System Dynamics Simulation Model Research Articles

A systems dynamics simulation model of a steel supply chain-case study

Considering the complexity, interactions, and dynamics that permeate the Supply Chain (SC), computational modeling and simulation promote determining the system's behavior and decision-making. Among simulation techniques, system dynamics (SD) investigate on recognition of variables and their dynamic behavior trend throughout time in the SC. This paper presents a comprehensive SD model related to upstream steel SC management up to four echelons: concentrate, pellet, sponge iron, and steel. The model noticed Causal-Loops Diagrams (CLD) and Stock-Flow Diagrams (SFD) and provided a simulation framework. The robustness of the proposed model was evaluated by implementing the defined model in a multi-echelon steel complex in Iran. Various scenarios were analyzed applying stochastic simulation to include selected random variables. Iron ore grade and tonnage play the most critical role in the network’s performance. With an approximately 4% increase in the iron ore grade, steel production costs decreased 2.4%. The influence of the simultaneous uncertainty in iron ore grade and iron ore supply in the range of extreme levels of actual historical data resulted in an increase and decrease of +14% and −32% on the total steel production costs, respectively. Furthermore, removing energy subsidies and increasing five times in price results in a rise in total expenses up to 60% and a fall in the marginal profit up to 48%.

Convenience in a residence with demand response: A system dynamics simulation model

Demand Side Management (DSM) is a means to gain more control over energy demand to address some of the challenges of power grids. Demand Response (DR) is an approach to DSM that aims to influence the operation times of appliances; DR is often recommended for residences. Meanwhile, residents can undermine DR if it is not convenient. Therefore, there is need for tools to aid decision-making on the appropriate DR program for residences. Whilst models are used to explore DR programs, most do not measure, visualise or analyse the convenience of residents, although some models make assumptions about convenience. This paper explores convenience of a residence as timeliness by simulating four scenarios of DR programs in a single residence, using the System Dynamics (SD) methodology. In addition to delay in appliance-use that may result from DR, two indicators of convenience are proposed that consider preferences of the residence: Delay Duration Profile (DDP) and Delay Time Profile (DTP). When comparing convenience as delay, it was found that more hours of DR is better than less, earlier hours (from occupancy period) are better, and splitting or distributing DR hours during the day is better than being contiguous. Similar findings apply to DDP and DTP. Furthermore, it was found that DR leads to monetary savings and reduction in daily peak demand. This study represents the first attempt at a DR model from the bottom-up using SD, as well as using the model in decision-making analysis.

Socio-ecological systems modelling of coastal urban area under a changing climate – Case study for Ubatuba, Brazil

Understanding the complex dynamics between society and nature is a critical contribution of ecological modelling. Integrated views of human-nature relations as well as tools and frameworks for studying these relations are gaining ground. A socio-ecological systems (SES) perspective therefore embraces both social and environmental factors that uses nonlinearities, feedbacks, models, and multi-level networks for understanding and studying those phenomena. When undesired drivers as climate change are also taken into account, the most urgent question is how these critical socio–ecological systems will behave given the stresses they endure. This work had the objective of creating a new simulation of a coastal SES from Brazil that is able to integrate several climatic and social variables through a dynamic and coupled model, and forecast its behavior in the future according to scenarios. Specifically, a systems dynamics simulation model using MIMES (Multiscale Integrated Model of Ecosystem Services) was developed for Ubatuba, a coastal city highly dependent and influenced by tourism. Results showed good correspondence between the model and the data when testing several environmental inputs (wind speed and direction, cloud cover, sea surface temperature, precipitation patterns). The model simulated the population dynamics of 15 biological groups from 2010 to 2100 under different scenarios. Climate change will reduce most of populations in a range from −0.13% (± 0.0%) to −10.31% (± 0.0%). There are groups where the influence of climate change is not significant (Bivalve, Brachyuran, pelagic feeding fish and benthic feeding fish) with variations from 0 to 2% and others with moderate significance (Phytoplankton, Zooplankton, and Enterococcus) with variations >2%. Tourists reacting to water quality degradation is very relevant in Enterococcus population (with a reduction of 34%). Results show the urban activities strongly influencing the biological populations and that these impacts depend on the scenario context. This suggests a policy that limits the number of tourists and increases the water quality at the same time. Therefore, the model's spatial simulation of this complex socio-ecological system can be used to develop an integrative decision-making tool to help the city manage its natural capital and adapt to its changes.

