Development and Verification of an Integrated Seawater Desalination and Renewable Energy System Model

This work investigates the modeling and verification of seawater reverse osmosis powered by renewable energy resources (SWRO-RES). The model includes one stage of RO membranes, high pressure (HP) pump, energy recovery devices (ERD), Wind turbines (WT), photovoltaic panels (PV), and electrical grid as a back-up for the cases when there is weak penetration of the RES. Antibugging and tracing for the computer model were used as part of the code verification to discover all coding errors and check whether the computer model conforms to the SWRO specifications. After that, the calculations verifications process was performed using sensitivity analysis (SA) to evaluate whether the model response follows the anticipated direction and to check the model at some extreme conditions. Four SA cases were implemented to evaluate the SWRO-RES freshwater production, freshwater concentration, recovery rate, and specific energy consumption (SEC). Case 1 was the SA for different feed pressure values. In this case, it is important to find the pressure value that gives the lowest freshwater concentration. Case 2 was the SA for different feedwater temperature values. While case 3 was the SA for the feedwater concentration. In all these cases, the model shows a verified response and as anticipated. The last SA case was to study the effect of different numbers of WT and PV panels and evaluate the electrical grid share considering one year of operation for the SWRO-RES plant.

Bayesian Calibrating Educational Building Thermal Models to Hourly Indoor Air Temperature: Methodology and Case Study

With the increase in the frequency and duration of heatwaves and extreme temperatures, global warming becomes one of the most critical environmental issues. Heatwaves pose significant threats to human health, including related diseases and deaths, especially for vulnerable groups. Such as the one during the 2018 summer in Montreal, Canada, caused up to 53 deaths, with most lived in buildings without access to air-conditioning. Unlike building energy models that mainly focus on energy performance, building thermal models emphasizes indoor thermal performance without a mechanical system. It is required an understanding of the complex dynamic building thermal physics in which detailed building parameters need to be specified but challenging to be determined in real life. The uncertainty assessment of the parameters estimates can make the results more reliable. Therefore, in this paper, a Bayesian-based calibration procedure was presented and applied to an educational building. First, the building was modeled in EnergyPlus based on an in-site visit and related information collection. Second, a sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. Then, a Meta-model was developed and used during the calibration process instead of the original EnergyPlus model to decrease the requirement of computing load and time. Subsequently, the Bayesian inference theory was employed to calibrate the model on hourly indoor air temperatures in summer. Finally, the model was validated. It is shown that the Bayesian calibration procedure not only can calibrate the model within the performance tolerance required by international building standards/codes but also predict future thermal performance with a confidence interval, which makes it more reliable.

Response Effects Due to Polygonal Representation of Pores in Porous Media Thermal Models

Physics models — such as thermal, structural, and fluid models — of engineering systems often incorporate a geometric aspect such that the model resembles the shape of the true system that it represents. However, the physical domain of the model is only a geometric representation of the true system, where geometric features are often simplified for convenience in model construction and to avoid added computational expense to running simulations. The process of simplifying or neglecting different aspects of the system geometry is sometimes referred to as “defeaturing.” Typically, modelers will choose to remove small features from the system model, such as fillets, holes, and fasteners. This simplification process can introduce inherent error into the computational model.Asimilar event can even take place when a computational mesh is generated, where smooth, curved features are represented by jagged, sharp geometries. The geometric representation and feature fidelity in a model can play a significant role in a corresponding simulation’s computational solution. In this paper, a porous material system — represented by a single porous unit cell — is considered. The system of interest is a two-dimensional square cell with a centered circular pore, ranging in porosity from 1% to 78%. However, the circular pore was represented geometrically by a series of regular polygons with number of sides ranging from 3 to 100. The system response quantity under investigation was the dimensionless effective thermal conductivity, k*, of the porous unit cell. The results show significant change in the resulting k* value depending on the number of polygon sides used to represent the circular pore. In order to mitigate the convolution of discretization error with this type of model form error, a series of five systematically refined meshes was used for each pore representation. Using the finite element method (FEM), the heat equation was solved numerically across the porous unit cell domain. Code verification was performed using the Method of Manufactured Solutions (MMS) to assess the order of accuracy of the implemented FEM. Likewise, solution verification was performed to estimate the numerical uncertainty due to discretization in the problem of interest. Specifically, a modern grid convergence index (GCI) approach was employed to estimate the numerical uncertainty on the systematically refined meshes. The results of the analyses presented in this paper illustrate the importance of understanding the effects of geometric representation in engineering models and can help to predict some model form error introduced by the model geometry.

