Dynamic Modeling Tool Research Articles

Transportation Demand Forecasting System using System Dynamics

The transportation system has a certain capacity. The needs of the transportation system need to be predicted. The predictions made are the basis for providers to prepare existing transportation facilities. Through this paper, we propose a system to predict the capacity requirements of the transportation system using a dynamic systems approach. We use the Stella application as a dynamic system modeling tool. It was found that using the dynamic systems approach, the relationship between variables that form complexity can be modelled.

Tiny house, tiny footprint? The potential for tiny houses to reduce residential greenhouse gas emissions

While considerable improvements to the energy efficiency of housing have been achieved over recent decades, the residential sector still represents a significant and increasing proportion of global greenhouse gas emissions. This is exacerbated by an increasing global population and living standards, demand for larger houses, and smaller household size. Tiny houses have emerged as a potential solution to this issue. While research exists on the environmental benefits of smaller housing, there is little on that of tiny houses. This study quantifies the life cycle GHG emissions of a tiny house, and their potential to reduce residential GHG emissions. A hybrid analysis and a dynamic energy modelling tool were used to quantify embodied and operational GHG emissions, respectively, for a tiny house located in Australia. The study shows that a tiny house may result in a 70% reduction in per capita GHG emissions over its life compared to a traditional Australian house. This indicates the potential of tiny houses to be a useful option for reducing GHG emissions in the building sector.

A systems approach toward climate resilient livelihoods: A case study in Thai Nguyen province, Vietnam

This study aims to identify strategic actions towards climate resilient livelihoods and secure income for smallholder farmers in Thai Nguyen province of Vietnam using a systems approach and system dynamic modelling tools. Information and data for this research was collected through surveys, interviews, focus group discussions and workshops with relevant stakeholders and 187 farmers in two vulnerable districts during October 2019–April 2020. Findings of this study uncovered a number of shortcomings of the government policies and approaches in climate change adaptation. Local initiatives, community learning and ownership seem to be neglected. This research has substantiated the effectiveness and validity of systems approaches and tools in structuring and solving complex issues in agricultural research and development under the interwoven relationships between environmental and human factors. Climate resilient production models and practices are just part of the systemic interventions that need to be implemented in a coordinated manner towards a more resilient future of the farming communities. This study has addressed the current knowledge gap and the need for using integrated approaches and decision support systems for unravelling ill-structured and/or complex issues of climate change adaptation (CCA). It also provided practical recommendations for informed CCA policies and implementation.

A Novel Contribution for Resilient Buildings. Theoretical Fragility Curves: Interaction between Energy and Structural Behavior for Reinforced Concrete Buildings

The paper introduces a new semi-probabilistic methodology for the definition of energy fragility curves suitable for a macro-classification of building stock inspired to and coupled with the widely adopted method of seismic fragility curves. The approach is applied to the reinforced concrete residential buildings of the Italian stock. Starting from a classification according to the climatic zone and the construction period, some reference buildings in terms of building envelope typologies have been defined and simulated by means of dynamic modeling tools. Then, cumulative distributions of the probability that the primary energy consumption for heating was comparable with certain threshold values are defined according to the climatic conditions expressed with the heating degree days, which constitute the intensity measure for the fragility curves. Finally, by focusing on the interaction points between structural and energetic aspects, it is shown how these curves can be useful for decision-makers with regards to definition of importance and or the level of intervention to be made to the building envelope for improving its seismic safety and the energy quality. Indeed, non-integrated interventions are more expensive and less efficient.

Multiobjective stochastic programming with recourses for real-time flood water conservation of a multireservoir system under uncertain forecasts

Flood water conservation realized through real-time multireservoir operations is effective in mitigating water scarcity. Owing to the influence of real-time inflow forecast uncertainty, determining an informed operation plan necessitates resolution of the conflict between upstream flood risk, downstream flood risk, and water scarcity risk. This study developed a multiobjective stochastic programming with recourses (MOSP) model to seek robust risk-averse plans under multiple risks. In the proposed approach, inflow forecast errors are modeled and sampled as spatially and temporally correlated stochastic processes using a copula function. The multiobjective stochastic programming is solved using the epsilon constraint method based on explicit formulation of multiple risk objectives with discretized inflow scenarios, and a rolling horizon with recourses for real-time operational updating is modeled to address the dynamic decision-making characteristics. The proposed methodologies were applied to the multireservoir system of the Pi River Basin (China), and comparisons with model results of a traditional coupled simulation and a deterministic single objective optimization model were conducted. Results indicated that capturing spatial and temporal dependencies of forecast uncertainty provides informative forecast support. Moreover, noninferior solutions obtained from MOSP dominate the deterministic optimal solution, which could either conserve 4.86 million m3 (12%) more water without increase in flood risk, or reduce the upstream flood risk by 3.1% without increase in either water scarcity or downstream flood risk during a typical receding flood. The proposed methodology provides a dynamic decision-making modeling tool that could reduce overall risk and determine a compromise for flood water conservation under uncertainty.

