Calibration Of Simulation Models Research Articles

Analysis of accelerometer-derived interpersonal spatial proximities: A calibration, simulation, and validation study

ABSTRACTThe purpose of this study was to estimate distances from accelerometer-derived Bluetooth signals as a measure of interpersonal spatial proximity. Accelerometer-derived proximity data were collected indoors and outdoors over a 10m range to calibrate simulation models. Proximity data were simulated over 20m (indoor) and 50m (outdoor) ranges. Competing statistical and machine learning models were used to predict simulated distances; the Root-Mean-Square-Error (RMSE) was calculated. Simulation estimates were validated under conditions wherein a single beacon-receiver (SBR) and multiple beacons-receivers (MBR) collected proximity data indoors and outdoors within a ≤10m range. Simulation data showed that a Random Forest (RF) model performed optimally. The validated RF RMSE was ≤2.7 for SBR, and ≥90% of predicted distances were accurately classified as ≤10m. For MBR, ≥67% of predicted distances were accurately classified as ≤10m. Simulation and validation data suggest that distances can be estimated from accelerometer-derived proximity data within a 20m range using a SBR.

A systems dynamics approach for supporting regional decisions on the energetic use of regional biomass residues.

This article presents the definition, building, calibration and application of a system dynamics simulation model to quantify the present and future comparative advantages and disadvantages of using forest and agricultural residual biomass for energetic purpose through different processes and technologies, on a regional basis. A dynamic structure of the agricultural and forestry biomass process activities (i.e. production, transport, conversion and consumption) based on regional time-series data was built, implemented into and calibrated within a dynamic simulation tool (Vensim software) within a Hungarian county. Besides the agriculture and forestry sectors, the model includes data about demography, economy, environment and land use, among others. For carrying out the assessment, realistic scenarios of future biomass processes and of biomass use were formulated, characterised by quantitative sustainable development indicators and simulated within the dynamic model. The results prove that the introduced model supports decision making of biomass utilisation in a regional scale, through comparing and analysing quantitative changes of economic, social and environment indicators, which characterise a sustainable regional development. The model therefore allows analyses and evaluation of not only partial prosperous or adverse characteristics of biomass processes and use, but also to relate regional components and their development over time. The developed dynamic tool is therefore a powerful method to model complex systems, such as a regional energy system, considering the framework of the regional preferences stated by the regional stakeholders. It can thus provide useful decision support, especially when used as a priority-based screening of potential regional development programmes in the energy field.

Experimental and numerical investigation of a taut-moored wave energy converter: A validation of simulated buoy motions

This study presents an experimental and numerical investigation of a taut-moored wave energy converter (WEC) system with a point absorber type of WEC. The WEC system consists of a buoy, a unique three-leg two-segment mooring system with submerged floaters, and a power take-off system designed for the current experiment as a heave plate. The main objective of the study is to validate a numerical simulation model against experiments carried out in an ocean basin laboratory. Two physical models in model scales 1:20 and 1:36 were built and tested. The detailed experimental testing program encompasses tests of mooring system stiffness, decay tests, and different sea state conditions for ocean current, regular and irregular waves. A numerical model in the model scale 1:20 was developed to simulate coupled hydrodynamic and structural response analyses of the WEC system, primarily using potential flow theory, boundary element method, finite element method, and the Morison equation. Several numerical simulations are presented for each part of the experimental testing program. Results for the WEC buoy motions under operational conditions from the experiments and the numerical simulations were compared. This study shows that the simulation model can satisfactorily predict the dynamic motion responses of the WEC system at non-resonant conditions while at resonant conditions additional calibration is needed to capture the damping present during the experiment. A discussion on simulation model calibration with regard to linear and non-linear damping highlights the challenge to estimate these damping values if measurement data are not available.

Calibration and validation of large-scale traffic simulation networks: a case study

The availability, accuracy and relevance of real world input data are essential for developing a reliable traffic simulation model. Large-scale traffic simulation models, in particular, require data from many sources and in great detail. Though it is now possible to obtain detailed field data with the advent of new technologies such as GPS, cellular phones, RFIDs, it is still a challenge to gather all available data, especially traffic flow data, in the required spatial and temporal accuracy. The central theme of this paper is the calibration and validation (C&V) development of a large-scale traffic simulation model using data from multiple sources.