Discretization Error Estimation in Subsonic, Transonic and Supersonic Flows of an Inviscid Fluid Over a Bump

This paper presents a solution verification exercise for the simulation of subsonic, transonic and supersonic flows of an inviscid fluid over a circular arc (bump). Numerical simulations are performed with a pressure-based, single-phase compressible flow solver. Sets of geometrically similar grids covering a wide range of refinement ratios have been generated. The goal of these grids is twofold: obtain a reference solution from power series expansion fits applied to the finest grids; check the numerical uncertainties obtained from coarse grids that do not guarantee monotonic convergence of the quantities of interest. The results show that even with very fine grids it is not straightforward to define a reference solution from power series expansions. The level of discretization errors required to obtain reliable reference solutions implies iterative errors reduced to machine accuracy, which may be extremely time consuming even in two-dimensional inviscid flows. Quantitative assessment of the estimated uncertainties for coarse grids depends on the selected reference solution.

Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches

Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.

VVS2021 Front Matter

The front matter for this proceedings is available by clicking on the PDF icon.

A Framework for In Silico Clinical Trials for Medical Devices Using Concepts From Model Verification, Validation, and Uncertainty Quantification (VVUQ)

The rising costs of clinical trials for medical devices in recent years has led to an increased interest in so-called in silico clinical trials, where simulation results are used to supplement or to replace those obtained from human patients. Here we present a framework for executing such a trial. This framework relies heavily on ideas already developed for model verification, validation, and uncertainty quantification. The framework uses results from an initial cohort of human patients as model validation data, recognizing that the best model credibility evidence usually comes from real patients. The validation exercise leads to an assessment of the model’s suitability based on pre-defined acceptance criteria. If the model meets these criteria, then no additional human patients are required and the study endpoints that can be addressed using the model are met using the simulation results. Conversely, if the model is found to be inadequate, it is abandoned, and the clinical study continues using only human patients in a second cohort. Compared to other frameworks described in the literature based on Bayesian methods, this approach follows a strict model build-validate-predict structure. It can handle epistemic uncertainties in the model inputs, which is a common trait of models of biomedical systems. Another idea discussed here is that the outputs of engineering models rarely coincide with measures that are the basis for clinical endpoints. This manuscript discusses how the link between the model and clinical measure can be established during the trial.

Error Quantification of Nusselt Number Analysis in Miniature Heat Sinks: Verification and Validation Assessment

Solution verification is performed to quantify the numerical uncertainty of Nusselt numbers in micro-scale heat sinks obtained from 3-D numerical simulations. A numerical procedure is first developed to calculate local and average Nusselt numbers along 4 different miniature heat sinks. Validation process is performed by comparing the obtained numerical results experimental data. Fairly good agreement between numerical results and experimental data confirms the reliability and accuracy of the proposed numerical procedure. The observed order of accuracy for water flow in a micro-tube with constant uniform heat flux is 1.81 while the observed order of accuracy for conjugate heat transfer of water flow within a microchannel heat sink is estimated as 1.2. The numerical uncertainty for local Nusselt numbers within the investigated microchannel heat sink is estimated to be 0.13.

Uncertainty Quantification of Deep Neural Network-Based Turbulence Model for Reactor Transient Analysis

Deep neural networks (DNNs) have demonstrated good performance in learning highly non-linear relationships in large datasets, thus have been considered as a promising surrogate modeling tool for parametric partial differential equations (PDEs). On the other hand, quantifying the predictive uncertainty in DNNs is still a challenging problem. The Bayesian neural network (BNN), a sophisticated method assuming the weights of the DNNs follow certain uncertainty distributions, is considered as a state-of-the-art method for the UQ of DNNs. However, the method is too computationally expensive to be used in complicated DNN architectures. In this work, we utilized two more methods for the UQ of complicated DNNs, i.e. Monte Carlo dropout and deep ensemble. Both methods are computationally efficient and scalable compared to BNN. We applied these two methods to a densely connected convolutional network, which is developed and trained as a coarse-mesh turbulence closure relation for reactor safety analysis. In comparison, the corresponding BNN with the same architecture is also developed and trained. The computational cost and uncertainty evaluation performance of these three UQ methods are comprehensively investigated. It is found that the deep ensemble method is able to produce reasonable uncertainty estimates with good scalability and relatively low computational cost compared to BNN.

Asymmetrical Heat Distribution Pattern in Miniature Heat Sinks Due to Conjugate Heat Transfer

Three-dimensional numerical simulations are performed to investigate the conjugate heat transfer of water within microchannel heat sinks. Validation process is performed through comparison between obtained numerical results and experimental data. The global deviation grid convergence index (GCI) is used to conduct solution verification and calculate observed order of accuracy. Conducted numerical analyses include hydraulic diameter range of 206–330 µm, aspect ratio of 1–4 and Reynolds numbers of 300 to 850. Heat is observed to distribute non-uniformly among microchannel side and bottom walls due to conjugate heat transfer. Results show that over 93% of heat is transferred to water through microchannel side walls at the aspect ratio of 4. It is observed that the heat distribution is more non-uniform destruction while microchannel aspect ratio gets larger.