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Optimal uncertainty quantification with model uncertainty and legacy data
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A statistics modeling approach for the optimization of thin film photovoltaic devices
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Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation
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Steam Gasification Rates of Three Bituminous Coal Chars in an Entrained-Flow Reactor at Pressurized Conditions
Three bituminous coal chars (Illinois #6, Utah Skyline, and Pittsburgh #8) were gasified separately at total pressures of 10 and 15 atm in an entrained-flow reactor using gas temperatures up to 1830 K and particle residence times <240 ms. The experiments were performed at conditions where the majority of particle mass release was due to H 2 O gasification, although select experiments were performed at conditions where significant mass release was due to gasification by both H 2 O and CO 2 . The measured coal data we recorded were fit to three char gasification models including a simple first-order global model, as well as the CCK N and CCK models that stem from the CBK model. The optimal kinetic parameters for each of the three models are reported, and the steam reactivity of the coal chars at the studied conditions is as follows: Pittsburgh #8 > Utah Skyline > Illinois #6.
Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution
From hillslope to small catchment scales (< 50 km 2 ), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m 2 ) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Here, subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a – 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.
The Molecular Structure of a Phosphatidylserine Bilayer Determined by Scattering and Molecular Dynamics Simulations
Phosphatidylserine (PS) lipids play essential roles in biological processes, including enzyme activation and apoptosis. We report on the molecular structure and atomic scale interactions of a fluid bilayer composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylserine (POPS). A scattering density profile model, aided by molecular dynamics (MD) simulations, was developed to jointly refine different contrast small-angle neutron and X-ray scattering data, which yielded a lipid area of 62.7 A2 at 25 C. MD simulations with POPS lipid area constrained at different values were also performed using all-atom and aliphatic united-atom models. The optimal simulated bilayer was obtained using a model-free comparison approach. Examination of the simulated bilayer, which agrees best with the experimental scattering data, reveals a preferential interaction between Na+ ions and the terminal serine and phosphate moieties. Long-range inter-lipid interactions were identified, primarily between the positively charged ammonium, and the negatively charged carboxylic and phosphate oxygens. The area compressibility modulus KA of the POPS bilayer was derived by quantifying lipid area as a function of surface tension from area-constrained MD simulations. It was found that POPS bilayers possess a much larger KA than that of neutral phosphatidylcholine lipid bilayers. We propose that the unique molecular features of POPS bilayers may play an important role in certain physiological functions.
Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making
One of the most important factors in the operations of many cooperations today is to maximize profit and one important tool to that effect is the optimization of maintenance activities. Maintenance activities is at the largest level divided into two major areas, corrective maintenance (CM) and preventive maintenance (PM). When optimizing maintenance activities, by a maintenance plan or policy, we seek to find the best activities to perform at each point in time, be it PM or CM. We explore the use of stochastic simulation, genetic algorithms and other tools for solving complex maintenance planning optimization problems in terms of a suggested framework model based on discrete event simulation.
Optimization of the SWAT+ model to adequately predict different segments of a managed streamflow hydrograph
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Development of an Optical Library for Coevaporated CdSe x Te 1– x
The conversion efficiency of CdTe solar cells may be improved by bandgap engineering, i.e., changing the bandgap value through the addition of Se in the absorber. The Se alloying enables a short-circuit current density improvement, as it leads to a bandgap energy value decrease. Furthermore, it has been associated with increased minority carrier lifetimes, assuring high open-circuit voltage values. An Se gradient profile control can further optimize the solar cell performance. Thus, an optical model baseline of the CdSe x Te 1–x (CST) compound was developed. Spectroscopic ellipsometry measurements were conducted to accurately extract the optical constants of ten CST layers deposited through coevaporation with x varying from 0 to 1. Using the measured dielectric function spectra from the discrete CST layers with varying x, and considering the composition-induced shift in the critical point energies, an energy-shift model was employed to develop the accurate optical library for the CST compound for any x value to provide data for future modeling and optimization. Furthermore, the library accuracy was validated through optical simulations of the quantum efficiency of a graded CST solar cell using the finite-difference time-domain method by replicating the Se profile in the absorber layer measured through secondary ion mass spectrometry.
SEX-RATIO CONTROL IN A PARASITIC WASP, NASONIA VITRIPENNIS . II. EXPERIMENTAL ANALYSIS OF AN OPTIMAL SEX-RATIO MODEL
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A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model
We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, the Chemora framework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.
Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling
In this report we describe an enhanced methodology for performing stochastic Bayesian inversions of atmospheric trace gas inversions that allows the time variation of model parameters to be inferred. We use measurements of methane atmospheric mixing ratio made in Livermore, California along with atmospheric transport modeling and published prior estimates of emissions to estimate the regional emissions of methane and the temporal variations in inferred bias parameters. We compute Bayesian model evidence and continuous rank probability score to optimize the model with respect to temporal resolution. Using two different emissions inventories, we perform inversions for a series of models with increasing temporal resolution in the model bias representation. We show that temporal variation in the model bias can improve the model fit and can also increase the likelihood that the parameterization is appropriate, as measured by the Bayesian model evidence.
Modeling and Co-optimizing Integrated Transmission-Distribution Systems [Slides]
Abstract not provided.