Enabling large-scale dynamic simulations and reducing model complexity of district heating and cooling systems by aggregation

District heating and cooling (DHC) systems are considered cornerstones of a future heat and cold supply due to their ability to integrate renewable and waste heat sources as well as long and short-term storage technologies and their flexibility for integration of other infrastructures. Therefore, DHC systems may represent the central hub of an interlinked overall energy system. These options nevertheless lead to a more complex system if implemented as the number of technical components and potential interactions increase and as the energy demands on the network grow. The transportation of the heat supplied to the consumers through water flow involves both time and temperature-dependent changes based on the mass flow rates and the heat losses to the surrounding from the pipes. The dynamic properties of the consumers and the distribution pipes strongly influence the operation of the district heating systems. Dynamic modeling tools are required to cope with the increasing complexities and to optimize the operation of the DHC systems by using the knowledge of time delay in the heat transport, temperature distribution and flow characteristics. Nevertheless, it is computationally challenging to simulate large-scale DHC networks dynamically. One proposed method for improvement in this context is aggregation, which simplifies the topological complexities of the original network by reducing the number of pipe junctions (branches) and pipes. This paper focuses on the analysis and evaluation of the application of two aggregation methods, the Danish and the German method, in a dynamic modelling environment and comparing the aggregation to the monitoring data of a virtual district heating network with 146 consumers and an existing district heating network with 66 consumers. These evaluations are based on comparison of the simulation results of the aggregated network with fewer consumers than the original network in terms of accuracy, information loss and computational time. The results show that the CPU time in the simulation of this network can speed up approximately more than 95% by aggregating the network down to 3 consumers without significant information loss. We will highlight the current potentials and limitations of both aggregation methods in terms of integrating them in the planning, modelling and operation of DHC systems, including the 4th generation district heating systems.

Numerical investigation of response-conditioning wave techniques for short-term rare combined loading scenarios

Response-conditioning wave techniques are a rational way to link wave excitation environments with return-period extreme loading responses. By retaining the wave excitation which leads to a design response, these techniques can also define extreme combined loading scenarios. For complex or novel hull forms, combined loading may be relevant for evaluating structural reliability or adequacy. But using combined loading scenarios as inputs to high-fidelity structural or dynamic modeling tools implies that such load scenarios are realistic for the defined return-period. This paper investigates three response-conditioning wave techniques: a modified Equivalent Design Wave method, a modified Conditioned Random Response Wave method, and the Design Loads Generator, to evaluate how well they reproduce combined loading statistics for a 1000-hr return-period as compared to stochastic brute-force simulations. The investigation is carried out for extreme combined loading scenarios on a 110 m trimaran hull. The Design Loads Generator produces the most realistic extreme combined loading statistics as compared to the brute-force approach with a significant reduction in computation time based on combined load conditional probability density functions, cumulative density functions, and individual stochastic load vectors.

A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions

ObjectivesPublic health officials need tools to assist in anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We constructed a modeling tool to aid active public health officials to estimate healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impact of population-wide social-distancing interventions. MethodsThe tool uses an SEIR compartmental model to project the pandemic’s local spread. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention, and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile. ResultsOur scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at six to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals >70 years, and telework interventions may keep treatment demand below capacity. ConclusionsAggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.

An Analytical and Modular Software Workbench for Solving Kinematics and Dynamics of Series-Parallel Hybrid Robots

Abstract Parallel mechanisms are increasingly being used as modular subsystem units in various robots and man-machine interfaces for their superior stiffness, payload-to-weight ratio, and dynamic properties. This leads to series-parallel hybrid robotic systems that are challenging to model and control due to the presence of various closed loops. Most model-based kinematic and dynamic modeling tools resolve loop closure constraints numerically and hence suffer from inefficiency and accuracy issues. Additionally, they do not exploit the modularity in robot design. In this paper, we present a modular and analytical approach toward kinematic and dynamic modeling of series-parallel hybrid robots. This approach has been implemented in a software framework called hybrid robot dynamics (hyrodyn) and its application is demonstrated with the help of a series-parallel hybrid humanoid robot recently developed at DFKI-RIC.

