rolling stock to the terrain, allowing continuous simulations with a defined time step. Furthermore, the incorporation of computational contact mechanics tools between various elements not only ensures accuracy in the time domain but also extends the analysis into the frequency domain. In this novel approach, the segmented models are shifted to continuous simulations where the nonlinear problem of a rigid–flexible multibody system is fully considered. The model can predict the impact of a high-speed rail (HSR) vehicle passing, capturing the key intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain.

Damage evolution simulation and lifetime prediction for composite blades under continuous droplet impacts

Offshore wind power generation is a promising technology in renewable energy applications due to its high reserves of wind energy in sea areas. To improve the energy transformation efficiency, the blade length of the offshore wind turbines has become larger and larger, and it has made rain erosion be one of the most frequent failures during the turbine operation. As the natural rainfall is stochastic in spatial and time domains, it is difficult to depict the damage evolution process caused by rain impact exactly. Therefore, regular and continuous droplet impact simulation and experiments present an alternative methodology for this issue. With the composite structure of the blade and deformability of the liquid droplet in consideration, a fluid solid interaction model will be established to investigate the impact response and subsequent damage evolution. In which, the Smooth Particle Hydrodynamics (SPH) model is utilized to depict the constitutive relationship within the droplet, and Finite Element Method (FEM) is used to construct the Representative Volume Element (RVE) model of the blade leading edge. The impact process is simulated first to obtain the impact pressure distribution at the contact center and velocity field in the droplet. Furthermore, the stress wave propagation in the blade multilayer structure can be analyzed. Owing to the multiaxial fatigue feature of the continuous droplet impact, the continuum damage mechanics is integrated with the fatigue criterion and the Jump-in-Cycle procedure is used to simulate the high-cycle fatigue process. The damage factor distribution on the blade coating surface and its influence on mechanical properties are analyzed. Thereafter, the droplet impact fatigue life can be accumulated based on Miner’s linear rules. The theoretical achievements are validated by experimental data provided by Rain Erosion Testing (RET), which shows a good agreement between each other. As a result, V-N curves and D-N curves, i.e. quantitative relationship between droplet falling conditions and impact fatigue life, are established. The achievements in this study can provide an effective tool for rain erosion mechanism analysis and life prediction in industrial applications.

A model framework for atmosphere–snow water vapor exchange and the associated isotope effects at Dome Argus, Antarctica – Part 1: The diurnal changes

Abstract. Ice-core water isotopes contain valuable information on past climate changes. However, such information can be altered by post-depositional processing after snow deposition. Atmosphere–snow water vapor exchange is one such process, but its influence remains poorly constrained. Here we constructed a box model to quantify the atmosphere–snow water vapor exchange fluxes and the associated isotope effects at sites with low snow accumulation rates, where the effects of atmosphere–snow water vapor exchange are suspected to be large. The model reproduced the observed diurnal variations in δ18O, δD, and deuterium excess (d-excess) in water vapor at Dome C, East Antarctica. According to the same model framework, we found that under average summer clear-sky conditions, atmosphere–snow water vapor exchange at Dome A can cause diurnal variations in atmospheric water vapor δ18O and δD of 4.8 ‰ ± 2.6 ‰ and 29 ‰ ± 19 ‰, with corresponding diurnal variations in surface snow δ18O and δD of 0.80 ‰ ± 0.35 ‰ and 1.6 ‰ ± 2.7 ‰. The modeled results under summer cloudy conditions display similar patterns to those under clear-sky conditions but with much smaller magnitudes of diurnal variations. However, under winter conditions at Dome A, the model predicts few to no diurnal changes in snow isotopes, consistent with the stable boundary condition in winter that inhibits effective vapor exchange between the atmosphere and snow. In addition, after 24 h and continuous simulations of 11 d, the model predicts significant enrichments in snow isotopes under summer conditions, while in winter, the depletions also accumulate after each 24 h simulation but with a much smaller magnitude of change compared to the results from summer simulations. If the modeled snow isotope enrichments in summer conditions and the depletions in winter conditions represent the general situation at Dome A, this likely suggests that atmosphere–snow water vapor exchange tends to increase snow isotope seasonality, and the annual net effect would be overall enrichments in snow isotopes since the effects in summer appear to be greater than those in winter. This trend will need to be further explored in the future with more comprehensive model studies and/or field observations and experiments.