Simulation of Energy Sorghum under Limited Irrigation Levels Using the EPIC Model

Abstract. Energy sorghum is one of the most attractive alternatives for producing energy in many regions of the world because of the high biomass productivity obtained in a short period. However, it faces many challenges, particularly where water resources are limited. Crop simulation models are suitable decision support tools for the assessment of crop water use and biomass production under different spatial and climatic conditions. Calibration of simulation models to local conditions is a necessary procedure to improve model reliability. The objective of this study was to calibrate and evaluate the Environmental Policy Integrated Climate (EPIC) model for the production of energy sorghum under different irrigation levels. The model was then used to simulate crop biomass productivity and crop water use to identify appropriate irrigation strategies. This study was conducted at the Texas A&M AgriLife Research Center in Weslaco, Texas. Simulations were performed to determine the total dry biomass, crop water use, the relationship between crop productivity and crop evapotranspiration (ETc), and water use efficiency (WUE). Simulated ETc agreed well with estimates from a weather station, except for a few simulation events. The statistical parameters derived from measured versus simulated dry biomass in the calibrated model, which indicated that the model performed well, were R2 = 0.99 and PBIAS = -5.35%. The calibrated model showed great potential for simulating the total dry biomass. At full irrigation, the difference between measured and simulated total dry biomass was 4.3% in 2013 and 3.0% in 2015. This study showed that energy sorghum requires approximately 600 mm of water to obtain 23 Mg ha-1 of total dry biomass. It also demonstrated that the EPIC model could be used for assessment of crop water use and total biomass under limited irrigation levels, especially in semi-arid regions. Keywords: Crop model, Dry biomass, Energy sorghum, EPIC model, Irrigation, Model calibration, Water use efficiency.

Diesel and Spark Ignition Engines Emissions and After-Treatment Control: Research and Advancements

One of the major risks mankind has encountered during recent years is, without a doubt, the anthropogenic contribution to environmental pollution [...]

Public Transit and Air Pollution

Advocates of public transit frequently tout improved air quality as a primary benefit. Yet little is known about the causal impacts of public transit on local air pollution. Exploiting variation in transit availability resulting from work stoppages in 18 Canadian cities between 1974-2011, this study identifies the effect of public transit on air pollution. Our findings indicate that transit leads to a 3.5 part-per-billion increase in nitrogen oxides while having no statistically significant effect on carbon monoxide or fine particulate matter. Estimates are robust to a series of specification and placebo tests and magnitudes are consistent with a calibrated simulation model. Overall, the results suggest that expanding the current configuration of public transit in North American cities is unlikely to yield improvements in local air quality.

Calibration of Hygrothermal Simulations by the Help of a Generic Optimization Tool

Hygrothermal simulation represents an established method for the performance evaluation of building constructions. Some validation cases are known where the validity of these models was approved in form of a comparison between measured and simulated quantities under controlled boundary conditions. A further step is commonly not explained but also included, the calibration of uncertainly known model settings, e.g. surface exchange coefficients or measured material properties with a relevant uncertainty range. This paper summarizes a calibration procedure, which was carried out for the data analysis of a hygrothermal test stand with two- and three-dimensional junctions, including brickwork and joist ends. The calibration procedure is based on the definition of quantitative criteria (target values) that describe the accordance between measured and simulated data series. Starting with the thermal two-dimensional simulation model, uncertain parameters are defined with their start values, ranges and steps of variation. This is used as an input for the identification of the best-fitting parameter combination via generic optimization algorithm. The final calibrated simulation model shows a remarkably better accordance to the measurement results. The procedure is systematic and reproducible.

Evaluation of low-lift sensible cooling in the tropics using calibrated simulation models and preliminary testing

This article addresses an opportunity to improve Low Exergy air-conditioning system efficiencies in the tropics by incorporating ‘low-lift’ chillers. This paper builds on the 3for2 Project in Singapore, the region's lowest energy consuming office. Using calibrated energy models, laboratory measurements of the low-lift chiller and preliminary on-site testing, the performance of a low-lift chiller was studied in relation to a high-lift chiller. The paper concludes that the complete energy saving potential of high-temperature radiant cooling panels can be realized only if the high-temperature chilled water is produced directly by a low-lift chiller. The preliminary indicate that the peak and annual aggregate of radiant sensible cooling can be reduced by 22.8% and 13.5%

