Yunwei Sun

Bio: Yunwei Sun is an academic researcher from Lawrence Livermore National Laboratory. The author has contributed to research in topics: Geothermal energy & Sorption. The author has an hindex of 23, co-authored 87 publications receiving 2259 citations. Previous affiliations of Yunwei Sun include Pacific Northwest National Laboratory & Washington State University.

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Abstract: Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400-600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LP"@t (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.

Modeling Multispecies Reactive Transport in Ground Water

Abstract: In this paper, the details of RT3D, a general purpose, multispecies, reactive transport code, are presented. The code uses MODFLOW to simulate flow and several MT3D sub-programs to simulate advection and dispersion. A set of reaction modules were developed and incorporated into RT3D to simulate various types of multispecies reactive transport. This new computer model can be used for analyzing different types of subsurface contaminant reactions, microbial metabolisms, and microbial transport kinetics. Details of the model and numerical solution procedure are presented. The numerical formulation of the code is general enough to allow description of any type of reaction with any number of mobile/immobile species. Several example problems are presented to test the performance of the code, and to illustrate its features. The presented numerical model is shown to be a useful tool for analyzing different types of subsurface bioremediation systems. Prediction based on this model can be used for screening remediation alternatives including natural attenuation and/or for forecasting contaminant exposure levels and environmental risks at sensitive, downgradient receptors.

Development of analytical solutions for multispecies transport with serial and parallel reactions

Abstract: A direct method for transforming multiple solute transport equations, coupled by linear, series, and/or parallel first-order, irreversible reactions, into a series of simple transport equations having known solutions is developed. Using this method, previously published analytical solutions to single-species transport problems, in which the transported species reacts with first-order kinetics, can be used to derive analytical solutions to multispecies transport systems with parallel, serial, and combined reaction networks. This new method overcomes many of the limitations that were implicit in previously published methods. In particular, the number of species that can be described is unlimited, and the reaction stoichiometry does not have to be unimolar. To illustrate the method, an analytical solution is derived for a five-species serial-parallel reactive transport system. The analytical solution obtained for this problem is compared with a numerical solution obtained with a previously developed code. This analytical method is applicable to the verification of new numerical codes.

Carbon capture and storage (CCS): the way forward

Abstract: Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

A Matlab toolbox for Global Sensitivity Analysis

Abstract: Global Sensitivity Analysis (GSA) is increasingly used in the development and assessment of environmental models. Here we present a Matlab/Octave toolbox for the application of GSA, called SAFE (Sensitivity Analysis For Everybody). It implements several established GSA methods and allows for easily integrating others. All methods implemented in SAFE support the assessment of the robustness and convergence of sensitivity indices. Furthermore, SAFE includes numerous visualisation tools for the effective investigation and communication of GSA results. The toolbox is designed to make GSA accessible to non-specialist users, and to provide a fully commented code for more experienced users to complement their own tools. The documentation includes a set of workflow scripts with practical guidelines on how to apply GSA and how to use the toolbox. SAFE is open source and freely available for academic and non-commercial purpose. Ultimately, SAFE aims at contributing towards improving the diffusion and quality of GSA practice in the environmental modelling community. SAFE implements several GSA methods and can easily integrate new ones.SAFE facilitates assessment of robustness/convergence and effective visualization.SAFE embeds good practice guidelines through workflow scripts.SAFE is intended for both non-specialists users and SA developers.