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The use of computational techniques in the design of dry powder inhalers (DPI), as well as in unravelling the complex mechanisms of drug aerosolization, has increased significantly in recent years. Computational fluid dynamics (CFD) is used to study the air flow, inside the DPI, during the patient inspiratory act while discrete element methods (DEM) are used to simulate the dispersion and aerosolization of the drug product powder particles. In this work we discuss the possibility to validate a coupled CFD-DEM model for the NextHaler® DPI device against previously published experimental data. The approximations and assumptions made are deeply discussed. The comparison between computational and experimental results is detailed both for fluid and powder flows. Finally, the potential and possible applications of a calibrated DPI model are discussed as well as the missing elements necessary to achieve a fully quantitative predictive computational model.

This paper presents a novel design method of the Mission Success Space (MSS) for the evaluation on aircraft contribution effectiveness. MSS concept was proposed for giving success criterion of a mission and judging the success by conventional mission effectiveness with regards to the aircraft capabilities. This space is created by the Mission Success Function (MSF) and the original Effectiveness Index Space (EIS) where empirical equations are usually assumed to be MSFs. Based on this MSS concept, this paper firstly defines the MSS-based evaluation, then further summarizes the evaluation process of the Contribution to System-of-Systems (CSS). More importantly, based on the thought of Inverse Design (ID), a new design method of MSF is presented comprehensively analyzing aircraft’s CSS in a combat mission without using any empirical MSF. The definition of MSS based ID is given and the design procedure is sequentially introduced. Two different confrontation cases are depicted with many details as the simulation validation: Air-to-ground and Penetration. There are two design variables considered for designing MSS in the latter case while only one for the former. However, in both cases, the best design is given by evaluating the Gaussian fitting performance of CSS.

The aim of this work is to improve numerical predictions of natural convection heat transfer inside of gas turbines. It is desirable for engineers to use numerical simulations be able to accurately predict the heat transfer inside of a gas turbine casing during engine shutdown. This will help them to understand and alleviate problems such as engine casing distortion. A natural convection test rig was constructed to provide simulation validation data. The rig represented a section of a large gas turbine casing and operated at the same Rayleigh number as a real engine. The casing rig was able to provide a unique dataset that could not be obtained through real engine measurements. Analysis of the casing rig was performed using a large eddy simulation and RANS simulations. It was found that the baseline RANS simulation did not predict the heat transfer accurately enough for engineering use. The simple gradient diffusion hypothesis (SGDH) turbulent heat flux model used in the baseline simulations was replaced by more advanced generalised gradient diffusion hypothesis (GGDH). A modification to the GGDH model, called GGDH+, was developed to account for buoyancy effects on the turbulent heat flux. The GGDH+ model was then able to give heat transfer predictions comparable to the LES results.

This paper presents on-going challenges in the present paradigm shift of earthquake-induced ground motion prediction from empirical to physics-based simulation methods. The 2010–2011 Canterbury and 2016 Kaikoura, New Zealand earthquakes are used to illustrate the predictive potential of the different methods. On-going efforts in simulation validation and theoretical developments are then presented, as well as the demands associated with the need for explicit consideration of modelling uncertainties. Finally, discussion is also given to the tools and databases needed for the efficient utilisation of simulated ground motions both in specific engineering projects as well as for near-real-time impact assessment.

This article presents the simulation, validation and implementation of a temperature sensor based on RO (Ring Oscillator) in order to carry out a thermal study for the detection and localization of thermal peaks in an integrated circuit (CI). The input temperature, angles, distance as well as certain frequencies will be obtained using the GDS (gradient Direction Sensor) method based on RO. In this work, a simulation with the ModelSIM tool as well as the technological validation of the degree of integration with the NCLAUNCH Cadence tool and check this type of fully digital temperature sensor includes a number of exact inverters that can be logically inserted. In addition, a DE1 FPGA cyclone of the V 5CSEMA5F31C6 family is used for implementation. Before the transition to FPGA board implementation and verification, the use of the VHDL code and its bench test is required to describe three inverters that form a ring oscillator, in order to validate the results obtained after the synthesis Quartus Prime. This document provides a solution to thermal constraints and local overheating in the design of complex systems, which has been a major concern for designing integrated circuits.

Model-based development is increasingly used in embedded systems, which are often deployed in a safety-critical environment. Verification techniques, supporting the development process can not only increase safety, but also help to speed up the process. In many cases models are designed with block diagrams, assisting rapid prototyping. However, automated verification is, thereafter, often applied to the resulting code. Instead of focusing on code, we apply verification techniques to models consisting of block diagrams and MATLAB code. We propose to combine a value and slope range analysis, with symbolic methods. In this way, our concept can not only prove properties in models, but also check rate requirements automatically, which arise from physical constraints of the environment. We evaluate our work in case studies from ongoing research projects. These case studies cover the domains of clinical intensive care, autonomous drone control and driver assistance. All systems are also evaluated with a commercial verification tool, highlighting benefits of the tool and our implementation.

In this paper, an analytical model for buildings simulation and performance evaluation has been introduced and compared to existing numerical models already used in simulation tools. The proposed analytical model is based on the quadrupole approach, which is used for solving partial differential linear equations, while the numerical model is mainly provided by EnergyPlus simulation tool. To validate the proposed analytical model, results have been confronted to those of the BESTEST standard case 600 FF (Light weighted free-floating building) and 900 FF (Heavy weighted free-floating building). These latter are based on the ANSI/ASHRAE norm standard 140-2014 “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs”. Simulations have been conducted by solving, in transient conditions, the heat equation and assessing then the temperature evolution inside the building’s zone. Results are reported and proved the accuracy of the proposed analytical model in terms of transient regime resolution since it considers all the necessary data and dynamics, to model the building and its internal gains without simplifications or hypothesis. Besides, it requires very low simulation time, which is required for large scale buildings’ simulations.