Flexible Fitting of Biomolecular Structures to Atomic Force Microscopy Images via Biased Molecular Simulations.

High-speed (HS) atomic force microscopy (AFM) is a prominent imaging technology that observes large-scale structural dynamics of biomolecules near the physiological condition, but the AFM data are limited to the surface shape of specimens. Rigid-body fitting methods were developed to obtain molecular structures that fit to an AFM image, without accounting for conformational changes. Here, we developed a method to fit flexibly a three-dimensional (3D) biomolecular structure into an AFM image. First, we describe a method to produce a pseudo-AFM image from a given 3D structure in a differentiable form. Then, using a correlation function between the experimental AFM image and the computational pseudo-AFM image, we developed a flexible fitting molecular dynamics (MD) simulation method by which we obtain protein structures that well fit to the given AFM image. We first test it with a twin experiment; using an AFM image produced from a protein structure different from its native conformation as a reference, we performed the flexible fitting MD simulations to sample conformations that fit well the reference AFM image, and the method was confirmed to work well. Then, parameter dependence in the protocol was discussed. Finally, we applied the method to a real experimental HS-AFM image for a flagellar protein FlhA, demonstrating its applicability. We also test the rigid-body fitting of a molecular structure to an AFM image. Our method will be a general tool for dynamic structure modeling based on HS-AFM images and is publicly available through the CafeMol software.

Learning at the Intersection of Self and Society: The Family Geobiography as a Context for Data Science Education

Open large-scale datasets (LSDS) and data visualization technologies are new cultural tools that have potential to inform public dialogue and learning about important socioeconomic and scientific matters, particularly if the data is used to consider both personal and shared experiences. This paper reports on a design study in which diverse middle and high school youth in a free summer workshop at an urban public library were asked to model their family geobiographies, or their personal family migration stories, with socioeconomic LSDS. Youth represented family decision-making and social conditions that might have motivated family movements with online, dynamic data modeling and mapping tools (Gapminder.org; SocialExplorer.com). The qualitative video analysis examined participants’ experiences and learning in storytelling and modeling the family geobiography, focusing on multimodal talk-in-interaction to understand how the study design engaged learners’ capacities, histories, and imagined futures in relation to the LSDS and supported learning about oneself and society. Grounded examples from two family cases are used to illustrate how participants placed their families and selves into data ontologies and the role of family members in composing storylines.

A modelling tool for engine and exhaust aftertreatment performance analysis in altitude operation

Abstract New regulation standards on engine pollutant emissions are widening the engine operating conditions subjected to type approval tests as a way to prevent from the gap between regulated and real-driving emissions. In this regard, ambient temperature and driving altitude are new boundaries to be considered. Although the basis of the impact of these variables has been studied concerning the engine performance, new challenges appear to meet the emission limits and the aftertreatment conversion efficiency. In this work, a gas dynamic modelling tool is approached to explore the maximisation of the engine torque when operating at high altitude in a wide range of ambient temperatures. Particular focus is put on the modelling of the combustion, the turbocharger and the exhaust aftertreatment system. Starting from a sea-level calibration, the proposed methodology accounts for mechanical criteria as well as the impact on the engine raw emissions and exhaust flow properties to define new combustion settings for altitude operation. Next, these boundaries are applied to the exhaust aftertreatment system to analyse the impact on the catalyst conversion efficiency and the particulate filter performance concerning pressure drop and filtration efficiency.

Lightweight physics-based models for the control of fluid-mediated self-assembly of robotic modules