Constructing Long‐Term Hydrographs for River Climate‐Resilience: A Novel Approach for Studying Centennial to Millennial River Behavior

AbstractStudying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling.

Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.

Self-induced Bose glass phase in quantum quasicrystals

We study the emergence of Bose glass phases in self sustained bosonic quasicrystals induced by a pair interaction between particles of Lifshitz–Petrich type. By using a mean-field variational method designed in momentum space as well as Gross–Pitaevskii simulations we determine the phase diagram of the model. The study of the local and global superfluid fraction allows the identification of supersolid, super quasicrystal, Bose glass and insulating phases. The Bose glass phase emerges as a quasicrystal phase in which the global superfluidity is essentially zero, while the local superfluidity remains finite in certain ring structures of the quasicrystalline pattern. Furthermore, we perform continuous space Path Integral Monte Carlo simulations for a case in which the interaction between particles stabilizes a quasicrystal phase. Our results show that as the strength of the interaction between particles is increased the system undergoes a sequence of states consistent with the super quasicrystal, Bose glass, and quasicrystal insulator thermodynamic phases.

A meshless model for three-dimensional direct numerical simulation of local scour around cylinder bridge foundation

A meshless local scour model for cylinder bridge foundation based on the smoothed particle hydrodynamics (SPH) method is proposed in this paper. Differing from the traditional Eulerian mesh model used for monopile local scour, it overcomes the high requirements of mesh quality and density for capturing large deformations at the fluid interface. Unlike the transient sediment erosion SPH model used for dam-break, sediment flushing, and water jet, the meshless approach is innovatively applied to the continuous simulation of bridge local scour under steady flow conditions. The present SPH model is established based on the SPH multi-phase fluid model, integrating a classical fluid model combined with numerical stabilization algorithms to govern water particle motion and a sediment model from Zhang et al. (2024) to govern sediment evolution, and tests a new scheme for boundary treatment in the model based on DualSPHysics. Furthermore, a novel scour visualization method that aims at extracting both visible and actual bed surfaces from discrete particles is proposed. The model is validated through comparison with rigid bed and live bed experiments, demonstrating favorable agreement in terms of flow and scour simulation. Importantly, the model results reveal a discrepancy between the elevation of the post-scour visible bed surface derived from the conventional Eulerian mesh model and experimental data, compared to the actual bed surface unaffected by water flow erosion. This indicates the necessity for incorporating a safety margin in foundation design when utilizing experimental results to determine the maximum scour depth.

Real-time regulation of detention ponds via feedback control: Balancing flood mitigation and water quality

Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves, and spillways can have controllable gates. The real-time optimal operation of its hydraulic devices can be achieved with techniques such as Model Predictive Control (MPC). A distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model is developed and integrated with an MPC algorithm to estimate the operation of valves and movable gates in real-time. The control optimization problem is adapted to switch from a flood-related algorithm focusing on mitigating floods to a heuristic objective function that aims to increase the detention time when no inflow hydrographs are predicted. The case studies show the potential results of applying the methods developed in a catchment in Sao Paulo, Brazil. The performance of MPC compared to alternatives that do not change the operation over time with either fully or partially open valves and gates are tested. Comparisons with HEC-RAS 2D indicate volume and peak flow errors of approximately 1.4% and 0.91% for the watershed module. Simulating two consecutive 10-year storms shows that the MPC strategy can achieve peak flow reductions of 79%. In contrast, the passive scenario has nearly half of the performance (41%). A 1-year continuous simulation results show that the passive scenario with 25% of the valves opened can treat 12% more runoff compared to the developed MPC approach, with an average detention time of approximately 6 h. For the MPC approach, the average detention time is nearly 14 h, indicating that both control techniques can treat similar volumes; however, the proxy water quality for the MPC approach is enhanced due to the longer detention times achieved.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity-by-descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

Effect of dissolved KOH and NaCl on the solubility of water in hydrogen: A Monte Carlo simulation study.