A simulation program for calculating the IV-curve for series connected PV-modules during partial shadowing has been developed and experimentally validated. The software used for modelling the modules is LTspice IV. The validation has been done by means of a comparative analysis using the experimental results obtained in a set of tests performed on the mono-crystalline modules of the Gavle University’s laboratory in Sweden. Experimental measurements were carried out in two groups. The first group is a string of six modules with bypass diodes while the second one corresponds to a single PV module. The simulation results of both groups demonstrated a remarkable agreement with the experimental data, which means that the designed model can be used for simulating the influence of shading on the power of a string. The model has been used for analysing the performance of strings of PV modules with shadows and the benefits of installing DC-DC optimizers or module inverters, that minimise the impact of shading, have been investigated.

In this paper, the robust tuning rules for a first order reduced ADRC (Active Disturbance Rejection Controller) are suggested for lag dominated processes. Based on the D-partition method and FOPDT (First Order plus Dead Time) process approximation, a software tool is designed that allows for tuning subject to phase and gain margins. Based on this software, six robust tuning rules were derived. For these rules, the results of simulation validation in the application to benchmark processes are presented and compared to the performance of PI-based control systems. Additional experimental validation shows the practical applicability of the suggested tuning rules.

Uncertainty and sensitivity analysis is an indispensable element of any substantial attempt in reactor simulation validation. A Coordinated Research Program (CRP) supervised by the International Atomic Energy Agency was started to investigate the various uncertainty quantification methodologies for High Temperature Gas-cooled Reactors. The first phase of the CRP is dedicated to the estimation of cell and lattice model uncertainties due to the neutron cross sections. Phase II is oriented towards the investigation of propagated uncertainties from the lattice to the coupled neutronics/thermal hydraulics core calculations. Best-estimate results for the prismatic single block (Ex. I-2a) and super cell models (Ex. I-2c) were obtained using the SCALE 6.2.0 two-dimensional lattice code NEWT. A reference spectrum was obtained with Serpent 2.1.27 for the single block, super cell and core models. The flux spectrum in the system of interest plays a primary role in the quantification of uncertainties caused by cross sections. In this work, the TRITON/NEWT-flux-weighted cross sections obtained for Ex. I-2a and various models of Ex.I-2c are utilized to perform a sensitivity analysis of the MHTGR-350 core power densities and eigenvalues. The core solutions are obtained with the Idaho National Laboratory (INL) coupled code PHISICS/RELAP5-3D. It is observed that axial shape of the core power density does not vary significantly with the various lattice cell libraries utilized. The use of cross section libraries originating from super cells induces changes of the core power density by 1–10% radially as compared to the Ex.I-2a cross sections. The magnitude of these variations increases as the moderator-to-fuel ratio increases in the super cell lattice models. A lattice flux spectrum resembling the core spectrum is hence necessary for correct predictions in nodal core calculations.

High-resolution computer models can simulate complex systems and processes in order to evaluate a solution quickly and inexpensively. Many simulation models produce dynamic functional output, such as a set of time-series data generated during a process. These computer models require verification and validation (V&V) to assess the correctness of these simulations. In particular, the model validation effort evaluates if the model is an appropriate representation of the real-world system that it is meant to simulate. However, when assessing a model capable of generating functional output, it is useful to learn more than simply whether the model is valid or invalid. Specifically, if the model is deemed invalid, then what aspects of the model are incorrect? Is it possible to identify over what range the model data are a poor representation of the system data? Current V&V methods cannot identify these ranges. This paper proposes a wavelet analysis of variance (WANOVA) bisection method that first assesses model validity and can also identify the interval(s) over which the model is biased. The technique is illustrated using several simulation studies. Ultimately, this new method supports and expands the efficacy of model validation efforts.

In recent years, lightweight steel-framed (LSF) constructions are considered sustainable and widely used worldwide. However, the LSF wall assemblies have the risk of durability failure in hot-humid climates. This study conducted fundamental research on the hygrothermal performance of LSF wall assemblies by characterizing the hygrothermal responses, modelling and validating the simulation models under hot-humid climatic conditions . Through measuring the relative humidity and temperature of various positions inside, four typical and full-scale LSF wall assemblies were tested thoroughly and comparatively on two room-like test cubes in Guangzhou, a hot-humid city in China. It was found that the ventilated rainscreen provided better hygrothermal responses of the wall assemblies than the face-sealed cladding; the closed-cell external insulation could significantly improve the hygrothermal responses of the wall assemblies with stucco cladding; generally, wall assemblies with ventilated rainscreen and external insulation exhibited the best hygrothermal response in a hot-humid climate, followed by wall assemblies with ventilated rainscreen, wall assemblies with face-sealed cladding and external insulation and wall assemblies with face-sealed cladding. Furthermore, the measured data were used to model and validate the simulation models of the four typical wall assemblies in software. It was shown that hygrothermal simulation is sufficient for evaluating the long-term hygrothermal performance of LSF wall assemblies in hot-humid climates, and relatively conservative settings are necessary for evaluating cases with great uncertainties. Overall, this study could lay a foundation for better assessing and improving the hygrothermal performance, durability and sustainability of LSF buildings in hot-humid climate regions worldwide.