The Public Health 12 framework: interpreting the \u2018Meadows 12 places to act in a system\u2019 for use in public health

BackgroundSystems science approaches have demonstrated effectiveness in identifying underlying drivers of complex problems and facilitating the emergence of potential interventions that are locally tailored, feasible, sustainable and evidence informed. Despite the potential usefulness of system dynamics simulation modelling and other systems science modelling techniques in guiding implementation, time and cost constraints have limited its ability to provide strong guidance on how to implement complex interventions in communities. Guidance is required to ensure systems interventions lead to impactful systems solutions, implemented utilising strategies from the intersecting fields of systems science and implementation science. To provide cost-effective guidance on how and where to implement in systems, we offer a translation of the ‘Meadows 12 places to act in a system’ (Meadows 12) into language useful for public health.MethodsThis translation of Meadows 12 was informed by our experience in working with 31 communities across two complex large scale randomised control trials and one large whole of community case study. These research projects utilised systems science and implementation science to co-create childhood obesity prevention interventions. The team undertaking this translation comprised research academics, implementation specialists and practitioners, practice-based researchers and a systems dynamicist. Our translation of each of the Meadows 12 levels to act in the system maintains the fidelity and nuance of the 12 distinct levels. We provide examples of each level of the Public Health 12 framework (PH12) drawn from 31 communities. All research was conducted in Victoria, Australia between 2016 and 2020.ResultsPH12 provides a framework to guide both research and practice in real world contexts to implement targeted system level interventions. PH12 can be used with existing implementation science theory to identify relevant strategies for implementation of these interventions to impact the system at each of the leverage points.ConclusionTo date little guidance for public health practitioners and researchers exists regarding how to implement systems change in community-led public health interventions. PH12 enables operationalisation Meadows 12 systems theory into public health interventions. PH12 can help research and practice determine where leverage can be applied in the system to optimise public health systems level interventions and identify gaps in existing efforts.Trial registrationWHO STOPS: ANZCTR: 12616000980437. RESPOND: ANZCTR: 12618001986268p.

Development of a System Dynamics Simulation for Assessing Manufacturing Systems Based on the Digital Twin Concept

Historically, manufacturing system researchers and managers have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to develop an integral modeling approach for manufacturing processes in order to assess their status and performance. For this purpose, a novel conceptual model consisting of an integral definition of areas and flows is applied. As a result, manufacturing systems can be modeled, considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, these models serve as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, a system dynamics simulation model is developed for a metallurgical process. The goal of the simulation model is to provide a digital manufacturing system regulated with different maintenance, quality, and production strategies in order to secure quality and delivery service. The results show how the monitoring of all flows together with the optimal strategies in the quality and maintenance areas as a result of a regulated system can enable companies to increase their profitability and customer service level. In conclusion, the applied simulation case study allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.

A holistic model for understanding the dynamics of outsourcing

Outsourcing is a complex process as many external and internal factors that look convincing in the first place might, however, lead to a failure in the long run. Motivated by this, we wanted to get a holistic understanding of such outsourcing decisions. Thus, we created a comprehensive System Dynamics simulation model including all relevant variables to examine the dynamic nature of outsourcing in a holistic manner and over time that consists of more than 200 interrelated variables. Our results show, amongst others, that higher process specialisation that requires substantial investments by the supplier appears to be favourable for an outsourcing company and shifting a larger quantity to such a supplier achieves better cost savings and thus accounts for a better overall outsourcing result. On an operational level, we identified an innovation trap, a bargaining power shift, a plagiarism trap, and a knowledge trap. Based on that, we give specific managerial recommendations to tackles these aspects. We conclude that, amongst others, it is important for innovative companies with rather complex processes and parts to carefully plan which and how many employees to release so as not to lose the knowledge on those outsourced processes and parts.

Analysis of national policies for Circular Economy transitions: Modelling and simulating the Brazilian industrial agreement for electrical and electronic equipment.