Vapor-Liquid Equilibria (VLE) of hydrogen (H2) and aqueous electrolyte (KOH and NaCl) solutions are central to numerous industrial applications such as alkaline electrolysis and underground hydrogen storage. Continuous fractional component Monte Carlo simulations are performed to compute the VLE of H2 and aqueous electrolyte solutions at 298-423K, 10-400bar, 0-8 molKOH/kg water, and 0-6 molNaCl/kg water. The densities and activities of water in aqueous KOH and NaCl solutions are accurately modeled (within 2% deviation from experiments) using the non-polarizable Madrid-2019 Na+/Cl- ion force fields for NaCl and the Madrid-Transport K+ and Delft Force Field of OH- for KOH, combined with the TIP4P/2005 water force field. A free energy correction (independent of pressure, salt type, and salt molality) is applied to the computed infinite dilution excess chemical potentials of H2 and water, resulting in accurate predictions (within 5% of experiments) for the solubilities of H2 in water and the saturated vapor pressures of water for a temperature range of 298-363K. The compositions of water and H2 are computed using an iterative scheme from the liquid phase excess chemical potentials and densities, in which the gas phase fugacities are computed using the GERG-2008 equation of state. For the first time, the VLE of H2 and aqueous KOH/NaCl systems are accurately captured with respect to experiments (i.e., for both the liquid and gas phase compositions) without compromising the liquid phase properties or performing any refitting of force fields.

Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.

Response of hypoxia to future climate change is sensitive to methodological assumptions

Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial–estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.

Effects of auricular acupoint pressing combined with continuous scenario simulation nursing interventions for patients with Meniere's disease

Effects of auricular acupoint pressing combined with continuous scenario simulation nursing interventions for patients with Meniere's disease

Activation of the influenza B M2 proton channel (BM2).

Influenza B viruses have co-circulated during most seasonal flu epidemics and can cause significant human morbidity and mortality due to their rapid mutation, emerging drug resistance, and severe impact on vulnerable populations. The influenza B M2 proton channel (BM2) plays an essential role in viral replication, but the mechanisms behind its symmetric proton conductance and the involvement of a second histidine (His27) cluster remain unclear. Here we perform the membrane- enabled continuous constant-pH molecular dynamics simulations on wildtype BM2 and a key H27A mutant to explore its pH-dependent conformational switch. Simulations capture the activation as the first histidine (His19) protonates and reveal the transition at lower pH values compared to AM2 is a result of electrostatic repulsions between His19 and pre-protonated His27. Crucially, we provide an atomic-level understanding of the symmetric proton conduction by identifying pre-activating channel hydration in the C-terminal portion. This research advances our understanding of the function of BM2 function and lays the groundwork for further chemically reactive modeling of the explicit proton transport process as well as possible anti-flu drug design efforts.

Nursing leadership in disaster preparedness and response lessons learned and future directions