Model validation is that critical component in the simulation development process that ensures a model is truly representative of the system that it is meant to model. Although there are numerous validation techniques described in the literature, many of these techniques still require some amount of subjective analysis in order to assess validity. This is particularly true with dynamic simulation output. To reduce or eliminate this subjectivity, this paper proposes a validation process that uses wavelet analysis of variance (WANOVA) as an effective method to statistically accept or reject a model as valid. This WANOVA validation approach performs statistical inference in the time-frequency domain to take advantage of wavelet sparsity and decorrelation. This process uses a test statistic based on thresholded wavelet coefficients to test the null hypothesis that the set of system data and model data are statistically equivalent. The validation technique is illustrated using a simulation study and empirical data from an automobile crash study.

Programming-illiterate users face barriers exploiting end-user programming tools.We offer a user-centric approach to support testing and validation in an end-user software engineering cycle.This approach allow end users to build error-free SOA-based software.Thus, end users can create applications to support their routine work on their own.The presented approach elicit the best practices and principles of the current SOTA. This paper addresses one of the major web end-user software engineering (WEUSE) challenges, namely, how to verify and validate software products built using a life cycle enacted by end-user programmers. Few end-user development support tools implement an engineering life cycle adapted to the needs of end users. End users do not have the programming knowledge, training or experience to perform development tasks requiring creativity. Elsewhere we published a life cycle adapted to this challenge. With the support of a wizard, end-user programmers follow this life cycle and develop rich internet applications (RIA) to meet specific end-user requirements. However, end-user programmers regard verification and validation activities as being secondary or unnecessary for opportunistic programming tasks. Hence, although the solutions that they develop may satisfy specific requirements, it is impossible to guarantee the quality or the reusability of this software either for this user or for other developments by future end-user programmers. The challenge, then, is to find means of adopting a verification and validation workflow and adding verification and validation activities to the existing WEUSE life cycle. This should not involve users having to make substantial changes to the type of work that they do or to their priorities. In this paper, we set out a verification and validation life cycle supported by a wizard that walks the user through test case-based component, integration and acceptance testing. This wizard is well-aligned with WEUSE's characteristic informality, ambiguity and opportunisticity. Users applying this verification and validation process manage to find bugs and errors that they would otherwise be unable to identify. They also receive instructions for error correction. This assures that their composite applications are of better quality and can be reliably reused. We also report a user study in which users develop web software with and without a wizard to drive verification and validation. The aim of this user study is to confirm the applicability and effectiveness of our wizard in the verification and validation of a RIA.

Ambiguity Resolution (AR) is a key technique in GNSS precise positioning. In case of weak models (i.e., low precision of data), however, the success rate of AR may be low, which may consequently introduce large errors to the baseline solution in cases of wrong fixing. Partial Ambiguity Resolution (PAR) is therefore proposed such that the baseline precision can be improved by fixing only a subset of ambiguities with high success rate. This contribution proposes a new PAR strategy, allowing to select the subset such that the expected precision gain is maximized among a set of pre-selected subsets, while at the same time the failure rate is controlled. These pre-selected subsets are supposed to obtain the highest success rate among those with the same subset size. The strategy is called Two-step Success Rate Criterion (TSRC) as it will first try to fix a relatively large subset with the fixed failure rate ratio test (FFRT) to decide on acceptance or rejection. In case of rejection, a smaller subset will be fixed and validated by the ratio test so as to fulfill the overall failure rate criterion. It is shown how the method can be practically used, without introducing a large additional computation effort. And more importantly, how it can improve (or at least not deteriorate) the availability in terms of baseline precision comparing to classical Success Rate Criterion (SRC) PAR strategy, based on a simulation validation. In the simulation validation, significant improvements are obtained for single-GNSS on short baselines with dual-frequency observations. For dual-constellation GNSS, the improvement for single-frequency observations on short baselines is very significant, on average 68%. For the medium- to long baselines, with dual-constellation GNSS the average improvement is around 20–30%.

In many multi-phase flows, the online measurement and monitoring of phase fractions as well as distributions play a vital role in determining the process efficiency and safety. The dual-modality electrical capacitance tomography (ECT) and electromagnetic tomography (EMT) technique provides an efficient measure to estimate the distribution of electromagnetic property in rapidly changing multi-phase flows. A generative adversarial network (GAN) is designed to solve the fusion problem of ECT and EMT in the gas–liquid–solid (G–L-S) three-phase flow measurement. The fusion model incorporates the features of dual-modality measurements and images to generate the electromagnetic property in high precision, by which the accurate phase volume can be derived. Furthermore, a simulation approach is proposed to provide the sufficient measurement samples that approximate the real three-phase flow measurement. In the numerical study, the fluidization process of a G–L–S fluidized bed (GLSFB) reactor is simulated and measured by the models of ECT and EMT. The simulation validation on samples from GLSFB and experiments on the three-phase flow setup demonstrate the high accuracy of electromagnetic property reconstruction and generalization ability of fusion model that suitable for various flow regimes. The errors of calculated phase fraction are less than 0.15 in both simulations and experiments.

The accumulation of local slippage between thread contact surfaces has recently emerged as a key reason for the self-loosening of threaded fasteners subjected to transversal vibration. However, the mechanism of local slippage accumulation is still elusive despite intense research. The authors recently proposed modified Iwan models to theoretically analyze local slippage accumulation and identified the principle of anti-loosening, upon which some novel anti-loosening structures were designed. However, simulation validation of the modified Iwan models and deep performance analysis of novel thread structures are lacking. Hence, we developed novel algorithms to simulate the modified Iwan models for the cumulative process of local slippage. These algorithms confirmed that the previous theoretical analytical results are reliable. Additionally, the mechanism of local slippage accumulation was revealed and the critical local slippage accumulation was studied. We also compared systematically the tightening and anti-loosening performances of proposed novel thread structures with other existing anti-loosening products by finite-element analyses and experiments. The novel thread structures exhibited superior anti-loosening performance and were convenient to install and remove.

