Flexible Modeling Environment Research Articles

Assessing the status of leopard in the Cape Fold Mountains using a Bayesian spatial capture–recapture model in Just Another Gibbs Sampler

AbstractLarge carnivores are in decline globally. The leopard's (Panthera pardus) adaptability enabled its survival as the last remaining apex predator in the Western Cape, South Africa. Limited suitable habitat and anthropogenic activity imperil the continued survival of leopards, yet density estimates are lacking in the Western Cape, especially across unprotected areas. We employ the flexible modelling environment of Just Another Gibbs Sampler (JAGS) to implement a Bayesian spatial capture–recapture (SCR) model and generate the first density estimate for the leopard population in the Boland Mountain Complex using a dataset collected in 2010–2011. Leopard density was estimated at 1.69/100 km2 (95% CI = 1.4–1.99) with adult female leopards occurring at a higher density (0.93/100 km2 [95% CI = 0.64–1.18]) than males (0.76/100 km2 [95% CI = 0.62–0.90]). Our modelling shows that males have more extensive ranges than females, increasing their susceptibility to anthropogenic threats, which are generally more abundant at the study area's periphery. Tailored conservation efforts are recommended in conjunction with an up‐to‐date leopard population density reassessment. The JAGS approach to SCR also enabled a detailed investigation of animal distribution and movement, and provides a reliable methodology to monitor population trends.

An object-oriented method for fully coupled analysis of floating offshore wind turbines through mapping of aerodynamic coefficients

This work presents a novel object-oriented approach to model the fully-coupled dynamic response of floating offshore wind turbines (FOWTs). The key features offered by the method are the following: 1) its structure naturally allows for easy implementation of arbitrary platform geometries and platform/rotor configurations, 2) the analysis time is significantly faster than that of standard codes and results are accurate in situations where rotor dynamic contribution is negligible, and 3) an extremely flexible modeling environment is offered by the object-oriented nature of Modelica. Moreover, the current modeling facility used for the code development is open source and is naturally suitable for code sharing. In the present method, the aerodynamic model computes the aerodynamic loads through the mapping of steady-state aerodynamic coefficients. This modeling approach can be placed at the intersection between simplified aerodynamic methods, such as TDHMill, and full beam element/momentum-based aerodynamic methods. Aerodynamic loads obtained from the coefficients mapping are composed of a concentrated thrust and a concentrated torque. The thrust acts at the hub, while the torque is applied at the rotor low-speed shaft of a simplified rigid rotor equation of motion (EoM) used to emulate the rotor response. The aerodynamic coefficients are computed in FAST for a baseline 5 MW wind turbine. A standard rotor-collective blade-pitch control model is implemented. The system is assumed to be rigid. Linear hydrodynamics is employed to compute hydrodynamic loads. The industry-standard numerical-panel code Sesam-Wadam (DNV-GL) is used to preprocess the frequency-domain hydrodynamic problem. Validation of the code considers a standard spar-buoy platform, based on the Offshore Code Comparison Collaboration (OC3-Hywind). The dynamic response is tested in terms of free-decay response, Response Amplitude Operator (RAO), and the time histories and power spectral densities (PSDs) of several load cases including irregular waves and turbulent wind. The resulting model is benchmarked against well-known code-to-code comparisons and a good agreement is obtained.

Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires.

Large wildfires (>50,000ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State. Across varied topography, vegetation, and distinct fire progressions, we used a combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. Simultaneous autoregression improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. Random forest modeling provides a flexible modeling environment capable of capturing complex interactions and nonlinearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high-severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin-only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pronounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind-driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events.

Agent-oriented simulation framework for handling disruptions in chemical supply chains

To cope with increasing vulnerability, global business especially chemical manufacturing companies need to actively manage (the risk of) disruptive events in their supply chains. This calls for systematic frameworks to guide their efforts. Further, due to the complexity of today's global supply chains, decision making tools are needed to provide support in different stages of the supply chain disruption management process. This paper presents an agent-oriented simulation framework for disruption management in supply chains. This simulation framework provides a flexible modelling and simulation environment for decision makers to experiment with different types of disruptions and disruption management strategies. The application of the simulation model to support decision-making in different steps of the pre- and post-disruption management processes is illustrated using a lube oil supply chain case study.

