An approachable problem for the Bayesian analysis of a variety of uncertainties in hierarchical physical models
Explore the source record for details and available documents.
Validation/Uncertainty-Quantification↗
Engineering topics
Smith, Philip J.
Publications and source records attributed to Smith, Philip J..
Strengthening the US Department of Energy’s Recruitment Pipeline: The DOE/NNSA Predictive Science Academic Alliance Program (PSAAP) Experience
The US Department of Energy (DOE) oversees a system of 17 national laboratories responsible for developing unique scientific capabilities beyond the scope of academic and industrial institutions. These labs strive to keep America at the forefront of discovery and are home to some of the Nation’s best minds and the world’s best scientific and research facilities. Collaborations between national laboratories and academic institutions are critical to develop and recruit talent for the DOE workforce. Academia’s cooperative education model poses challenges for DOE recruitment pipelines centered around traditional internships. This paper discusses a promising DOE recruitment pipeline, the National Nuclear Security Administration’s (NNSA) Predictive Science Academic Alliance Program (PSAAP) initiative. As a part of this, experiences capturing the successes and challenges faced by the University of Utah’s Carbon Capture Multidisciplinary Simulation Center (CCMSC) through their participation in the PSAAP-II initiative are shared. These experiences demonstrate the success of Utah’s PSAAP center as a recruitment pipeline with approximately 43% of CCMSC students going to a national laboratory after graduation. Potential opportunities to strengthen the DOE’s recruitment pipeline are also discussed.
Holmen, John↗
Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory
Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.
25 ENERGY STORAGE↗
Atikokan Digital Twin: Machine learning in a biomass energy system
The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.
09 BIOMASS FUELS↗
Numerical analysis of particle dispersion and deposition in coal combustion using large-eddy simulation
High-fidelity modeling provides a useful approach to investigate the particle dispersion and deposition mechanism in pulverized coal combustion. To be able to analyze these detailed mechanisms, this work couples detailed multiphysics models for poly-dispersed turbulent, particle-laden flow, particle and gas phase reaction chemistry, convective, conductive and radiative heat transfer, ash formation and deposition with a Large-Eddy Simulation (LES) approach to numerically simulate coal combustion in a downfired self-sustained oxy-fuel combustor (OFC). It is necessary to explicitly capture all but the highest frequency dynamics of the turbulence and its coupling with each of the other physical phenomena using LES. Effects of subgrid-scale unresolved turbulence on the particle motions are also analyzed by a novel Stokes number analysis. Due to the inherent relation ship between coal combustion and ash deposition, this study integrates the improved ash deposition model into the numerical simulation. Overall simulation results are compared with experimentally measured data from the OFC. The simulation and measured data for the averaged gas temperature and deposition rates agree with 5% and 28%. This study shows that high-fidelity LES coupled with other detailed multiphysics models running on a exascale computing facility can provide a good representation of complex coal combustion and deposition in a laboratory-scale furnace.
multiphysics model↗
Large-eddy simulation of coal combustion in 15 MW pilot-scale boiler-simulation facility
Not provided.
Engineering↗
Investigations of degradation and encapsulation of plastic scintillator
Studies have revealed that plastic scintillators such as polyvinyl toluene and polystyrene can undergo environmentally related material damage (“fogging”) that adversely affects detection performance under certain conditions and histories. A significant decrease in sensitivity has been seen in some gamma-ray detectors as they age as a result of this damage. Performance degradation due to such damage is characterized by a signal change from the detector, which shifts to lower energy, and ultimately a reduction in the ability to detect gamma radiation. This degradation is due to the permeation of water into the plastic, which can then cause temporary fogging and permanent damage to the material. As an example, a 1-mm thick barrier of high-density polyethylene with an area of 1 m 2 would allow 5 g per year of water transmission at 100% relative humidity. Thus, significant amounts of water can penetrate plastics over time and provide the potential for damage to the plastic. Such damage to plastic scintillator can cause problems for radiation detection applications in uncontrolled environments. Mitigation approaches that have been proposed to prevent damage to plastic scintillator include encapsulation of the plastic scintillator to prevent water intrusion that leads to damage during cold cycles. It is concluded that an encapsulation material with a moisture vapor transmission rate on the order of 10 -4 g∙m -2 ∙d -1 or better is needed for protecting large pieces of plastic scintillator from fogging over years of use. This article presents information on testing of bare and encapsulated plastic scintillator samples and on diagnostic approaches to measure the nature and progression of the fogging condition. It is shown that several encapsulation approaches fail to prevent water intrusion, while a couple methods can successfully encapsulate plastic and prevent water from penetrating the plastic and producing fogging.