This paper addresses the gain-scheduled output feedback control for morphing aircraft (MA) in full envelope using switching polytopic linear parameter-varying (SPLPV) theory. The SPLPV model of MA is derived by scheduled parameter interval division and convex decomposition. Considering average dwell time (ADT) and mode-dependent average dwell time (MDADT) switching logics, sufficient conditions to ensure that the SPLPV systems are exponential stable with weighted disturbance attenuation indexes are presented based on the multiple parameter-dependent Lyapunov functions (MPDLFs) technique, and the comprehensive algorithm for the controller solution is obtained. To reduce the computational burden and ease controller implementation, a finite number of linear matrix inequalities (LMIs) are developed for solving controller by proposing the intermediate controller variables are depend affinely on the scheduled parameter. In addition, the control matrix of linear parameter-varying (LPV) system is not limited to be constant, which greatly expands the application scope of the proposed method. The simulation validation is carried out on the SPLPV control for MA.

Model development is a successive process of validating, revising, and extending models, and requires iterative execution of simulation experiments. While developing a model by extension, executing similar simulation experiments to those performed with the original model reveals important behavioral insights into the extended model. An automatic generation and execution of these simulation experiments can provide valuable support in the process of developing models. A prerequisite is an explicit specification of simulation experiments. Therefore, we annotate models with simulation experiments that are specified in a declarative domain specific language SESSL (Simulation Experiment Specification via a Scala Layer). Based on experiment specifications of the original model, we introduce a mechanism to automatically generate and execute simulation experiments for the extended model with necessary adaptations. Furthermore, as we experiment with stochastic models, we exploit statistical model checking and specify the expected model behavioral properties, against which the simulation results are checked. Thereby, when a model is extended, the original experiment specifications are reused, adapted, and applied to the extended model. Accordingly, the generated simulation trajectories are probed to check whether the expected properties hold with a certain probability or not. Thus, more fast and frequent feedback during model development can be provided to the modeler. Based on a model of membrane related dynamics, we show how the developed approach can be used in successively extending models.

New framework for capturing, statistically modeling and simulating evolution of modelsEvolution formulated at 2 abstraction levels: low and high level changes between modelsEmpirical study of evolution of Java models using 3 kinds of time series modelsMixed ARMA-GARCH models were superior, but ARMA models performed well in practiceStatistical models were used to generate more realistic test models for MDE tools This paper presents a new methodological framework for capturing and statistically modeling the evolution of models in model-driven software development. The framework captures the changes between revisions of models in terms of both low-level (internal) and high-level (developer-visible) edit operations applied between revisions. In our approach, evolution is modeled statistically by using ARMA, GARCH and mixed ARMA-GARCH models. Forecasting and simulation aspects of these time series models are thoroughly assessed. The suitability of the framework is shown by applying it to a large set of design models of real Java systems. Our analysis shows that mixed ARMA-GARCH models are superior to ARMA models.A main motivation for, and application of, the resulting statistical models is to control the generation of realistic model histories which are intended to be used for testing model versioning tools. We present the architecture of the model generator and show how to generate random sequences from the statistical models which control the generation process. Further usages of the statistical models include various forecasting and simulation tasks.

The SI-CAI (Spark ignited-controlled auto-ignition) hybrid combustion is promising in achieving smooth transition between SI and CAI combustion, but it is challenging to control due to its high sensitivity to boundary conditions. In this paper, a disturbance rejection-based control framework is proposed for the SI-CAI hybrid combustion. The complexity, nonlinearity and cross-coupling inside are stripped away by idealizing the combustion process into three decoupled integrators, for the combustion timing channel, the IMEP channel and λ channel respectively. All other dynamics that deviate from the integrators, internal and external, are lumped as "total disturbance" for each channel. With the total disturbances estimated and cancelled in real-time via three extended state observers, the enforced plant, i.e. the parallel integrators, is controlled by three proportional controllers. To further enhance the response and reject the time-varying uncertainties, a feedforward controller assisted by parameters on-line correction controller is added as a complementary. Simulation validation confirms the superiority of the proposed framework in terms of transient response, parameter tuning, robustness, and the adaptability.

Infrared(IR) sensors are widely used to estimate distance due to their practicality and cost-effectiveness. The distance is estimated by measuring the reflectance amplitude of IR light from a targeted object. However, the non-linearity characteristic of the reflected IR light makes distance estimation difficult. Additionally, IR sensors have low accuracy and precision. Some researches superficially studied distance estimation using IR sensors. However, a deeper study is needed on this subject as competitive market conditions push companies to produce high-quality devices without increasing the production cost. Therefore, this paper proposed a procedure on improving the accuracy and precision of distance estimation of a low-cost, but noisy IR sensor. The proposed procedure contains six complementary steps. Experimental and simulation validation is performed to prove the effectiveness of the proposed procedure. In the validation, the distance estimation error never exceeds ±2 mm between 10 cm and 400 cm.