A flexible multi-disciplinary environment for performance, life-cycle cost, and safety evaluation of suborbital vehicles

Suborbital vehicles are innovative and unconventional aerospace concepts that are characterized by a high level of complexity and a lack of optimized baseline. The present focuses on the development of a flexible multi-objective modeling and simulation environment that provides the capabilities to rapidly evaluate the flying, economic, and safety performance of suborbital vehicles at a conceptual design level. One of the goals of this environment is to enable the exploration of large design spaces and facilitate the mapping between high-level requirements and the identified optimized concept. The environment is broken down into six modules: weight/size, aerodynamics, trajectory, propulsion, economics, and safety. By leveraging empirical models, physics-based approaches, and surrogate modeling techniques, it enables the rapid and parametric assessment and optimization of a multitude of design concepts. It is the first environment of this sort to support informed design space exploration of suborbital vehicles and allow for new trends to be identified and crucial observations to be made.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity.

Holistic Diagnosis of Pipeline System with Impedance Method

In this study, a diagnosis potential of impedance method implementing several unsteady friction models for a pipeline system with or without leakage. The development based on impedance approach provides a feasible and flexible modelling environment that can address not only for various fundamental system properties, but also for dynamic variables in transient generation. Calibration parameters cover conventional diagnostic parameters such as friction parameters and leakage, steady and transient condition parameters such as the initial flow rate and valve closure duration, and system characteristics such as pipeline length and wave propagation speed. Based on the perspective of the impedance for system definition, corresponding response functions in the frequency domain approach were derived. Time domain responses of pipeline systems were integrated into the genetic algorithm for inverse transient analysis. Unsteady models addressed both frequency-dependent friction and acceleration-based frictions. Parameter calibrations with experimental data have shown that the impedance-based approach provides a versatile and feasible optimization structure via the objective fitting function of the pressure head variation. The performance of the integrated optimization examples demonstrates the robustness of the impedance-based approach for a representation of a real-life system, a diagnosis of system abnormality and a configuration of system characteristics.

Modelling methodology for the simulation of the manufacturing systems

Facing an increasingly competitive environment, companies must continually improve the performance of their production systems to respond to consumer demand which is increasingly unpredictable, unstable and with competitive prices. This article is intended as a contribution to finding a solution to an emerging problem in the management of manufacturing flows in recent years where product diversity, shortened lead times and strong competition make the aspect of the 'flow' of goods from supplier to end customer a central one. In this perspective, the aim of this paper is to develop a flexible modelling environment for the simulation and analysis of production systems. This environment enables the decomposition of the production system, by offering generic and modular concepts for modelling the physical processes as well as the control processes to simulate the manufacturing processes as a whole. These concepts are specified and modelled using an object oriented approach such as the unified modelling language (UML).

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

ELECANS—an integrated model development environment for multiscale cancer systems biology

Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub_0204.php. Supplementary data are available at Bioinformatics online.

An integrated modelling framework for regulated river systems

Management of regulated water systems has become increasingly complex due to rapid socio-economic growth and environmental changes in river basins over recent decades. This paper introduces the Source Integrated Modelling System (IMS), and describes the individual modelling components and how they are integrated within it. It also describes the methods employed for tracking and assessment of uncertainties, as well as presenting outcomes of two case study applications.Traditionally, the mathematical tools for water resources planning and management were generally designed for sectoral applications with, for example, groundwater being modelled separately from surface water. With the increasing complexity of water resources management in the 21st century those tools are becoming outmoded. Water management organisations are increasingly looking for new generation tools that allow integration across domains to assist their decision making processes for short-term operations and long-term planning; not only to meet current needs, but those of the future as well.In response to the need for an integrated tool in the water industry in Australia, the eWater Cooperative Research Centre (CRC) has developed a new generation software package called the Source IMS. The Source IMS is an integrated modelling environment containing algorithms and approaches that allow defensible predictions of water flow and constituents from catchment sources to river outlets at the sea. It is designed and developed to provide a transparent, robust and repeatable approach to underpin a wide range of water planning and management purposes. It can be used to develop water sharing plans and underpin daily river operations, as well as be used for assessments on water quantity and quality due to changes in: i) land-use and climate; ii) demands (irrigation, urban, ecological); iii) infrastructure, such as weirs and reservoirs; iv) management rules that might be associated with these; and v) the impacts of all of the above on various ecological indices. The Source IMS integrates the existing knowledge and modelling capabilities used by different state and federal water agencies across Australia and has additional functionality required for the river system models that will underpin the next round of water sharing plans in the country. It is built in a flexible modelling environment to allow stakeholders to incorporate new scientific knowledge and modelling methods as they evolve, and is designed as a generic tool suitable for use across different jurisdictions. Due to its structure, the platform can be extended/customised for use in other countries and basins, particularly where there are boundary issues.