Public policies, incentives, and infrastructure are top-down instruments that can align stakeholders' roles and expectations for Circular Economy (CE) transitions, but it is crucial to analyse the possible effects of such instruments before implementation. This research investigates the Brazilian Industrial Agreement for Electrical and Electronic Equipment (BIAEEE) that governs the responsibilities and targets for nationwide collection and treatment of Waste from Electrical and Electronic Equipment (WEEE). A system dynamics simulation model is adapted for the use of smartphones in Brazil, and interventions focused on the collection of end-of-life products are examined against the BIAEEE targets. Twelve policy scenarios investigate three aspects of EEE collection: coverage increase, distribution of collection points and rewards. All scenarios show improvement in the EEE collection, but only one meets the BIAEEE targets. This research demonstrates how modelling and simulation can inform strategic decision-making in public policies for CE transitions.

Decision-making dynamic evolution among groups regarding express packaging waste recycling under different reference dependence and information policy.

The reveal of express packaging waste recycling behavior evolution trend is crucial to waste management. Current models are mostly based on the classic expected utility theory, without considering groups are susceptible to internal and external factors. To address this drawback, we construct an evolutionary game model of express packaging waste recycling behavior by considering information policy and reference dependence factors to explore groups' decision-making with different initial adoption rates. A system dynamics simulation model based on survey data is then built, and simulation experiments are also designed to reveal the impacts of key factors on the evolution path of recycling behavior. The results show that, without information policy, groups cyclically oscillate around the initial state. The stable trend depends on the information intensity, and the information effect is marginal diminishing. Groups with a lower initial adoption rate will evolve to an ideal stable strategy only when information intensity exceeds the threshold of ten. Reference points can change behavior strategies and are characterized by significant loss aversion. The benefits and costs affect groups' adoption or rejection behaviors. These findings can provide new ideas for related research and offer a reference for the government to formulate efficient waste management policies.

Design and Simulation of Manufacturing Organizations Based on a Novel Function-Based Concept

Historically, researchers and managers have often failed to consider organizations as a sum of functions leading to a set of capabilities that produce a product that can serve society’s needs. Furthermore, functions have increased with the development of industrial revolutions, however, many manufacturing organizations have not realized their full potential. As a result, many industrial organizations do not know why, where, and when the existing functions and projects for implementing new functions fail where tactical and strategic functions of a manufacturing organization are commonly over-seen. Thus, the aim of this research was to propose a holistic approach for manufacturing organizations in order to model their functions enabling the assessment, design, management, and control of operations and performance as well as to identify improvement potentials. For this purpose, a conceptual model was developed based on the evolution of functions along with the industrial revolutions. Moreover, using the conceptual model, manufacturing organizations can be modeled, considering common organizational functions in the respective areas of production, maintenance, and quality, etc., in the three planning horizons—strategic, tactical, and operative. As a result, the model serves as a basis for the integral management and control of manufacturing organizations. Moreover, it can be also used as a basis framework for a digital twin model for organizations. Thus, a system dynamics simulation model based on the conceptual model was developed for a generic organization. The goal of the simulation model is to provide an exemplary digital model of a manufacturing organization in which the different functions are applied with different methods, systems, and/or individuals along the development phases.

Contingencies of Violent Radicalization: The Terror Contagion Simulation

This paper builds confidence in the terror contagion hypothesis that violent radicalization leading to predatory mass violence operates as a system. Within this system, the contingent values of key root causes create channels within which violent ideologies and terrorism emerge. We built a system dynamics simulation model capable of replicating historical reference modes and sophisticated enough to test the contingent values of these propositions. Of 16 propositions, we identified six root-cause propositions that must simultaneously exist, act in concert and explain the dynamics of their interaction which generate a terror contagion. Other propositions can strengthen or weaken an existing contagion but not eliminate it. We use an experiment to demonstrate how changing the contingent values of these propositions creates downward channels. This experiment helps reconcile the swarm vs. fishermen debate over the true root causes of violent radicalization. Within these channels, the contingent values can favor swarm or fishermen manifestations. The simulation and experimentation results enable the future development of the terror contagion hypothesis, provide a testing environment for research on violent radicalization, and provide a pathway to policy development in the combating of terrorism that arises from violent radicalization.

