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DOE OSTI2016

Apparent Monte Carlo Source Convergence Problem with BWR Fuel Depleted with Partial Control Blade Insertion

Oak Ridge National Laboratory (ORNL) and the US Nuclear Regulatory Commission (NRC) have initiated a multiyear project to investigate application of burnup credit (BUC) to boiling-water reactor (BWR) fuel in storage and transportation casks and spent fuel pools (SFPs). Ideally, a single combination of all operating parameters including moderator density and axial burnup profiles, control blade exposure, and others can be identified to allow a simple, bounding approach to BWR BUC. One aspect of this investigation involves examining the effect of control blade insertion during operation on discharged assembly reactivity. In the course of these calculations, an apparent source convergence problem was discovered for fuel that has experienced partial blade insertion for the entire lifetime of the assembly. The source convergence problem appears not to impact k{sub eff}, but it manifests in predictions of the axial fission density profile. This phenomenon is of interest in identifying areas of concern with BWR BUC since it has not been studied as thoroughly as BUC for pressurized-water (PWR) reactor fuel. There may also be interest in this configuration for further study of a realistic source Monte Carlo convergence problem as opposed to some earlier manufactured source convergence problems through careful selection of initial conditions. This summary presents the depletion conditions used to generate the problem, a description of the models, a background based on well-behaved calculations, the identification of the problem, and attempts made to resolve the problem. Only a brief description of the depletion conditions and models is possible within the limited scope of this summary. Finally, conclusions are presented. This summary identifies a source convergence problem in the process of investigating BUC for BWR spent fuel. The depletion scenario which created the depleted fuel and burnable absorber compositions included a control blade inserted in the bottom 14 nodes throughout the entire depletion. This insertion is slightly over half the length of the assembly, and it appears to create two regions of nearly equal reactivity. As expected, one region is near the top of the assembly, and the other region is near the middle of the assembly. This node includes significant {sup 239}Pu generation as a result of rodded operation, but it is also the top node of the full lattice zone. While the calculated k{sub eff} values appear to be statistically equivalent, the axial neutron production profile varies significantly among independent simulations of the same model. A range of standard approaches has been used to attempt to resolve the source convergence problem and determine the true axial neutron production profile. This profile is of interest in cask criticality safety analysis because it influences the interaction among assemblies loaded in the cask. The forthcoming SCALE 6.2 release will include source convergence testing based on Shannon entropy, which may assist in identifying these types of problems in the future. It should be noted that this source convergence problem results from the depletion conditions and the final distribution of fissile and absorber material within the discharged fuel assembly. The results, while entirely generated within the SCALE code system, are representative of any Monte Carlo program attempting to calculate k{sub eff} for such a system. The most important conclusion of this work is that the BUC criticality safety community must be aware of the potential for this type of source convergence problem. Analysts must approach such partially rodded scenarios with caution and critically examine the results. Fortunately, it is extremely unlikely that BWR assemblies would experience the bladed operation necessary to create this particular source convergence problem. These results may also have some bearing on the analysis of PWR BUC for new plant designs such as AP1000 that incorporate significant rodded operations. In a broader context, the source convergence problem identified here could have been magnified if it had occurred in a Monte Carlo depletion simulation. It may be possible to identify this type of problem in a depletion calculation by comparing the apparently converged fluxes between steps to ensure that no oscillations or other serious shifts are occurring. The depletion calculations performed in this work were performed with a two-dimensional deterministic code and are therefore not subject to this problem. These results may also raise questions about the validity of 2D depletion models with extruded geometry in the 3D k{sub eff} calculation. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Distributed Real-time Plume Monitoring for Deep Sea Mineral Extraction

In the emerging industry of deep-sea mining for minerals and deposits (e.g. polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediment. There are two main sources of plume generation, the first being at the active mining site where the “collector” directly removes the top layer of the sea floor. The other is the “midwater plume” consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on seafloor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration. The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the seafloor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications. By using high-precision laser transmitters, many of the challenges current subsea optical systems face can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of a wide array of sensor types. This interlinking allows all the data gathered from the network to be processed through a single hardline uplink to the surface, lowering the complexity required for near real-time data processing. Additionally, the laser control systems produce metadata that can be used to help characterize the water column between the nodes. Combining data from various sensors such as turbidity, temperature, current velocity with metadata such as beam attenuation and deflection can produce a high-resolution model of sea floor conditions around an active mining zone. The resulting near real-time model can be used to optimize location and flow rate of the mining operation to minimize and quantify the environmental impact.