Dlmap: AnRPackage for Mixed Model QTL and Association Analysis

<b>dlmap</b> is a software package capable of mapping quantitative trait loci (QTL) in a variety of genetic studies. Unlike most other QTL mapping packages, <b>dlmap</b> is built on a linear mixed model platform, and thus can simultaneously handle multiple sources of genetic and environmental variation. Furthermore, it can accommodate both experimental crosses and association mapping populations within a versatile modeling framework. The software implements a mapping algorithm with separate detection and localization stages in a user-friendly manner. It accepts data in various common formats, has a flexible modeling environment, and summarizes results both graphically and numerically.</b></b>

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Towards flexible exascale stream processing system simulation

Stream processing is an important emerging computational model for performing complex operations on and across multi-source, high-volume, unpredictable dataflows. We present Flow, a platform for parallel and distributed stream processing system simulation that provides a flexible modeling environment for analyzing stream processing applications. The Flow stream processing system simulator is a high-performance, scalable simulator that automatically parallelizes chunks of the model space and incurs near-zero synchronization overhead for acyclic stream application graphs. We show promising parallel and distributed event rates exceeding 149 million events per second on a cluster with 512 processor cores.

A flexible modelling environment for integrated urban water harvesting and re-use

The steady increase of urban population and the possible effects of climate change that may adversely affect the amount of water available in current water supply systems, makes the study of stormwater and rainwater harvesting and wastewater recycling a high priority. The basic proposition is that any system of water supply that can reduce the amount of water drawn from main reservoirs will be of benefit to the whole supply region especially in terms of drought security. This paper describes a versatile modelling framework which can simulate a wide variety of combinations of centralised and decentralised Integrated Urban Water Management schemes from the allotment to the whole suburb scale. The framework combines two modelling approaches. The first, called urbanCycle, simulates water supply and demand, stormwater and wastewater using allotments as the basic building block. Although urbanCycle can simulate processes in great detail, it assumes that the network forms a directed acyclic graph. This simplifies the connectivity logic but precludes investigation of systems with decentralised storage, feedbacks and multiple supply paths. To overcome this, a second model, called urbanNet, based on network linear programming, is embedded in the urbanCycle framework to enable the modelling of recycling and harvesting options, as well as on-the-fly supply and demand decision making, based on objectives rather than pre-set operating rules.

Design of a Domain Specific Language for modelling processes in landscapes

The modelling of processes that occur in landscapes is often confronted to issues related to the representation of space and the difficulty of properly handling time and multiple scales. In order to investigate these issues, a flexible modelling environment is required. We propose to develop such a tool based on a Domain Specific Language (DSL) that capitalises on the service-oriented architecture (SOA) paradigm. The modelling framework around the DSL is composed of a model building environment, a code generator and compiler, and a program execution platform. The DSL introduces five language elements ( entity, service, relation, scenario and datafacer) that can be combined to offer a wide range of possibilities for modelling in space and time at different scales. When developing a model, model parts are either built using the DSL or taken from libraries of previously built ones, and adapted to the specific model. The practical usage of the DSL is illustrated first with the Lotka–Volterra model, and then with a landscape modelling experiment on the spread of a mosquito-borne disease in the Sahelian region of West Africa. An interesting characteristic of this approach is the possibility of adding new elements into an existing model, and replacing others with more appropriate ones, thus allowing potentially complex models to be built from simpler parts.