Environmental effects of food waste reduction using system dynamics approach

ABSTRACT The present paper aims to study the energy consumption of the food industry and to evaluate the effects of reducing energy consumption on pollution emission in the future. The proposed integrated system dynamics simulation model focuses on the feedback processes and interaction between the population, economic growth, food demand, energy consumption, and air pollution. Diminishing food waste rate and developing energy productivity are considered to create energy management and pollution emission mitigation scenarios. The results revealed that the food demand and energy consumption will grow by 1.35% and 3.31% per year, respectively. Under these circumstances, the amount of pollution emission, with annual growth of 4%, reaches 1.13 million tons in 2031. The results of improving energy productivity and reducing of food waste under population growth also confirmed that these policies can be effective in energy management and pollution. Through a 1% reduction in the food waste scenario, we can save 0.95% on energy demand and control pollution emissions by 0.83%. Also, for every 1% improvement in energy efficiency, we can expect a 5.24% reduction in energy consumption and a 4.82% decline in pollution emissions. Thus, by controlling the energy demand in Iran, an effective step can be taken to improve the quality of the environment.

Understanding resilience of farming systems: Insights from system dynamics modelling for an arable farming system in the Netherlands

Farming systems in Europe are facing economic, social, environmental and institutional challenges. Highly intensive, climate-exposed, arable farming systems like the Veenkoloniën in the north of the Netherlands are particularly vulnerable to many of these challenges. Just in the past twenty years, the Veenkoloniën has lost half of its small and medium sized family farms specialised in cultivating starch potatoes. While starch potato production continues to be stable as the remaining farms are increasing the size of their operation, local stakeholders are concerned that the farming system in the Veenkoloniën is endangered. In this paper we investigate this issue by using a system dynamics simulation model to explore what the potential structures are that could threaten the long term future of starch potato production and to identify leverage points that can enhance the resilience of the system. Our analysis shows that, so far, farmers’ active engagement in a processing cooperative has been an important element to their resilience to cope with economic and environmental challenges. In practice, the cooperative has been able to act as a buffer and stabilise prices for farmers in the region by implementing strategies that increase the value of their products, open new markets and increase starch potato production.

A system dynamics-based framework for examining Circular Economy transitions

Decision-makers in the public policy and business arenas need tools to deal with multiple sources of complexity in Circular Economy (CE) transitions. System Dynamics (SD) facilitates coping with increased complexity by enabling closed-loop thinking via identifying the causal structures underlying behaviour and permitting to proactively experiment with the system through simulation. This research aims to propose and test an SD-based framework for examining CE transitions to supporting decision-making at the micro-, meso-, and macro-levels. Two inductive model-based cases studies led to formalising the framework, finally tested in a third deductive model-based case study. The framework is built upon the well-known stages for building SD simulation models and complemented with domain-specific activities, guiding questions, and expected outcomes when examining CE transitions. The SD-based framework is the first modelling-oriented prescriptive approach to help researchers and practitioners examining CE transitions on their journeys to understand and facilitate changes through SD simulation models.

Unintended Effects of Energy Efficiency Policy: Lessons Learned in the Residential Sector

The European Union has set an ambitious goal to tackle climate change, and energy efficiency in the residential sector is among the measures required to close the gap between targeted and actual greenhouse gas emissions. While different policy tools have been applied, the diffusion rate of these measures remains low. A system dynamics simulation model of the residential sector was developed to assess the advantages and drawbacks of energy efficiency policy in the multi-family building sector based on experience accumulated over the last twelve years in Latvia. The model was validated in expert group model building sessions and with historical trends. Simulating the model, a hypothesis was tested that supported the idea that seemingly positive policy tools set the stage for a series of unintended adverse effects due to the complex interactions between different system components. The common assumption that information and financial support should result in significant energy efficiency diffusion proved to be wrong. It instead results in unintended long-term consequences that hamper national energy efficiency goals. The model carried out an analysis and brought insights for improving the effectiveness of government energy efficiency policy. It is concluded that models that broadly describe complex systems are needed to identify effective policies and foresee unintended side effects.