Accurately predicting long-term, process-based runoff and sediment yields measured at the outlet of even small watersheds can be challenging. The assessments following careful parameter determination enable establishing a baseline for local land management to assess policy implementations and rehabilitation especially under climate or land use/cover changes. Process-based, continuous models have demonstrated their advantage of representing event-based hydrological responses at smaller spatial and temporal scales. In this study, the Geospatial interface for the Water Erosion Prediction Project (WEPP) is validated for a series six, neighboring, nested subwatersheds (101 to 106) at Lucky Hills, Walnut Gulch Experimental Watershed (WGEW), Tombstone, Arizona (USA). The primary objective of this study is to assess short-term parameterization and long-term verification and simulation validation of GeoWEPP based on 55 years of runoff and sediment yields for six subwatersheds. The effective hydraulic conductivity (Keff) parameter is adjusted based on runoff observed in watershed 101 using a research-grade 1 m-Digital Elevation Model (DEM). The performance of runoff simulated generated by an aggregated 5 m-resolution DEM lead to better results in contrast to using the original 1 m- or aggregated 3 m-resolution. Since there are no sediment yield observations for that watershed, the similar sized, neighboring watershed 102 and a publicly available DEM were used to parameterize critical shear. The short-term verification of Keff as well as the long-term verification of Keff and critical shear stress indicate that both parameters generated based on one subwatershed can be used to accurately predict the runoff in all other watersheds in the study area. However, the results have a tendency to slightly over-estimate runoff, and become more significant with the distance from the rainfall and runoff gauges for the watershed that was used for the Keff parameter estimation. For sediment yields, the results indicate that the short-term parameterization of shear stress based on one watershed can potentially lead to significantly different results for neighboring watersheds. The results are the baseline for spatially distributed shear stress and channel erosion parameter validation and impact assessment for future climate and land use changes.

A Reynolds-averaged Navier Stokes computational fluid dynamics (RANS-CFD) package will be one of the primary tools used during the development of a performance prediction program for wind-assisted commercial ships. This paper describes the simulation verification exercise, performed in support of the experimental validation presented in Part 1 of this two-part series describing the RANS-CFD method employed in this research. The predominance of large-scale separated flow structures in the wake of the sailing ship, an artefact of sideforce production necessary for sailing, points to a careful verification exercise and estimate for the numerical uncertainty to support the systematic investigation of wind-assisted ship hydromechanics and meshing guidelines within the available computer resources. Methods for CFD uncertainty quantification are defined and implemented for verification cases at leeway angles equal to 0ᵒ, 6ᵒ, and 9ᵒ. Analysis for four sets of grids with different meshing strategies and for varying time steps results in a grid definition and time step for simulation validation. Numerical uncertainty as adopted in Part 1 for validation is defined. Finally, the meshing strategy for full-scale simulation is described, as used for the production runs of the Delft Wind Assist Series.

Test verification and validation are two very important, College of American Pathologists (CAP)-required processes used in the laboratory to ensure that a new test performs as expected and continues to perform as expected throughout its use. The initial analysis of test performance is achieved by performing test verification, which is a one-time process to confirm the expected test performance characteristics provided by the manufacturer. The type and depth of the verification are dependent upon the type of test being verified and can range from a rather simple comparison to a much more complex analysis. Conversely, test validation, is an ongoing process to monitor and ensure that the test continues to perform as expected and is typically achieved though the laboratory's quality assurance plan. Designing appropriate test verification and validation plans can be confusing; therefore, the purpose of this paper is to clarify the various processes and procedures that can be used.

Validation constitutes a vital process in model development and application, as it ensures the applicability of a model for the intended purposes and trustworthy results within the range of model assumptions. Commonly, independent empirical data sets are statistically compared with the generated model results, which is an adequate approach for models which operate on a single hierarchical level, such as most equation-based models. Individual-based models (IBM) can operate on different organisational levels synchronously and have an inherent complex and variable interaction structure for many applications. Thus a plain comparison of data congruity on the result levels might leave too many questions unanswered. However, a more comprehensive assessment of model validity can require additional investigations which encompass also qualitative and structural relationships. Here we describe a hierarchically structured validation which is oriented towards the investigated context of the model and allows organising the validation process in close relation to the different hierarchical levels which are covered in the model. The context oriented organisation protocol for validation includes the following steps: (1) assessing the different model levels separately, then, (2) applying a set of different techniques such as visual inspection, statistical comparison, involvement of experts, aggregation of data on higher integration levels and experimental validation. The context oriented approach accounts for the specificity of individual-based models – i.e., the dynamic self-organisation of model outcomes from biologically underpinned individual interactions without an inherent determination of properties on higher hierarchical levels – and extends the potential of the validation process qualitatively, as it allows to assess complex structural and causal relations and multi-level feedback processes of the developed models.

This paper proposes a Bayesian methodology to integrate model verification, validation, and calibration activities for the purpose of overall uncertainty quantification in different types of engineering systems. The methodology is first developed for single-level models, and then extended to systems that are studied using multi-level models that interact with each other. Two types of interactions amongst multi-level models are considered: (1) Type-I, where the output of a lower-level model (component and/or subsystem) becomes an input to a higher level system model, and (2) Type-II, where parameters of the system model are inferred using lower-level models and tests (that describe simplified components and/or isolated physics). The various models, their inputs, parameters, and outputs, experimental data, and various sources of model error are connected through a Bayesian network. The results of calibration, verification, and validation with respect to each individual model are integrated using the principles of conditional probability and total probability, and propagated through the Bayesian network in order to quantify the overall system-level prediction uncertainty. The proposed methodology is illustrated with numerical examples that deal with heat conduction and structural dynamics.