Increase of the fuel cell system efficiency — Modular testing, analysis and development environment

The main issue in preparing fuel cell systems for the future market is system reliability and efficiency. Apart from successful field test trials, any type of stationary, in general automotive or portable fuel cell systems are at the development stage. One task to deal with is to increase the component and system efficiencies by facilitating the system construction or eliminating parasitic components.With newly established effective standardised system and component tests, linked with a flexible modelling and simulation environment, the development process and the determination of the system efficiencies as well as the inaccessible system values can be accelerated.In this work a modular model-aided system analysis and development environment is presented which has been evaluated and validated at the IWE. The tool, a combination of standardised testing, modelling and simulation, has been applied to different types of fuel cell systems showing the tool flexibility, modularity and accuracy. In the presented case the tool was used for system analysis and studies on efficiency increase of a complex prototype stationary PEMFC system.

Modular Testing and Simulation Environment for Analysis and Optimization of Fuel Cell Systems

Improvement of fuel cell systems with respect to reliability and efficiency is the main issue in preparing them for their commercialization. Stationary, automotive or portable systems are still at the development stage and a lot of tasks have to be solved as to increase the component and system efficiencies by facilitating the system construction or eliminating parasitic components. To accelerate the development and to determine the systems efficiencies as well as the inaccessible system values, effective standardized tests have to be established and linked with a flexible modeling and simulation environment. A modular model-aided system analysis and development environment is presented which has been evaluated and validated at the IWE. The tool has been applied to a complex prototype stationary PEMFC system, a commercial methanol based PEMFC auxiliary power unit and a fictive stationary SOFC system proving the tool flexibility, modularity and accuracy for a wide range of applications.

Development of Traffic Air Quality Simulation Model

The U.S. Environmental Protection Agency currently promulgates the use of CAL3QHC to model the concentrations of carbon monoxide near roadway intersections. The steady-state and macroscopic methods used in this model represent rough approximations of the physical phenomena that occur at intersections and are unintuitive for the users. Therefore, the traffic air quality simulation model (TRAQSIM) was developed to create a theoretically more realistic (more natural), easier to understand, and more flexible modeling environment than CAL3QHC. Instead of the steady-state plume equations used in CAL3QHC, TRAQSIM models dispersion through the use of Gaussian puffs emitted from discrete moving sources in a traffic simulation environment. Although most components incorporated in TRAQSIM are not new, the combination of these components within a fully integrated environment is new and provides the potential for a more direct (more logical) expansion of modeling capabilities. As part of an initial validation assessment, a relative comparison of the results obtained with CAL3QHC and TRAQSIM showed that TRAQSIM produced a more intuitively correct spatial allocation of concentrations. The validation assessment also showed good agreement by both models when the data obtained with the models were compared with the measured data, with overall R2 values of .721 for CAL3QHC and .605 for TRAQSIM. Although these values appear to favor CAL3QHC, the analysis of individual cases showed mixed results (i.e., six cases favored CAL3QHC and five cases favored TRAQSIM). Therefore, additional assessments with larger data sets will need to be conducted before any definitive conclusions can be made. This paper describes the data and methodologies used to develop TRAQSIM and the initial validation work.

Development of Traffic Air Quality Simulation Model

The U.S. Environmental Protection Agency currently promulgates the use of CAL3QHC to model the concentrations of carbon monoxide near roadway intersections. The steady-state and macroscopic methods used in this model represent rough approximations of the physical phenomena that occur at intersections and are unintuitive for the users. Therefore, the traffic air quality simulation model (TRAQSIM) was developed to create a theoretically more realistic (more natural), easier to understand, and more flexible modeling environment than CAL3QHC. Instead of the steady-state plume equations used in CAL3QHC, TRAQSIM models dispersion through the use of Gaussian puffs emitted from discrete moving sources in a traffic simulation environment. Although most components incorporated in TRAQSIM are not new, the combination of these components within a fully integrated environment is new and provides the potential for a more direct (more logical) expansion of modeling capabilities. As part of an initial validation assessme...