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DOE OSTI2021

Real-Time Dispatch With Secondary Frequency Regulation: A Pathway to Consider Intra-Interval Fluctuations

Real-time dispatch balances the power demand with minimized operating costs. For the current dispatch model, the demand is assumed to be constant within a time interval, while the intra-interval power balance is left to frequency regulation. Based on practical experience and simulations, this behavior may lead to insufficient frequency regulation and uneconomic regulation costs considering the increase in intra-interval fluctuations caused by renewables. Here, a real-time dispatch method with secondary frequency regulation behaviors is proposed. Without changing the interval of the real-time dispatch command, the system regulation mileage and intra-interval generation adjustment are explicitly formulated in a mixed-integer optimization model. To reduce the computational burden, an efficient two-stage calculation method is proposed. With practical utility data, this article finds that the proposed method can effectively improve the system frequency performance with the subminute net load forecasting curve obtained by an off-the-shelf forecasting approach. Time-domain simulations in IEEE and practical utility systems validate the effectiveness of the proposed method in terms of the frequency performance and total operating costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

The Role of Mixed Amine/Amide Ligation in Nickel Superoxide Dismutase

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99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗

DOE OSTI2013

Integrating Solar PV in Utility System Operations

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, and day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.

14 SOLAR ENERGY↗

DOE OSTI2016

Evaluating the Community Land Model in a pine stand with shading manipulations and 13 CO 2 labeling

Carbon partitioning and flow through ecosystems regulates land surface atmosphere CO 2 exchange and thus is a key, albeit uncertain component of mechanistic models. The Partitioning in Trees and Soil (PiTS) experiment-model project tracked C partitioning through a young Pinus taeda stand following pulse-labeling with 13 CO 2 and two levels of shading. The field component of this project provided process-oriented data that was used to evaluate and improve terrestrial biosphere model simulations of rapid shifts in carbon partitioning and hydrological dynamics under varying environmental conditions. Here we tested the performance of the Community Land Model version 4 (CLM4) in capturing short-term carbon and water dynamics in relation to manipulative shading treatments, and the timing and magnitude of carbon fluxes through various compartments of the ecosystem. To constrain CLM4 to closely simulate pretreatment conditions, we calibrated select model parameters with the pretreatment observational data. Compared to CLM4 simulations with default parameters, CLM4 with calibrated model parameters was better able to simulate pretreatment vegetation carbon pools, light response curves, and other initial states and fluxes of carbon and water. Over a 3-week treatment period, the calibrated CLM4 generally reproduced the impacts of shading on average soil moisture at 15-95 cm depth, transpiration, relative change in stem carbon, and soil CO 2 efflux rate, although some discrepancies in the estimation of magnitudes and temporal evolutions existed. CLM4, however, was not able to track the progression of the 13 CO 2 label from the atmosphere through foliage, phloem, roots or surface soil CO 2 efflux, even when optimized model parameters were used. This model bias arises, in part, from the lack of a short-term non-structural carbohydrate storage pool and progressive timing of within-plant transport, thus indicating a need for future work to improve the allocation routines in CLM4. Overall, these types of detailed evaluations of CLM4, paired with intensive field manipulations, can help to identify model strengths and weaknesses, model uncertainties, and additional observations necessary for future model development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets

This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.

Majidi, Majid↗

DOE OSTI2018

Impact of Model Simplification on Optimal Control of Combustion Engine and Electric Vehicles Considering Control Input Constraints

This paper analyzes the impact of model simplification on optimal control for electric and conventional engine-powered vehicles. An optimal control problem is formulated to minimize energy/fuel consumption subject to control input constraints and solved analytically using the Pontryagin's Minimum Principle. We found that simplified solutions without nonlinear aerodynamic drag are sub-optimal, and their loss of optimality increases with average travel speed. The energy/fuel savings of each control approach are evaluated using the intelligent driver model to capture driving behavior of a driver. Simulation case studies are presented for illustration.

Han, Jihun↗

DOE OSTI2019

Subgrid modelling for two-dimensional turbulence using neural networks

Here in this investigation, a data-driven turbulence closure framework is introduced and deployed for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the subgrid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a priori and a posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability-density-function-based validation of subgrid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for subgrid quantity inference. In addition, it is also observed that some measure of a posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.