Dynamic Simulations supporting the Design Process of a real Combined Heat and Power Application in Switzerland

In many European countries the production of combined heat and power based on renewable energies is well established though the efficient and economical operation of such plants remains a challenging task. This also applies to the existing district heating network at Baden-Dättwil (Switzerland) where a conventional gas boiler is substituted by a wood-fired boiler comprising an Organic Rankine Cycle. An overall control strategy that allows fully exploring governmental incentives is therefore of paramount importance. In addition, the highly fluctuating heat demands combined with the thermal inertia of the different plant components impose demanding requirements to the control system to guarantee a stable as well as highly efficient operation.The overall control concept is successfully tested and verified by means of dynamic simulations of the overall plant with a simplified model for the district heating network. The models are implemented using the object oriented modeling language Modelica. The overall model is based on open source Modelica libraries such as ThermoCycle, Modelica Standard Library and StateGraph2 as well as on own Modelica models.The overall model is prepared to be coupled to the real plant control system which will allow virtual commissioning in the next step. This allows pre-tuning of control parameters as well as a weakness analysis which again helps to speed up the commissioning process. In General, the dynamic simulations proved to be a useful tool that deepened the insight and understanding of the plant operation at an early project phase and therefore greatly supported the making of design decisions. After commissioning, the calibrated simulation models will be used for monitoring purposes.

Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds

We propose a systematic approach for calibrating the interphase drag models used in mesoscale CFD simulations of gas-liquid flow through packed beds. The method uses packing specific liquid holdup and pressure drop data to find model coefficients that both minimize relative error in the two-phase momentum equations and ensure correct limiting single phase behavior of the model. The approach is demonstrated for three phenomenological drag models from the literature using data from a random Bialecki ring packing under counterflow conditions. Calibration results in a 2–3-fold reduction in the mean relative error for predicted pressure gradient/liquid holdup, relative to predictions with “standard” Ergun coefficients, when an idealized set of momentum equations are solved. The calibrated models are then implemented into a two-fluid CFD solver and used to perform a two-dimensional time accurate simulation of the same packed bed at high gas and liquid flowrates. The predicted mean flow properties are in very good agreement with macroscale experimental data, while the instantaneous liquid distributions show significant transient, axial and lateral nonuniformities.

Energy performance analysis of STILE house at the Solar Decathlon 2015: Lessons learned

West Virginia University and University of Rome Tor Vergata have partnered for the Solar Decathlon 2015 to present STILE (Sustainable Technologies Integrated in a Learning Experience), a house that merges Italian and Appalachian design concepts with innovative energy techniques.The aim of the work is the construction of a reliable and accurate dynamic building simulation model to perform a posteriori critical analysis of energy performance of the STILE house during the competition and to investigate the capability of the same house during a typical year in different locations. This was possible through the calibration of a dynamic simulation model of the house, using data gathered during the contest in Irvine, USA in October 2015. The work helped us to better understand the problems faced and to find optimized solutions that did not alter the original architectural concept. A 17% saving in the HVAC energy consumption for the period of competition was obtained by acting on shading, windows materials, and adequate floor insulation. The same model was also used to evaluate STILE behavior in different climatic conditions for a typical year. The study shows that the house has satisfactory thermal performance with mild and temperate climates, or whenever a benefit can be obtained by the use of the solar chimney.

Outdoor Characterization of Phase Change Materials and Assessment of Their Energy Saving Potential to Reach NZEB

Phase change materials (PCM) are very promising materials for improving energy efficiency in buildings, especially in hot weather conditions. In spite of the growing attention paid to the integration of PCM into buildings, there are few studies on PCM evaluation under real operating conditions. This lack of data often does not allow accurate calibration and validation of building simulation models. This work aims to characterize a commercial PCM panel by RUBITHERM®. The panel was laid on the floor of a test box exposed outdoors, and the experimental data were used to validate a PCM software tool implemented in IDA Indoor Climate and Energy software. A reference office building model with characteristics prescribed by Italian regulations (STD) was provided with three PCM with melting points of approximately 21 °C, 24 °C and 26 °C, laid on the floor office. The building energy performance obtained was compared to the energy performance of a reference building prescribed by the new Italian building energy performance regulation (NZEB) for three cities in Italy (Trento, Rome and Palermo). The results showed that energy savings obtained from implementing PCM in the STD building were not sufficient to reach the NZEB reference value for all cities. Only the use of night ventilation was able to assist in reaching NZEB. PCM with a 21 °C melting point showed the best annual energy saving performance in all cities. Temperature range and temperature peaks experienced by PCM in the day/night cycle can explain the behavior of these materials in the various cities and seasons as latent and sensible heat storage systems.