36 MATERIALS SCIENCE↗
Large-eddy simulation of ash deposition in a large-scale laboratory furnace
Not Available
Zhou, Min-min (ORCID:0000000336945404)↗
Application of LES-CFD for predicting pulverized-coal working conditions after installation of NOx control system
The upcoming environmental requirements outlined at the 2017 European Commission Integrated Pollution Prevention and Control (IPPC) are becoming more and more strict in comparison to the existing standards – IED 2010/75/EU. Questions arise about changes in boiler operating conditions after the adoption of the regulations to fulfill specified emission limits. Of initial concern is the question of which technology to pursue in order to reach the specified level of NOx emissions at 150 mg/Nm 3 @6%O 2 . By introducing an air staging technique the NOx limit can be partially achieved; however these changes affect the boiler operation in a detrimental way. In order to investigate impact of de-NOx installation on the temperature and heat flux distribution, numerical techniques can be used. In order to determine the impact of the changes in the boiler operation, here this paper presents the application of an advanced, multiphase open-source code Arches, developed at the University of Utah for modeling pulverized-coal combustion using Large-Eddy Simulation (LES). Numerical simulations showed the effect on the boiler's working parameters e.g., heat-transfer rate, temperature distribution, species concentration, and NOx emission before and after proposed modifications.
54 ENVIRONMENTAL SCIENCES↗
Application of Bound-to-Bound Data Collaboration approach for development and uncertainty quantification of a reduced char combustion model
The development of efficient industrial oxy-coal boilers can be significantly aided by Computational Fluid Dynamics (CFD) tools, as far as fidelity in modeling coal combustion is also complemented by feasible computational costs. Reduced and predictive models are the most suitable for this application scale. Reduced models feature predictivity when they are validated against a broad range of experiments and targeted by Uncertainty Quantification (UQ) procedures. This work proposes a numerical procedure that uses Bound-to-Bound Data Collaboration (B2B-DC) to derive a reduced char combustion model describing transport phenomena and reactions between char carbon and O 2 , CO 2 and H 2 O, in both conventional and oxy-conditions. The approach determines the consistency between a numerical model and an experimental dataset. The latter is made up of the experiments carried out in an optically accessible laminar entrained flow reactor, operated by Sandia National Laboratories. The procedure follows five steps towards predictive modeling capability, namely: quantification of the uncertainty in the experiments, via instrument verification and modeling; development of a physics model and continuous improvement of its fidelity, via model-form uncertainty; identification of the uncertain and most sensitive parameters and of their prior bounds; sampling of the initial uncertain parameter space and training of a surrogate model; validation of the physics model via inference from the data. The last step, also known as inverse problem, is performed by applying the Bound-to-Bound Data Collaboration approach. A char combustion model is found consistent with the experimental data and its validity stands for conventional and oxy-combustion conditions. It accounts for heterogeneous reactions at the particle surface, mass transport of species in the particle boundary layer, pore diffusion and surface area changes. The consistent reduced model overcomes the differences in mass transport and kinetics observed in the experimental campaign. In conclusion, a reduction of the initial degree of uncertainty in both model and experiments is achieved.
42 ENGINEERING↗
Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions
Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.
01 COAL, LIGNITE, AND PEAT↗
PVT Degradation Studies: Acoustic Diagnostics
Under certain environmental conditions, polyvinyl toluene (PVT) plastic scintillator has been observed to undergo internal fogging. This document reports on a study of acoustic techniques to determine whether they can provide a diagnostic for the fogging of PVT. Different ultrasound techniques were employed for detecting the level of internal fogging in PVT, including wave velocity measurements, attenuation, nonlinear acoustics, and acoustic microscopy. The results indicate that there are linear relations between the wave velocity and wave attenuation with the level of internal fogging. The effects of fogging on ultrasound wave attenuation is further verified by acoustic microscopy imaging, where regions with fog in the specimen demonstration higher levels of attenuation compared to clear regions. Results from the nonlinear ultrasound measurements were inconclusive due to high sensitivities to transducer coupling and fixture variabilities.