Quantifying Multi-Parameter Dynamic Resilience for Complex Reservoir Systems Using Failure Simulations: Case Study of the Pirot Reservoir System

The objective of this research is to introduce a novel framework to quantify the risk of the reservoir system outside the design envelope, taking into account the risks related to flood-protection and hydro-energy generation under unfavourable reservoir element conditions (system element failures) and hazardous situations within the environment (flood event). To analyze water system behavior in adverse conditions, a system analysis approach is used, which is founded upon the system dynamics model with a causal loop. The capability of the system in performing the intended functionality can be quantified using the traditional static measures like reliability, resilience and vulnerability, or dynamic resilience. In this paper, a novel method for the assessment of a multi-parameter dynamic resilience is introduced. The multi-parameter dynamic resilience envelops the hydropower and flood-protection resilience, as two opposing demands in the reservoir operation regime. A case study of a Pirot reservoir, in the Republic of Serbia, is used. To estimate the multi -parameter dynamic resilience of the Pirot reservoir system, a hydrological model, and a system dynamic simulation model with an inner control loop, is developed. The inner control loop provides the relation between the hydropower generation and flood-protection. The hydrological model is calibrated and generated climate inputs are used to simulate the long-term flow sequences. The most severe flood event period is extracted to be used as the input for the system dynamics simulations. The system performance for five different scenarios with various multi failure events (e.g., generator failure, segment gate failure on the spillway, leakage from reservoir and water supply tunnel failure due to earthquake) are presented using the novel concept of the explicit modeling of the component failures through element functionality indicators. Based on the outputs from the system dynamics model, system performance is determined and, later, hydropower and flood protection resilience. Then, multi-parameter dynamic resilience of the Pirot reservoir system is estimated and compared with the traditional static measures (reliability). Discrepancy between the drop between multi-parameter resilience (from 0.851 to 0.935) and reliability (from 0.993 to 1) shows that static measure underestimates the risk to the water system. Thus, the results from this research show that multi-parameter dynamic resilience, as an indicator, can provide additional insight compared to the traditional static measures, leading to identification of the vulnerable elements of a complex reservoir system. Additionally, it is shown that the proposed explicit modeling of system components failure can be used to reflect the drop of the overall system functionality.

Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

Integrating decision maker preferences to a risk-averse multi-objective simulation-based optimization for a military workforce planning, asset management and fleet management problem

The complexities associated with the interconnections between workforce and fleet of assets to achieve organizational objectives often lead to uncertain and multi-objective problems. Further, different decision makers may have different preferences towards the degree of risk they tend to accept in their decisions. We contribute to the literature by developing a risk-averse multi-objective optimization model to determine the appropriate settings of recruitment, promotion and fleet transition strategies that simultaneously minimize the weighted sum of expected and Conditional-Value-at-Risk values of several objectives. In this study, we consider three objectives, namely, workforce cost (WC), capability gap (CG) (defined as the unavailability of assets due to workforce shortages to crew assets) and capital and sustainment cost (CSC) of assets. To solve this problem, we develop a simulation–optimization method integrated with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a hybrid solution approach. The simulation–optimization method combines Non-dominated Sorting Genetic Algorithm (NSGA) III and a System Dynamics (SD) simulation model. In an iterative process, NSGA-III generates the recruitment, promotion and fleet transition strategies and the SD simulation model evaluates the fitness of the generated strategies by calculating the values of three objectives. The output of the simulation–optimization method is a set of Pareto optimal solutions and the corresponding Pareto fronts. We use TOPSIS to rank the set of Pareto front points and determine the best point. We apply the proposed model and solution approach on a joint problem of workforce planning, fleet renewal and asset management inspired by the Australian Navy. The results provide valuable information for military decision makers regarding the trade-offs between various alternatives based on their preferences.