Field Trial of LTE eMBMS Network for TV Distribution: Experimental Results and Analysis

In recent years, the advances in mobile network technologies have revolutionized the paradigm how the users receive video and audio content. With the rise of smart phones and tablets having large screens, users have become more interested than anytime before in watching linear and non-linear TV programs on their mobile devices. This is because the users can fetch nowadays the content using their mobile devices at anytime and any place they are located. Facing this phenomenon, broadcasters are looking for new means to reach the users of digital natives that are interested in watching TV on their mobile devices. As most mobile devices are based on a global 3rd generation partnership project standard, the enhanced multimedia broadcast multicast system (eMBMS) of long term evolution (LTE) network is foreseen as one potential candidate for delivering broadcast services. In order to investigate the capability and limitations of today’s LTE eMBMS network, a field trial was carried out in Munich, Germany. This paper describes the setup of the field trial and presents a comprehensive analysis of the LTE eMBMS technology with respect to performance and future enhancements. The measurement data is compared against simulation models for field strength and path loss of each transmitter, eMBMS useful field strength and received power, and signal-to-interference and noise ratio (SINR). Moreover, the performance of cyclic prefix (CP) durations that are longer than the standardized values of 16.67 us and 33.33 us is evaluated using the calibrated simulation models. Results have shown that the simulation models can adequately predict the measurement data. Moreover, it is shown that a CP duration of 66.67 us provides a gain in SINR and achieves the best tradeoff in terms of maximum user velocity and performance in networks with limited inter-site distance.

Calibration of microscopic traffic simulation models using metaheuristic algorithms

Abstract This paper presents several metaheuristic algorithms to calibrate a microscopic traffic simulation model. The genetic algorithm (GA), Tabu Search (TS), and a combination of the GA and TS (i.e., warmed GA and warmed TS) are implemented and compared. A set of traffic data collected from the I-5 Freeway, Los Angles, California, is used. Objective functions are defined to minimize the difference between simulated and field traffic data which are built based on the flow and speed. Several car-following parameters in VISSIM, which can significantly affect the simulation outputs, are selected to calibrate. A better match to the field measurements is reached with the GA, TS, and warmed GA and TS when comparing with that only using the default parameters in VISSIM. Overall, TS performs very well and can be used to calibrate parameters. Combining metaheuristic algorithms clearly performs better and therefore is highly recommended for calibrating microscopic traffic simulation models.

An efficient soft computing-based calibration method for microscopic simulation models

ABSTRACTIn recent years, vehicular microscopic simulation models have become one of the main tools used by transportation professionals to analyze transportation policies and projects. Effective use of the existing simulation packages is limited by the calibration of specific parameters based on observed real-life conditions. However, because the calibration of the packages is a resource-intensive process, one might resort to using the default parameter values. In this study, a soft-computing based methodology is proposed that considerably reduces the computation time in comparison to other commonly used methods. The proposed methodology is based on a synergistic combination of artificial neural networks (ANN) and genetic algorithms (GA). First, a Latin hypercube sampling method is used to select representative sets of values for the simulation model's calibration parameters. Second, the effect of each set of parameter values on the simulated traffic stream speed is evaluated. Third, an ANN is trained to determine the relationship between the input parameter values and the output vehicular speed. Finally, a genetic algorithm uses the trained ANN to determine the calibration parameters. Applications of the proposed methodology shows that it allows for less time-consuming calibration of microscopic traffic models compared to other commonly used methods.