36 MATERIALS SCIENCE↗
CFD-aided benchmark assessment of coal devolatilization one-step models in oxy-coal combustion conditions
Coal devolatilization, as first step in thermochemical processes involving solid fuels, has been targeted in plenty of CFD investigations: despite the variety of models that were developed to define the rate of the process and the amount of volatile matter released, not all of them are suitable for CFD simulations of large-scale coal combustors. Empirical models ensure both simplicity and feasibility, but not full reliability for all coal types and operating conditions. CFD modeling results for coal conversion during devolatilization in an entrained flow reactor and oxy-conditions are presented and compared to experimental data. A benchmark assessment of one-step models on the accuracy of the volatile yield prediction is then performed. It is shown that the heating rate strongly influences the devolatilization process and its modeling, not only in terms of rate but also in terms of yield, which differs significantly from the proximate analysis value. Furthermore, this indicates the need for a model able to predict the variation of coal volatile yield with temperature and heating rate, in addition to the kinetic model.
01 COAL, LIGNITE, AND PEAT↗
Underground coal thermal treatment as a potential low-carbon energy source
Not Available
Kelly, Kerry E.↗
Advanced regression methods for combustion modelling using principal components
Not Available
Isaac, Benjamin J.↗
Alternative Architectures for Distributed Work in the National Airspace System
The architecture for the National Airspace System (NAS) in the United States has evolved over time to rely heavily on the distribution of tasks and control authority in order to keep cognitive complexity manageable for any one individual. This paper characterizes a number of different subsystems that have been recently incorporated in the NAS. The goal of this discussion is to begin to identify the critical parameters defining the differences among alternative architectures in terms of the locus of control and in terms of access to relevant data and knowledge. At an abstract level, this analysis can be described as an effort to describe alternative "rules of the game" for the NAS.
Smith, Philip J.↗
Preliminary Findings: Issues in Surface Movement
The final report for the grant is presented. The recent goals for this project have been: (1) To identify common surface movement challenges which affect the airlines and Air Traffic Control; (2) To map out possible solutions to these challenges; (3) To start generalizing about the information we are receiving so that major, abstract categories of challenges and potential solutions will begin to emerge. In particular, there are several areas of opportunity which are beginning to emerge from the data, dealing with the need for: (1) Tools to support information exchange regarding priorities (both within an individual airline and between the ATC tower and airlines). Such priorities include both concerns affecting departure throughput as well as the ordering of departures to accommodate other airline considerations; (2) Planning tools to help ATC and airline Ramp staff deal with information about priorities; (3) Implementation of strategies to enable greater flexibility in queueing flights for departures; (4) Tools to provide better coordination and situation awareness during taxiing (within an airline as well as between airlines and between the airlines); (5) Tools to support planning and to deal with the interactions between departures and arrivals. Thus far, the initial interviews and observations at three airlines and two ATC facilities have been completed.
Smith, Philip J.↗
Human-Centered Automation: The View from Earth Versus the View from Mars
This panel addresses the topic of human-centered automation in, however, a very different context-manned missions to deep space in which predominantly autonomous systems must control the crew's life-support systems most of the time. The Mars spacecraft and its inhabitants are totally dependent on the proper operation of thousands of control loops and pieces of equipment to maintain the delicate, unbuffered homeostasis of their remote existence. Mission success hangs on a thread that depends on a successful partnership, or symbiosis. between crew and machines, which exceeds anything ever required of ground systems. The need for a new approach to human-centered automation is not a "frill" in future Mars mission, but rather a sine qua non of survival. Thus the issue is design: Can we design systems intended to operate in a predominantly autonomous mode. without a human backup, that are sufficiently safe such that human travel into deep space is possible?
Massimino, Michael J.↗