42 ENGINEERING↗

DOE OSTI2017

A graph-based computational framework for simulation and optimisation of coupled infrastructure networks

Here, we present a computational framework that facilitates the construction, instantiation, and analysis of large-scale optimization and simulation applications of coupled energy networks. The framework integrates the optimization modeling package PLASMO and the simulation package DMNetwork (built around PETSc). These tools use a common graphbased abstraction that enables us to achieve compatibility between data structures and to build applications that use network models of different physical fidelity. We also describe how to embed these tools within complex computational workflows using SWIFT, which is a tool that facilitates parallel execution of multiple simulation runs and management of input and output data. We discuss how to use these capabilities to target coupled natural gas and electricity systems.

42 ENGINEERING↗

DOE OSTI2013

Greenhouse Gas Mitigation Options in ISEEM Global Energy Model: 2010-2050 Scenario Analysis for Least-Cost Carbon Reduction in Iron and Steel Sector

The goal of the modeling work carried out in this project was to quantify long-term scenarios for the future emission reduction potentials in the iron and steel sector. The main focus of the project is to examine the impacts of carbon reduction options in the U.S. iron and steel sector under a set of selected scenarios. In order to advance the understanding of carbon emission reduction potential on the national and global scales, and to evaluate the regional impacts of potential U.S. mitigation strategies (e.g., commodity and carbon trading), we also included and examined the carbon reduction scenarios in China’s and India’s iron and steel sectors in this project. For this purpose, a new bottom-up energy modeling framework, the Industrial Sector Energy Efficiency Modeling (ISEEM), (Karali et al. 2012) was used to provide detailed annual projections starting from 2010 through 2050. We used the ISEEM modeling framework to carry out detailed analysis, on a country-by-country basis, for the U.S., China’s, and India’s iron and steel sectors. The ISEEM model applicable to iron and steel section, called ISEEM-IS, is developed to estimate and evaluate carbon emissions scenarios under several alternative mitigation options - including policies (e.g., carbon caps), commodity trading, and carbon trading. The projections will help us to better understand emission reduction potentials with technological and economic implications. The database for input of ISEEM-IS model consists of data and information compiled from various resources such as World Steel Association (WSA), the U.S. Geological Survey (USGS), China Steel Year Books, India Bureau of Mines (IBM), Energy Information Administration (EIA), and recent LBNL studies on bottom-up techno-economic analysis of energy efficiency measures in the iron and steel sector of the U.S., China, and India, including long-term steel production in China. In the ISEEM-IS model, production technology and manufacturing details are represented, in addition to the extensive data compiled from recent studies on bottom-up representation of efficiency measures for the sector. We also defined various mitigation scenarios including long-term production trends to project country-specific production, energy use, trading, carbon emissions, and costs of mitigation. Such analyses can provide useful information to assist policy-makers when considering and shaping future emissions mitigation strategies and policies. The technical objective is to analyze the costs of production and CO 2 emission reduction in the U.S, China, and India’s iron and steel sectors under different emission reduction scenarios, using the ISEEM-IS as a cost optimization model. The scenarios included in this project correspond to various CO 2 emission reduction targets for the iron and steel sector under different strategies such as simple CO 2 emission caps (e.g., specific reduction goals), emission reduction via commodity trading, and emission reduction via carbon trading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2009

Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

Rios, Joseph Lucio↗

NASA NTRS1994

Asymptotic Giant Branch stars as a source of short-lived radioactive nuclei in the solar nebula