Causal effects of population growth on energy utilization and environmental pollution: A system dynamics approach

Population growth will change the demand for food and energy resources and environmental pollution. Although early energy resources modeling has made vital efforts to model the energy system in the world, because of increasing complexity and integration of environmental, social, and economic functions, these models still need to be developed to show a system close to the real world to enhance sustainable management of natural resources. Hence, the main objective of this study is to design a system dynamics model for the food production system and energy demand in Iran in order to evaluate the effects of different population scenarios on key variables. In this regard, an integrated system dynamics simulation model was developed in Iran where managing energy resources is seriously challenging due to population growth and increasing food demand. The results of the behavioral test showed that the designed model can be used to investigate and simulate the effects of different population growth rate scenarios. Findings illustrated that by increasing population, if no further energy demand management policies were implemented, the total food demand and energy use increase by more than 1.35% and 3.31% respectively. Also, the annual air pollution change during 2014-2030 is expected to be around 4.41%. By changing the population growth rate in the form of population scenarios, the average annual energy demand in the first population scenario will be 20,277 barrels of crude oil and in the second population scenario will be 20049 barrels of crude oil. It seems that the change in the population growth rate will lead to an increase of 3.23% and 2.16% in average annual energy demand, respectively. The results showed that in the first population scenario, with a further increase in population variables, food demand and energy demand, the average change in pollution emission is 4.79%, which is at a higher level than the baseline conditions. In the second population scenario, changes in environmental pollution will be reduced to 4.31%. Therefore, given the effectiveness of population growth on the behavior of the energy system and pollution, the adoption of energy management policies should be considered by policy makers.

Multi-Scenario Simulation of a Water–Energy Coupling System Based on System Dynamics: A Case Study of Ningbo City

In this work, based on the concept of collaborative water–energy development, a multi-scenario system dynamics simulation model of a water–energy coupling system was constructed by using the system dynamics modeling method. The model was composed of four subsystems: society, economic, water resources, and energy. Taking Ningbo City as the research location to run the simulation model, the analysis of the validity of the model showed that the relative error between the historical data and the simulation results of the model was less than 10%, which proved that the model passed the test. In this paper, based on the scenario of business an usual (BAU), three scenarios of water-saving scenario (WSS), energy-saving scenario (ESS), and comprehensive savings (CS, the comprehensive scenario considers water-saving and energy-saving together) were designed, and the simulation indexes in the three scenarios were refined in order to strengthen the control of water-saving policies, improve the effective use of water, optimize the industrial energy structure, improve the level of energy-saving-related technologies, and advance the urbanization process. The simulation results for Ningbo City from 2010 to 2030 show that the water–energy coupling system is affected by many factors, and the adjustment of a driving factor of any subsystem will have an impact on the water–energy coupling system. There are two driving factors: the first is a constant variable related to water resources, energy, society, and economic, and the second is a variable affected by time. The coupling system is based on the law of real development and is composed of causal and functional relationships between variables. Therefore, within the prediction range of 2030, the driving factors in the coupling system are controllable, and there is no uncontrollable situation. The strengthening of water-saving policies and the improvement of the coefficient of the effective utilization of water will have the optimal saving effects on water resources and energy at both the single and the coupling level; this also demonstrates that the water resource management in Ningbo City plays an extremely important role in the relationship of the water–energy coupling. The results of this study are expected to provide a valuable reference for the management and conservation of water–energy coupling in Ningbo City.

Dynamic Analysis of a Closed Brayton Cycle Using Supercritical Carbon Dioxide

It is important to study the dynamic characteristics of closed Brayton cycle using supercritical carbon dioxide(S-CO2) for the research of the automatic control method of the system operation. The dynamics characteristics of a split-flow recompression closed Brayton power conversion system with supercritical carbon-dioxide as the working-fluid was investigated. The mathematics model about the compressor, turbine, recuperator in a S-CO2 Brayton cycle system was studied on the basis of the conservation principle and the basic equations of engineering thermodynamics, heat transfer theory and hydrodynamics. A complete system dynamic simulation model was built to study the dynamic response characteristics of the system under different disturbances by using the programming language in the computer. The results show that: the heating power of the heater is increased by 15.4%, the operating parameters are improved, the cycle efficiency is increased by 7.8%, the power of the pre-cooler is reduced by 50%, the cycle efficiency is reduced by 1.8%, and the opening of the valve is reduced by 10%, and the cycle efficiency is improved.