Self-assembling robotic systems form a subclass of distributed robotic systems that undertake the fundamental task of structure formation. These systems build desired target structures by putting their constituting robotic modules together in a distributed and stochastic fashion, i.e., through a self-assembly process. The use of self-assembly as the underpinning coordination mechanism provides powerful means for structure formation across a variety of length scales as well as media. In particular, fluidic media have been shown to be very efficient enablers for small-scale self-assembly. In this paper, we consider a distributed robotic system consisting of multiple miniature robotic modules performing self-assembly in 2D, at the water–air interface. The course of the assembly process in the system culminating in a predefined target structure is shaped by the ruleset controllers programmed on the individual robotic modules, allowing only certain formations and ruling out others throughout the process. Designing control strategies relies heavily on accurate models of the system dynamics. Faithfully modeling such systems and their inter-module interactions involves capturing the hydrodynamic forces acting on the modules using typically computationally expensive fluid dynamic modeling tools. Such computational cost restricts the usability of the resulting models, particularly for the purpose of designing optimized controllers. In this paper, we present a new modeling approach and proceed by employing the resulting model for optimizing ruleset controllers. First, we show how the hardware and firmware of the robotic platform can be faithfully modeled in a high-fidelity robotic simulator. Second, we develop a physics plugin to recreate the hydrodynamic forces acting on the modules and propose a trajectory-based method for calibrating the plugin model parameters. Finally, we employ the resulting model and obtain automatically optimized ruleset controllers for given target structures.

Augmenting Water Supplies Through Urban Wastewater Recycling (March 2019)

Projections indicate a high-risk water stress level (+80%) in the Western Cape province of South Africa by 2040. The reassessment of water management is paramount in ensuring water security for the generations to come. Water management approach, which recognizes the value of alternative water sources such as Urban Wastewater recycling (UWWR) amid shrinking surface water supplies is of critical importance. This management process mimics natural systems, placing the focus on stakeholder engagement and meaningful public participation. The objective is to ensure a vibrant economy, social equity, and protection of water resources for current and future generations. This paper's main goal is the assessment of the impact of UWWR on water availability in the Stellenbosch urban water system. Vensim PLE 7.2 systems dynamic modeling tools constructed the qualitative and quantitative systems dynamics models. The results from the simulation indicated that UWWR in Stellenbosch has a significant potential of augmenting water supplies over a period of ten years. Of the three scenarios investigated, the 50% reuse scenario projected the best option for augmenting water supplies. In addition, results extrapolated from availability index graphs, depicted a water availability index, which is greater than 1 for every UWWR scenario investigated, i.e., 10%, 30%, and 50%, a positive result which supports the decision for UWWR in Stellenbosch as a water augmentation option.

Demonstration of the Advanced Dynamic System Modeling Tool TRANSFORM in a Molten Salt Reactor Application via a Model of the Molten Salt Demonstration Reactor

Liquid-fueled nuclear reactors, particularly molten salt reactors (MSRs), have recently gained significant interest in the advanced reactor community. As with all reactors, modeling and simulation are critical to advanced reactor design and licensing and will be required for MSR deployment. However, there are significant gaps in existing simulation capabilities for MSRs, particularly with the unique challenges of liquid-fueled systems (e.g., fission product transport). Furthermore, advanced reactor designers require near-term tools that are readily modifiable to perform design and analysis, including the ability to extend their analysis beyond the primary system to auxiliary systems. Transient Simulation Framework of Reconfigurable Models (TRANSFORM), a Modelica-based, system modeling library developed at Oak Ridge National Laboratory, is an advanced tool that can help meet some of the near-term needs of the advanced reactor community. This paper describes advanced system modeling criteria and presents TRANSFORM to the advanced reactor community by demonstration of system modeling capabilities and support of advanced analysis workflows, i.e., the Risk Analysis Virtual Environment (RAVEN) framework from Idaho National Laboratory, using the liquid-fueled Molten Salt Demonstration Reactor (MSDR) as a reference design.

Stability Analysis of Grid-Connected Photovoltaic Systems with Dynamic Phasor Model

The typical layout of power systems is experiencing significant change, due to the high penetration of renewable energy sources (RESs). The ongoing evaluation of power systems is expecting more detailed and accurate mathematical modeling approaches for RESs which are dominated by power electronics. Although modeling techniques based on state–space averaging (SSA) have traditionally been used to mathematically represent the dynamics of power systems, the performance of such a model-based system degrades under high switching frequency. The multi-frequency averaging (MFA)-based higher-index dynamic phasor modeling tool is proposed in this paper, which is entirely new and can provide better estimations of dynamics. Dynamic stability analysis is presented in this paper for the MFA-based higher-index dynamical model of single-stage single-phase (SSSP) grid-connected photovoltaic (PV) systems under different switching frequencies.