We carried out a theoretical evaluation of the contribution of Asymptotic Giant Branch (AGB) stars to some short-lived (10(exp 6) less than or equal to Tau-bar less than or equal to 2 x 10(exp 7) yr) isotopes in the Interstellar Medium (ISM) and in the early solar system using stellar model calculations for thermally pulsing evolutionary phases of low-mass stars. The yields of s-process nuclei in the convective He-shell for different neutron exposures tau(sub 0) were obtained, and AGB stars were shown to produce several radioactive nuclei (especially Pd-107, Pb-205, Fe-60, Zr-93, Tc-99, Cs-135, and Hf-182) in diferent amounts. Assuming either contamination of the solar nebula from a single AGB star or models for continuous injection and mixing from many stars into the ISM, we calculate the ratios of radioactive to stable nuclei at the epoch of the Sun's formation. The dilution factor between the AGB ejecta and the early solar system matter is obtained by matching the observed Pd-107/Pd-108 and depends on the value of tau(sub 0). It is found that small masses M(sub He) of He-shell material (10(exp -4)-10(exp -7) solar mass) enriched in s-process nuclei are sufficient to contaminate 1 solar mass of the ISM to produce the Pd-107 found in the early solar system. Predictions are made for all of the other radioactive isotopes. The optimal model to explain several observed radioactive species at different states of the proto-solar nebula involves a single AGB star with a low neutron exposure (tau(sub 0) = 0.03 mbarn(sup -1)) which contaminated the cloud with a dilution factor of M(sub He)/solar mass approximately 1.5 x 10(exp -4). This will also contribute newly synthesized stable s-process nuclei in the amount of approximately 10(exp -4) of their abundances already present in the proto-solar cloud. Variations in the degree of homogenization (approximately 30%) of the injected material may account for some of the small general isotopic anomalies found in meteorites. It is also found that Fe-60 is produced in small but significant quantities that may be sufficient to explain the observations if the time elapsed delta from the contamination of the ISM to the formation of protoplanetary bodies is not higher than delta = 5 x 10(exp 6) yr. If delta is longer, up to 10 x 10(exp 6) yr, this would require the single AGB star to experience enhanced neutron densities (n(sub n) approximately 3 x 10(exp 9)n/cu cm) in the s-processing zone in order to compensate for the branching at Fe-59. The alternative model of long-term continuous ejection of matter from many AGB stars does not appear to match the observations. We also estimate the Al-26 production from the H-shell and find that the Al-26 abundance in the early solar system may be readily explained in a self-consistent manner. Moreover, Al-26 from AGB stars may contribute substantially to the galactic Al-26 gamma-source, while no significant gamma-flux from Co-60 (deriving from Fe-60 decay) is to be expected.

Wasserburg, G. J.↗

DOE OSTI2025

Networked Microgrid Topology Reconfiguration to Promote Fairness in Proactive Load Shedding

Increasing occurrences of natural disasters and grid emergency events consistently challenge the safe and reliable operations of power systems. During such emergency situations, system operators may proactively shed load to mitigate risks. However, uncoordinated implementation of load shedding may disrupt electricity supply and even lead to cascading failures. Meanwhile, it is crucial to address potential biases affecting different customers when executing load shedding. This paper addresses the dynamic topology reconfiguration problem for networked microgrids with distributed energy resources under emergency conditions. Specifically, we propose a novel rolling-horizon optimization model that integrates fairness-aware constraints into the networked microgrid topology reconfiguration. Unlike existing approaches that focus solely on efficiency or apply fairness considerations in static settings, our method explicitly incorporates temporal fairness constraints to restrict repeated or excessive load curtailment for load blocks. Moreover, the fairness-aware constraints are specifically developed for the context of dynamic networked microgrid topology reconfiguration, and are designed to be convex or amenable to linear reformulations, which offers a more tractable alternative to traditional models with non-convex formulations. Numerical studies on a modified IEEE 13-bus system and a larger-sized SMART-DS networked microgrid system demonstrate the performance of the proposed algorithm towards more fairness-aware networked microgrid topology reconfiguration decision-making.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Particle-based fast jet simulation at the LHC with variational autoencoders

Abstract We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a deep VAE to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Efficient discrimination of natural stereoisomers of chicoric acid, an HIV-1 integrase inhibitor