In modern offshore oil fields, wells can be equipped with routing valves to direct their production to multiple manifold headers, a strategy that is routinely adopted in practice either to provide resilience to equipment failure or to improve production. However, the existing models for production optimization do not account for splitting of flows and therefore require the wells to be connected to a single header. To this end, this work develops a nonlinear model of flow splitting that reproduces the complex behavior observed in multiphase-flow simulation. This model is further approximated with multidimensional piecewise-linear functions to a desired degree of accuracy with respect to simulated behavior. These piecewise-linear functions enable the development of a Mixed-Integer Linear Programming (MILP) formulation for production optimization, which decides between single and multiple routing of wells to headers. The effectiveness of this MILP formulation is assessed in a synthetic but representative gas-lifted oil field modeled in a standard simulator.

Accurate simulation is vital for the proper design and evaluation of any computing architecture. Researchers seek unified simulation frameworks that can model heterogeneous architectures like CPU and GPU devices and their interactions as computing patterns move toward heterogeneous era. In this paper, we introduce MCMG (Multi-CPU Multi-GPU) simulator, a cycle accurate, modular and open-source toolset that enables simulating x86 CPUs and Nvidia G80 like GPUs simultaneously. Targeting heterogeneous architectural exploration, MCMG supports fully configuration of multiple CPUs, GPUs and their memory sub-system. Not only CPUs, relative running frequency of each GPU can be also defined conveniently. Simulation validation is demonstrated with a preliminary architectural exploration study. Then we present shared LLC access results of heterogeneous cores and show reasonable explanation. Finally, we conclude the job. We first integrate Nvidia rasterization-based GPU simulator with CPU simulator.We first provide SLI-like mode multiple GPUs simulation capability.We provide multiple CPUs and GPUs simulation with shared memory address space.

In this paper, we describe simulation models in healthcare that have been developed in the past two decades. Simulation systems, ranging from simulation of patient flow in emergency rooms to simulation of populations with a specific chronic diseases, are reviewed. Simulation types included discrete event simulation (DES) and agent based simulation (ABS). A trend of variability and scalability were identified, and discussed in terms of platform used to develop the model, data sources, and computational power needed to run the simulation. In the synthesis of simulation models, programming languages and products emerged as clusters. Design models and systems engineering development processes are examined with a focus on requirements discovery, models and scenarios of simulation. Graphic user interfaces in the simulation tools in healthcare are reviewed in terms of visual design and human factors. Furthermore, interaction modes and trends of information visualization techniques used for the simulations are reported. Agent-based simulation models in particular were reviewed, and findings suggest agent characteristics varied across literature researched in aspects such as socio-demographic design considerations.

In this paper we revisit fundamentals of modeling and discuss the notions of models, representations, validation and verification, and many other basic concepts. We argue that the key notions about model and simulation quality, i.e. of what constitites valid simulations, can be understood and also classified using the simple concept of comparison. To arrive at this conclusion, we first discuss our basic understanding of modeling and that of the simulation process. Throughout the paper we will discuss issues related to computer simulation in full generality, but occasionally we will also refer to particular points concerning our own domain of work, agent-based simulations.

This article presents a scientific discussion about the ongoing progress in the development of traffic simulation system platforms. As part of the discussion, the presentation introduces a simulation model that is based on the fully functional, real-world online traffic information system OLSIMv4 which is the updated version of the traffic information platform for the highway network of North Rhine-Westphalia (Germany). The simulation model consists of a simulation engine and a combination of several subject-specific model families such as vehicle models, microscopic traffic models, detector models, and tuning element models. Additionally, it provides a data model for arbitrary road traffic networks in highway and urban environments. The presentation includes a demonstration of how to form and initialize all relevant system components by providing an example for each component. The demonstrations use the declarative programming language Maude to form and initialize the components due to its simplicity and expressive power. The components facilitate their enhancement by a verification and validation management approach. The goal of the enhancement effort is to optimize the further development of the underlying OLSIMv4 system. In addition, the presented methodology stands exemplarily for the design and implementation of a whole class of systems. Additionally, the definitions of the simulation model can be used as a specification for an implementation with sequential, parallel, and distributed operation. Therein, independent entities can be inferred automatically by the simulation engine as part of an automatic domain decomposition. It has been implemented as a sequential and a parallel simulation that exploits CPU thread-level parallelism.

A new identification method is proposed to solve the problem of the influence on the loaded excitation signals brought by high feedback gain augmentation in lateral-directional aerodynamic parameters identification of fly-by-wire (FBW) passenger airliners. Taking for example an FBW passenger airliner model with directional relaxed-static-stability, through analysis of its signal energy distribution and airframe frequency response, a new method is proposed for signal type selection, signal parameters design, and the appropriate frequency relationship between the aileron and rudder excitation signals. A simulation validation is presented of the FBW passenger airliner’s lateral-directional aerodynamic parameters identification. The validation result demonstrates that the designed signal can excite the lateral-directional motion mode of the FBW passenger airliner adequately and persistently. Meanwhile, the relative errors of aerodynamic parameters are less than 5%.

The ability to predict the natural fragmentation of an explosively loaded metal casing would represent a significant achievement. Physically-based material models permit the use of small scale laboratory tests to characterise and validate their parameters. The model can then be directly employed to understand and design the system of interest and identify the experiments required for validation of the predictions across a wide area of the performance space. This is fundamentally different to the use of phenomenologically based material algorithms which require a much wider range of characterisation and validation tests to be able to predict a reduced area of the performance space. Eulerians numerical simulation methods are used to describe the fragmentation of thick walled EN24 steel cylinders filled with PBXN-109 explosive. The methodology to characterise the constitutive response of the material using the physically based Armstrong–Zerilli constitutive model and the Goldthorpe path dependent fracture model is described, and the results are presented. The ability of an Eulerian hydrocode to describe the fragmentation process and reproduce the experimentally observed fragment mass and velocity distributions is presented and discussed. Finally the suitability of the current experimental analysis methodology for simulation validation is addressed.