Sensitivity Analysis for Building Energy Simulation Model Calibration via Algorithmic Differentiation

High-fidelity building energy simulation models are software tools that are built on the well-established physical laws of thermal/hygric processes to model heating, cooling, lighting, ventilation, and energy use of buildings. They are highly nonlinear systems that involve a large number of subroutine calls and submodel switches during execution. To calibrate a building energy simulation model with good quality, parameter sensitivity analysis is well advocated, since it aims to identify those parameters in a specific building that hold more influence on the building thermal performance than others to facilitate shortening the lengthy cycle of model calibration procedures. Since simulation models are given in a large piece of source codes and encapsulate a series of submodels, the prevailing sensitivity analysis is mostly built within the framework of Monte Carlo simulation and statistics-based random sampling methods. It is computationally intensive. We propose to perform such analysis via a differential sensitivity analysis method that relies on the estimation of derivatives. A key technical challenge is that the high nonlinearity of the model prohibits any analytical differentiation, while numerical differentiation is too sensitive to step size and suffers from a truncation error. We, hence, propose to adopt an algorithmic differentiation method, which exploits the operator overload feature of object-oriented programs, to obtain accurate and robust numerical estimations of derivatives in an automated and computationally efficient way.

Robust design optimization of fixed-fixed beam piezoelectric energy harvester considering manufacturing uncertainties

The piezoelectric energy harvester, which accumulates electrical energy from ambient vibration energy has emerged as an alternative energy source because of its eco-friendly characteristics. Numerous studies have been conducted on the basis of experimental methods to increase the power generating capabilities of the piezoelectric energy harvester. Recently, research using optimization techniques to improve the performance has been carried out. However, since the previous studies did not consider various uncertainties that exist in the energy harvester, it is difficult to overcome the variance of performance caused by the uncertainties. In order to obtain the highest performance from the piezoelectric energy harvester and simultaneously retain robustness while installed under the road pavement, this paper presents robust design optimization of piezoelectric energy harvester which is insensitive to the uncertainties. Prior to performing the robust optimization, statistical information of the uncertainties that exist in the piezoelectric energy harvester was measured from 30 piezoelectric experiments. Then uncertainty quantification was performed using Akaike information criterion. A statistical model calibration method, used to improve the accuracy of the simulation model for robust optimization, was proposed and implemented, based on the uncertainty data. By using the calibrated simulation model, the piezoelectric energy harvester was evaluated and optimized to achieve maximum performance and robustness simultaneously. Finally, the robust optimum design solution for the piezoelectric energy harvester was verified, showing improved performance and reduced variance.

History matching of dual continuum reservoirs\u2014preserving consistency with the fracture model

Ensemble- and optimization-based parameter estimation is commonly used to calibrate simulation models of fractured reservoirs to measured data. Traditionally, statistical data on small-scale fractures are upscaled to a dual continuum model in a single step, and the subsequent history matching procedure makes adjustments to the upscaled parameters. In this paper, we show that the resulting reservoir models may be inconsistent with the initial fracture description, meaning that the reservoir parameters do not correspond to a physically valid combination of fracture parameters. A number of numerical examples is provided, which illustrate why and when the problem occurs. We utilize an invertible analytical fracture upscaling method, and deviations from the fracture model can thus be quantified in each case. We show that consistency with the fracture model is preserved if fracture parameters are history matched directly, if the relation between inversion variables and fracture parameters is linear, or if an unbiased Bayesian sampling method is used. We also show that preserving consistency is less important if the uncertainty of the fracture upscaling method is large.

Data requirement for effective calibration of process-based crop models

While process-based crop models are increasingly applied in multiple research areas, the role of model calibration and its data requirement have been rarely addressed. We used data for two canola cultivars measured across up to six sowing dates, five plant densities and three years to investigate how different data impacted on the efficacy of calibration of the process-based APSIM-Canola simulation model. A Bayesian optimisation method was used to derive cultivar parameters. Our results showed that model calibration using data from a single or multiple similar seasons would likely result in significant equifinality (i.e. multiple combinations of parameters leading to similar simulation accuracy) and simulation uncertainty. The most effective calibration was to use data from contrasting environments (at least two seasons), particularly with in-season growth measurements. Targeting model calibration to minimise simulation error (NRMSE) any less than the inherent error in measurement (13.5%) appeared to be pointless, because model calibration cannot reduce the simulation error to less than the error in the data used for calibration. Our rigorous calibration and validation procedure helped to identify APSIM’s deficiency to simulate the response of canola growth to changes in plant density. Future modelling work is warranted to quantify simulation uncertainty due to equifinality caused by model calibration against limited data, and improve modelling of density effects of canola in APSIM.