Plants from the Asteraceae family are known to contain a wide spectrum of phytochemicals with various nutraceutical properties. One important phytochemical, chicoric acid (CA), is reported to exist in plants, such as Sonchus oleraceus and Bidens pilosa, as stereoisomers. They occur either as the naturally abundant RR-chicoric acid (RR-CA), or the less abundant RS-chicoric acid (RS-CA), also known as meso-chicoric acid. To date, little is known about the biological activity of RS-CA, but there is evidence of its anti-HIV (human immunodeficiency virus) properties. In this study, a reliable analytical method was developed to distinguish between the two stereoisomers detected in S. oleraceus and B. pilosa. Structure identification used liquid chromatography–mass spectrometry (LC-MS) in combination with ultraviolet radiation (UV) -induced geometrical isomerization, molecular dynamics (MD) simulations, and density functional theory (DFT) models. Optimized structures from DFT calculations were used for docking studies against the HIV-1 integrase enzyme. Different retention times on the reverse phase chromatograms revealed the plants produce two different CA stereoisomers: S. oleraceus produced the RR-CA isomer, while B. pilosa produced the RS-CA isomer. DFT results demonstrated the RR-CA molecule was more stable than RS-CA due to the stabilizing force of intra-molecular hydrogen bonding. Differences in the HIV-1 integrase enzyme binding modes were observed, with the RR-CA being a more potent inhibitor than the RS-CA molecule. The results highlight the significance of plant metabolite structural complexity from both chemical and biological perspectives. Artificial formation of geometrical isomers, in combination with the predictive ability of DFT models and the resolving power of the LC-MS, can be exploited to distinguish closely related compounds, in particular stereoisomers.

chicoric acid, RR-chicoric acid, RS-chicoric acid,↗

DOE OSTI2016

WE-AB-207B-11: Optimizing Tumor Control Probability in Radiation Therapy Treatment - Application to HDR Cervical Cancer

Purpose: The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor-control-probability(TCP) with an acceptable normal-tissue-complication probability(NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. We design treatment plans that optimize TCP directly and contrast them with the clinical dose-based plans. PET image is incorporated to evaluate gain in TCP for dose escalation. Methods: We build a nonlinear mixed integer programming optimization model that maximizes TCP directly while satisfying the dose requirements on the targeted organ and healthy tissues. The solution strategy first fits the TCP function with a piecewise-linear approximation, then solves the problem that maximizes the piecewise linear approximation of TCP, and finally performs a local neighborhood search to improve the TCP value. To gauge the feasibility, characteristics, and potential benefit of PET-image guided dose escalation, initial validation consists of fifteen cervical cancer HDR patient cases. These patients have all received prior 45Gy of external radiation dose. For both escalated strategies, we consider 35Gy PTV-dose, and two variations (37Gy-boost to BTV vs 40Gy-boost) to PET-image-pockets. Results: TCP for standard clinical plans range from 59.4% - 63.6%. TCP for dose-based PET-guided escalated-dose-plan ranges from 63.8%–98.6% for all patients; whereas TCP-optimized plans achieves over 91% for all patients. There is marginal difference in TCP among those with 37Gy-boosted vs 40Gy-boosted. There is no increase in rectum and bladder dose among all plans. Conclusion: Optimizing TCP directly results in highly conformed treatment plans. The TCP-optimized plan is individualized based on the biological PET-image of the patients. The TCP-optimization framework is generalizable and has been applied successfully to other external-beam delivery modalities. A clinical trial is on-going to gauge the clinical significance. Partially supported by the National Science Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

The objective of this work is to provide a valuable database from controlled experiments for validating computational models. Recently, coarse-grained techniques such as particle-in-cell (PIC) or coarse-grained discrete element modeling (DEM) have gained popularity due to their computational efficiency while modeling large-scale systems; however, the influence of model parameters and their sensitivities at different geometric scales and flow conditions remain to be analyzed. These datasets are critical for the multiphase flow research community to assess predictive capability of modeling techniques as well as elucidate the hydrodynamic behavior in these systems. This study performed fluidization experiments using three different test sections with internal diameters of 2.5, 4, and 6 in. The operating conditions, bed material, and range of flow velocities at the inlet were constant in all the units, which were not hydrodynamically scaled. Glass beads having a Sauter Mean Diameter of 332 μm were used. Superficial velocity was varied from 2.97 to 5.35 times the minimum fluidization velocity and the initial static bed height was 0.1524 m. The order in which the experiments were performed was randomized and replicates were included to provide uncertainty in measurements. Statistics of differential pressure and bed height from these tests were reported. Future plans include validating PIC methodology in the open-source software, MFiX (Multiphase Flow with Interphase Exchanges) using results from this study. This could further be extended to determine optimal model parameters using inverse techniques such as deterministic calibration or Bayesian inference.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

42 ENGINEERING↗