In an attempt to develop a verification and validation standard for building fire evacuation models, Ronchi et al. (2013) at the United States’ National Institute of Standards and Technology (NIST) recommended a set of seventeen verification tests. We found that the application of these verification tests allowed us to make rather significant improvements to our simulation code (PEDFLOW) for approximately half of the recommended tests (Table 1). In some cases, we added capabilities that did not exist before. In other cases, we found anomalous behaviors and adjusted the existing code to remove these unexplained behaviors. This paper summarizes the work on the verification tests, highlighting the lessons learned and modifications made. We also discuss some modifications we recommend to the NIST verification tests, as well as demonstrate how to make these tests suitable for all pedestrian flow models (not just building fire evacuation).

In November 2013, Technical Note 1822: The Process of Verification and Validation of Building Fire Evacuation Models was released by NIST. The note was intended to open discussion about evacuation modeling, rather than provide definitive guidelines. The aim of our paper is to add creative contribution to V&V topic. We propose adding some qualitative tests and distinguishing a base set of tests for all kinds of models from extended set of tests for specialized models. Moreover, the inclusion of additional tests is suggested, as well as the inclusion of some improvements and extensions to the tests proposed in the note.

This paper presents a framework for the integration of supply chain (or logistics/distribution), value chain (or financial), and business process (or operational/manufacturing) simulation models, which should facilitate assessing the impact of supply chain and operational changes on an enterprise's financial performance. A Design Science approach is taken to demonstrate that the REA ontology, which provides a shared conceptual ground for these three model types, and its axioms, which describe invariant conditions for value systems, can help to build conceptually sound simulation models and identify the integration points between these models. It is further shown how these three types of simulation models can be integrated into one value system model for discrete event simulation, making use of the ExSpecT simulation tool. With this ontology-based framework, simulation model builders should be able to scope their models better and define integration points with other models, which is expected to promote the (re)use of simulation models for different purposes (e.g., simulating logistical, operational and financial performance). Defining the scope of business process, supply and value chain simulation modelsRephrasing the REA modeling axioms for each of these types of simulation modelsIdentifying integration points between these models through the REA ontologyIntegrating these three types of models into a hierarchic value system modelFirst application of the REA ontology for building discrete-event simulation models

Currently, pedestrian simulation models are used to predict where, when and why hazardous high density crowd movements arise. However, it is questionable whether models developed for low density situations can be used to simulate high density crowd movements. The objective of this paper is to assess the existent pedestrian simulation models with respect to known crowd phenomena in order to ascertain whether these models can indeed be used for the simulation of high density crowds and to indicate any gaps in the field of pedestrian simulation modeling research. This paper provides a broad, but not exhaustive overview of the crowd motion simulation models of the last decades. It is argued that any model used for crowd simulation should be able to simulate most of the phenomena indicated in this paper. In the paper cellular automata, social force models, velocity-based models, continuum models, hybrid models, behavioral models and network models are discussed. The comparison shows that the models can roughly be divided into slow but highly precise microscopic modeling attempts and very fast but behaviorally questionable macroscopic modeling attempts. Both sets of models have their use, which is highly dependent on the application the model has originally been developed for. Yet, for practical applications, that need both precision and speed, the current pedestrian simulation models are inadequate.

The flight control system of a fly-by-wire (FBW) passenger airliner with a complex framework and high feedback gain augmentation would change the original characteristic of a loaded signal and suppress the excitation of an airplane’s pertinent motion modes. Taking a research example of an FBW passenger airliner model with longitudinal relaxed-static-stability, a new method of signal type selection and signal parameter design is proposed, through analysis of signal energy distribution and plane body’s frequency response. According to CCAR60—the Appraisal and Use Regulation of Flight Simulator Device, the simulation validation of the FBW passenger airliner’s longitudinal aerodynamic parameters identification is put forward. The validation result indicates that the designed signal could excite the longitudinal motion mode of the FBW passenger airliner adequately and the multiparameter comparison in simulation meets the objective test request of CCAR60. Meanwhile, the relative errors of aerodynamic parameters are less than 10%.

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.

Within the Model-Driven Engineering paradigm, software development is based on the definition of models providing different views of the system to be constructed and model transformations supporting a (semi)automatic development process. The verification of models and model transformations is crucial in order to improve the quality and the reliability of the products developed using this paradigm. In this context, the verification of a model transformation has three main components: the transformation itself, the properties of interest addressed, and the verification techniques used to establish the properties. In this paper we present an exhaustive review of the literature on the verification of model transformations analyzing these three components. We also take a problem-based approach exemplifying those aspects of interest that could be verified on a model transformation and show how this can be done. Finally, we conclude the need of an integrated environment for addressing the heterogeneous verification of model transformations.

The complementary role of experiments and of sensitivity analysis has been and still is a key feature of validation strategies used in the field of simulation tools for nuclear reactor design. The present paper gives a summary of the development of more and more sophisticated validation strategies up to the present trend for science-based validation approaches. Most examples and some very recent original developments are given, mostly in the field of neutronics that has traditionally provided cutting edge advances for simulation tools validation.

The computerized verification of scanned, handwritten signatures has been extensively studied in the past decades, but there are still several possibilities for improvement in the field. To achieve better verification results, we propose a simplified probabilistic model for off-line signature verification. In our model, each of the verification steps can be mathematically described and, therefore, individually analyzed and improved. Using this model, we are able to predict the accuracy of a signature verification system based on just a few a priori known parameters, such as the cardinality and the quality of input samples. Several experiments have been conducted using our statistics-based classifier to confirm the assumptions and the results of our model. Based on the results, we can provide answers to several old questions within the field, such as why is it so hard to achieve error rates below 10% or how does the number of original samples and features affect the final error rates.