Optimization of the cooling capacity of the cryo-magnetic system for EU DEMO at the pre-conceptual design phase

The cryo-magnetic system of the future tokamak EU DEMO includes the superconducting magnets cooled by a forced flow of supercritical helium at about 4.5 K, the cryo-distribution lines and valve boxes, and the cryogenic system with several cold boxes. The DEMO cryogenic system is at the pre-conceptional design phase, with the identification of the heat loads requirements of the main cryogenic users. The analysis will focus on the cooling capacity of the superconducting magnets, with the application on a toroidal field coil conductor based on a CEA design. Simcryogenics, a dynamic modelling tool developed by CEA, has been used to model a supercritical helium loop for cooling one conductor pancake. An algorithm has been developed to optimize the cold source temperature and the supercritical helium mass flow, in order to minimize the refrigeration power. At this pre-conceptional design phase, the interest of such analysis is to contribute to the optimization of both the cryogenic system and the superconducting magnets to minimize the refrigeration power, one of the important investment and operation costs for the future tokamak.

Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit

A method of fast multi-objective optimization and decision-making support for building retrofit planning is developed, and lifecycle cost analysis method taking into account of future climate condition is used in evaluating the retrofit performance. In order to resolve the optimization problem in a fast manner with recourse to non-dominate sorting differential evolution algorithm, the simplified hourly dynamic simulation modeling tool SimBldPy is used as the simulator for objective function evaluation. Moreover, the generated non-dominated solutions are treated and rendered by a layered scheme using agglomerative hierarchical clustering technique to make it more intuitive and sense making during the decision-making process as well as to be better presented.The suggested optimization method is implemented to the retrofit planning of a campus building in UPenn with various energy conservation measures (ECM) and costs, and more than one thousand Pareto fronts are obtained and being analyzed according to the proposed decision-making framework. Twenty ECM combinations are eventually selected from all generated Pareto fronts. It is manifested that the developed decision-making support scheme shows robustness in dealing with retrofit optimization problem and is able to provide support for brainstorming and enumerating various possibilities during the decision-making process.

VOLTAGE SAG ENHANCEMENT IN MICROGRID WITH APPLICATIONS ON SENSITIVE LOADS

Although one of the main problems of the power quality is voltage sag in nuclear installations,the terminology used for the compensation devices is different. A Thyristor switched capacitorcontrol is one of the equipment for voltage disturbance mitigation. This paper suggests aThyristor Switched Capacitor control for mitigation of voltage sag in nuclear research reactor as asensitive load. Performance of the proposed control investigated under different types of voltagesag magnitudes and is modeled using the user-defined dynamic modeling (UDM) tools in ETAP.The results show appropriate operation of the proposed control system to examine the developedtechnique.

Low Fatigue Dynamic Auxetic Lattices With 3D Printable, Multistable, and Tuneable Unit Cells

Stress distribution has led to the design of both tough and lightweight materials. Truss structures distribute stress well and are commonly used to design lightweight materials for applications experiencing low strains. In 3D lattices, however, few structures allow high elastic compression and tunable deformation. This is particularly true for auxetic material designs, such as the prototypical re-entrant honeycomb with sharp corners, which are particularly susceptible to stress concentrations. There is a pressing need for lightweight lattice designs that are dynamic, as well as resistant to fatigue. Truss designs based on hinged structures exist in nature and delocalize stress rather than concentrating it in small areas. They have inspired us to develop s-hinge shaped elastic unit cell elements from which new class of architected modular 2D and 3D lattices can be printed or assembled. These lattices feature locally tunable Poisson ratios (auxetic), large elastic deformations without fatigue, as well as mechanical switching between multistable states. We demonstrate 3D printed structures with stress delocalization that enables macroscopic 30% cyclable elastic strains, far exceeding those intrinsic to the materials that constitute them (6%). We also present a simple semi-analytical model of the deformations which is able to predict the mechanical properties of the structures within < 5% error of experimental measurements from a few parameters such as dimensions and material properties. Using this model, we discovered and experimentally verified a critical angle of the s-hinge enabling bistable transformations between auxetic and normal materials. The dynamic modeling tools developed here could be used for complex 3D designs from any 3D printable material (metals, ceramics, and polymers). Locally tunable deformation and much higher elastic strains than the parent material would enable the next generation of compact, foldable and expandable structures. Mixing unit cells with different hinge angles, we designed gradient Poisson’s ratio materials, as well as ones with multiple stable states where elastic energy can be stored in latching structures, offering prospects for multi-functional designs. Much like the energy efficient Venus flytrap, such structures can store elastic energy and release it on demand when appropriate stimuli are present.