Nursing leadership is integral to effective disaster preparedness and response, demanding agility, strategic planning, and robust resource management. Disasters, both natural and man-made, present unique challenges that require nursing leaders to be adept in rapid decision-making and efficient coordination. The unpredictability of disasters necessitates comprehensive and regularly updated preparedness plans, ensuring nursing teams are well-versed in their roles through continuous training and simulations. Interprofessional collaboration and strong community partnerships are essential, fostering a unified response and efficient resource distribution. Communication remains a cornerstone of disaster management, with clear, timely information dissemination being crucial for coordinating efforts and maintaining public trust. Resource scarcity during disasters, such as shortages of medications and personal protective equipment (PPE), poses significant challenges. Effective resource management strategies are essential to maintain patient care standards and protect healthcare workers. The psychological toll on healthcare providers is another critical issue, with nursing leaders needing to implement support systems that address mental health and promote resilience. Integrating advanced technologies like telehealth and data analytics can enhance disaster response, providing remote care options and enabling predictive modeling for better resource allocation. Innovative approaches in nursing leadership include the use of telehealth for remote consultations and monitoring, data analytics for predictive modeling, and interprofessional education to improve teamwork and coordination. Strengthening community partnerships enhances overall disaster response capacity, ensuring that community-specific needs are addressed. Mental health support for healthcare providers, both immediate and long-term, is vital for maintaining a resilient workforce. Policy advocacy by nursing leaders is crucial to developing robust disaster preparedness frameworks and securing adequate funding. In summary, nursing leadership is pivotal in disaster preparedness and response, with a focus on strategic planning, interprofessional collaboration, effective communication, resource management, mental health support, and the integration of advanced technologies. Emphasizing these areas will enhance the resilience and responsiveness of healthcare systems, ultimately improving outcomes for patients and healthcare providers during disasters.

The Ability of Different Tea Tree Germplasm Resources in South China to Aggregate Rhizosphere Soil Characteristic Fungi Affects Tea Quality.

It is generally recognized that the quality differences in plant germplasm resources are genetically determined, and that only a good "pedigree" can have good quality. Ecological memory of plants and rhizosphere soil fungi provides a new perspective to understand this phenomenon. Here, we selected 45 tea tree germplasm resources and analyzed the rhizosphere soil fungi, nutrient content and tea quality. We found that the ecological memory of tea trees for soil fungi led to the recruitment and aggregation of dominant fungal populations that were similar across tea tree varieties, differing only in the number of fungi. We performed continuous simulation and validation to identify four characteristic fungal genera that determined the quality differences. Further analysis showed that the greater the recruitment and aggregation of Saitozyma and Archaeorhizomyces by tea trees, the greater the rejection of Chaetomium and Trechispora, the higher the available nutrient content in the soil and the better the tea quality. In summary, our study presents a new perspective, showing that ecological memory between tea trees and rhizosphere soil fungi leads to differences in plants' ability to recruit and aggregate characteristic fungi, which is one of the most important determinants of tea quality. The artificial inoculation of rhizosphere fungi may reconstruct the ecological memory of tea trees and substantially improve their quality.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

Research on relative reachable domain in target orbit for maneuvering spacecraft

PurposeLots of successful space missions require that the maneuvering spacecraft can reach the target spacecraft. Therefore, research on relative reachable domain (RRD) in target orbit for maneuvering spacecraft is particularly important and is currently a hot-debated topic in the field of aerospace. This paper aims at analyzing and simulating the RRD in target orbit for maneuvering spacecrafts with a single fixed-magnitude impulse and continuous thrust, respectively, to provide a basis for analyzing the feasibility of spacecraft maneuvering missions and improving the design efficiency of spacecraft maneuvering missions.Design/methodology/approachBased on the kinematics model of relative motion, RRD in target orbit for maneuvering spacecraft with a single fixed-magnitude impulse can be calculated via analyzing the relationship between orbital elements, position vector and velocity vector of spacecrafts, and relevant studies are introduced to compare simulation results for the same case and validate the method proposed in the paper. With analysis of the dynamic model of relative motion, the calculation of RRD in target orbit for maneuvering spacecraft with continuous thrust can be transformed as the solution of the optimal control problem, and example emulations are carried out to validate the method.FindingsFor the case with a single fixed-magnitude impulse, simulation results show preliminarily that the method is in agreement with the method in Ref. (Wen et al., 2016), which treats the same case and thus is plausibly correct and feasible. For the case with continuous thrust, analysis and simulation results confirm the validity of the proposed method. The methods based on relative motion in this paper can efficiently determining the RRD in target orbit for maneuvering spacecraft.Originality/valueBoth theoretical analyses and simulation results indicate that the method proposed in this paper is comparatively simple but efficient for determine the RRD in target orbit for maneuvering spacecraft swiftly and precisely.