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description—including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations—should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

The verification system aims at monitoring the forecast quality over time. Verification helps improving the forecast quality by knowing the strengths and weaknesses of the existing forecasting system and by comparing the quality of different forecasting methodologies. Thus, the web-based verification system has been developed for verification of forecast results that is produced by International Center for Theoretical Physics Regional Climate Model v4 for our country. The forecasters and analysts can analyze the data in real-time with this web-based system. In this study, model values obtained from the system provided by ULAKBIM High Performance and Grid Computing Center. Model and station values were compared with each other for verification of model results with observation values. Therefore model grid values are transferred to station by using bi-linear and nearest neighbor (k-NN) (proximal) interpolation methods. This process in meteorological literature is called grid to point technique. Verification methods for forecasts of continuous variables are used to verify forecast values with observation values. Some verification methods; Mean Error, Mean Absolute Error and Root Mean Square Error, are calculated for validation. Verification results are shown as table and graphics on web-based system which is developed by the power of PHP (PHP: Hypertext Preprocessor).

The ground simulation validation is an important process during small satellite's development. Giving consideration to the characteristics of small satellite and a type of ground simulation requirements, a small satellite hardware-in-loop simulation system based-on HLA is established. System structure, the interaction data table, hardware interface unit and the federal members’ flow chart are designed. The simulation and validation are done to a certain small satellite through the designed hardware-in-loop simulation system. The results show that the simulation system is able to simulate dynamics and control subsystem, power supply subsystem and payload subsystem and realize accessing physical equipment of control subsystem much better, and has good practical application value.

Existed simulation works always emphasize on procedural verification, which put too much focus on the simulation models instead of simulation itself. As a result, researches on improving simulation accuracy are always limited in individual aspects. As accuracy is the key in simulation credibility assessment and fidelity study, it is important to give an all-round discussion of the accuracy of simulation systems themselves. The satellite networks as simulated targets are described first, and the major elements of satellite-network simulation systems are summarized. The framework of accuracy of simulation systems is presented in a comprehensive way. Based on the formalized description of framework of accuracy analysis in simulation systems, the practical approach are put forward, which can be applied to study unexpected or inaccurate simulation results. Following this, a satellite-network simulation system based on HLA is taken as an example to verify the usefulness of the approach proposed. The results show that the method works well and is applicable in accuracy analysis of simulation systems.

High fidelity Full Scope Operator Training Simulators play a key role in imparting plant related knowledge to the operating personnel in an effective way. It provides a platform for training the operators on normal and emergency conditions including all types of scenarios that would arise in any Nuclear Power Plant. The scenario based training helps the plant operator to handle a crisis in an efficient manner with the ultimate goal of safe and efficient operation of the plant. This paper discusses about the general description of PFBR Operator Training Simulator, modeling and simulation of various process models, the complexities involved etc. It also covers the associated process logics, controls, display of alarms and indications, malfunctions and transient incidents related to each process model, integration with other sub systems, individual process model testing, integrated performance testing and verification and validation of models. Simulation of process models are broadly classified into two main categories namely, External Models - that are developed in-house and ported to the simulator environment and Internal Models - that are developed using Simulation Tool. External Models are tested on the desk top for intended functioning and after obtaining satisfactory results, the models are ported to the simulator base wherein the Logical and Virtual Panel Models are built to represent a real system of the plant. Internal Models are built using the Simulation Tools and integrated with the External Models after testing. Combination of External and Internal Model represents the total plant and the performance testing is conducted in an Integrated Mode to qua lify the Process Models for training purpose.

Design verification in the digital domain, using model-based principles, is a key research objective to address the industrial requirement for reduced physical testing and prototyping. For complex assemblies, the verification of design and the associated production methods is currently fragmented, prolonged and sub-optimal, as it uses digital and physical verification stages that are deployed in a sequential manner using multiple systems. This paper describes a novel, hybrid design verification methodology that integrates model-based variability analysis with measurement data of assemblies, in order to reduce simulation uncertainty and allow early design verification from the perspective of satisfying key assembly criteria.

In traditional research, the validation of simulation models is mainly based on statistical analysis and simulation error evaluating. As simulation models become more and more complex, simulation behavior is more complicated and heavily dependent on simulation conditions, traditional method cannot be applied directly. On the other hand, measured data needed for traditional methods cannot always be available. What’s more, the validation of complex models is usually costly and time-consuming. This research makes effort to settle the problems above. It provides three aspects of advantage for model validation. First, complicated simulation behavior is abstracted and classified as five categories, among which behavior relationship and aggregative behavior is unique for complex simulation models. And the analysis and validation method of each kind of behavior is proposed. Second, besides measured data of real system, it proposed to utilize experience of expert and other kind of domain knowledge for validation task. And third, the simulation analysis and validation method proposed can be implemented in knowledge system and accomplish validation task automatically. Simulation output analysis is the most important step in model validation. Besides classical continuous dynamic fitting and statistical consistency analysis methods, we propose a domain knowledge-based method for the validation of relationship among behavior segments and aggregative behavior which is unique for complex simulation models. Domain knowledge takes the role of reference in model validation, and it varies from measured data of real system to qualitative experience of experts. Knowledge-based system is implemented based on domain knowledge and validation techniques proposed. Validation of electromagnetic rail gun simulation models are introduced as an example. It has been found that this method can provides an automatic validation way for complex simulation models, and validation tasks can be accomplished efficiently.