Mons, Ishan↗

DOE OSTI2015

SU-E-T-760: Tolerance Design for Site-Specific Range in Proton Patient QA Process Using the Six Sigma Model

Purpose: To show how tolerance design and tolerancing approaches can be used to predict and improve the site-specific range in patient QA process in implementing the Six Sigma. Methods: In this study, patient QA plans were selected according to 6 site-treatment groups: head &neck (94 cases), spine (76 cases), lung (89 cases), liver (53 cases), pancreas (55 cases), and prostate (121 cases), treated between 2007 and 2013. We evaluated a model of the Six Sigma that determines allowable deviations in design parameters and process variables in patient-specific QA, where possible, tolerance may be loosened, then customized if it necessary to meet the functional requirements. A Six Sigma problem-solving methodology is known as DMAIC phases, which are used stand for: Define a problem or improvement opportunity, Measure process performance, Analyze the process to determine the root causes of poor performance, Improve the process by fixing root causes, Control the improved process to hold the gains. Results: The process capability for patient-specific range QA is 0.65 with only ±1 mm of tolerance criteria. Our results suggested the tolerance level of ±2–3 mm for prostate and liver cases and ±5 mm for lung cases. We found that customized tolerance between calculated and measured range reduce that patient QA plan failure and almost all sites had failure rates less than 1%. The average QA time also improved from 2 hr to less than 1 hr for all including planning and converting process, depth-dose measurement and evaluation. Conclusion: The objective of tolerance design is to achieve optimization beyond that obtained through QA process improvement and statistical analysis function detailing to implement a Six Sigma capable design.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2011

A Roadmap for NEAMS Capability Transfer

The vision of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to bring truly predictive modeling and simulation (M&S) capabilities to the nuclear engineering community in order to enable a new approach to the design and analysis of nuclear energy systems. From its inception, the NEAMS program has always envisioned a broad user base for its software and scientific products, including researchers within the DOE complex, nuclear industry technology developers and vendors, and operators. However activities to date have focused almost exclusively on interactions with NEAMS sponsors, who are also near-term users of NEAMS technologies. The task of the NEAMS Capability Transfer (CT) program element for FY2011 is to develop a comprehensive plan to support the program's needs for user outreach and technology transfer. In order to obtain community input to this plan, a 'NEAMS Capability Transfer Roadmapping Workshop' was held 4-5 April 2011 in Chattanooga, TN, and is summarized in this report. The 30 workshop participants represented the NEAMS program, the DOE and industrial user communities, and several outside programs. The workshop included a series of presentations providing an overview of the NEAMS program and presentations on the user outreach and technology transfer experiences of (1) The Advanced Simulation and Computing (ASC) program, (2) The Standardized Computer Analysis for Licensing Evaluation (SCALE) project, and (3) The Consortium for Advanced Simulation of Light Water Reactors (CASL), followed by discussion sessions. Based on the workshop and other discussions throughout the year, we make a number of recommendations of key areas for the NEAMS program to develop the user outreach and technology transfer activities: (1) Engage not only DOE, but also industrial users sooner and more often; (2) Engage with the Nuclear Regulatory Commission to facilitate their understanding and acceptance of NEAMS approach to predictive M&S; (3) Place requirements gathering from prospective users on a more formal footing, updating requirements on a regular basis and incorporate them into planning and execution of the project in a traceable fashion; (4) Seek out the best available data for validation purposes, and work with experimental programs to design and carry out new experiments that satisfy the need for data suitable for validation of high-fidelity M&S codes; (5) Develop and implement program-wide plans and policies for export control, licensing, and distribution of NEAMS software products; (6) Establish a program of sponsored alpha testing by experienced users in order to obtain feedback on NEAMS codes; (7) Provide technical support for NEAMS software products; (8) Develop and deliver documentation, tutorial materials, and live training classes; and (9) Be prepared to support outside users who wish to contribute to the codes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings

This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Carbon Dioxide Shuttling Thermochemical Storage Using Strontium Carbonate

Phase I concludes with significant progress made towards the SunShot ELEMENTS goals of high energy density, high power density, and high temperature by virtue of a SrO/SrCO 3 based material. A detailed exploration of sintering inhibitors has been conducted and relatively stable materials supported by YSZ or SrZO 3 have been identified as the leading candidates. In 15 cycle runs using a 3 hour carbonation duration, several materials demonstrated energy densities of roughly 1500 MJ/m 3 or greater. The peak power density for the most productive materials consistently exceeded 40 MW/m 3 —an order of magnitude greater than the SOPO milestone. The team currently has a material demonstrating nearly 1000 MJ/m 3 after 100 abbreviated (1 hour carbonation) cycles. A subsequent 8 hour carbonation after the 100 cycle test exhibited over 1500 MJ/m 3 , which is evidence that the material still has capacity for high storage albeit with slower kinetics. Kinetic carbonation experiments have shown three distinct periods: induction, kinetically-controlled, and finally a diffusion-controlled period. In contrast to thermodynamic equilibrium prediction, higher carbonation temperatures lead to greater conversions over a 1 hour periods, as diffusion of CO 2 is more rapid at higher temperatures. A polynomial expression was fit to describe the temperature dependence of the linear kinetically-controlled regime, which does not obey a traditional Arrhenius relationship. Temperature and CO 2 partial pressure effects on the induction period were also investigated. The CO 2 partial pressure has a strong effect on the reaction progress at high temperatures but is insignificant at temperatures under 900°C. Tomography data for porous SrO/SrCO 3 structures at initial stage and after multiple carbonation/decomposition cycles have been obtained. Both 2D slices and 3D reconstructed representations have been obtained from the raw data. Porosity and surface area have been computed based on tomography. The two parameters, permeability and Forchheimer constants, in the Darcy’s law for linear and non-linear range have been obtained from the fluid dynamics simulation. The structure is nearly isotropic from the hydrodynamics point of view. Thermal conductivity at elevated temperature has been obtained from the 3-D pore-scale simulations. The computed thermal conductivities of the structure compare well with an existing correlation. Computational models have been developed for solving the energy and mass transport equations with chemical reaction on the surface and inside the particles. The chemical kinetics or rate law is needed on the particle scale; however it is generally measured using a lumped model based on the average of reacted as well as unreacted mass. Many simulations have been conducted on a 2-D model to determine the effects of those pore scale parameters on the heat release based on the measured data. The simulation results using 2-D model show that average volumetric heat flow rate of 4 MW/m 3 in the first 5 minutes can be obtained. The insight gained from the 2-D simulation will be useful for the next stage simulation based on realistic 3-D structures. A probabilistic analysis has been conducted on the baseline system of recovering heat from the hot CO 2 evolved from the endothermic reaction and compressing the cooled CO 2 into a storage tank at 20-21 bar. Using a Monte Carlo simulation using 2,000,000 samples and both uniform and triangular probability distributions of four variables (heat exchanger pinch points, compressor efficiency, and power block efficiency), exergetic efficiency of 90% appears to be very likely.

14 SOLAR ENERGY↗

DOE OSTI2017

Interfaces in Heterogeneous Catalysts: Advancing Mechanistic Understanding through Atomic-Scale Measurements

Developing novel catalysts with high efficiency and selectivity is critical for enabling future clean energy conversion technologies. Interfaces in catalyst systems have long been considered the most critical factor in controlling catalytic reaction mechanisms. Interfaces include not only the catalyst surface but also interfaces within catalyst particles and those formed by constructing heterogeneous catalysts. The atomic and electronic structures of catalytic surfaces govern the kinetics of binding and release of reactant molecules from surface atoms. Interfaces within catalysts are introduced to enhance the intrinsic activity and stability of the catalyst by tuning the surface atomic and chemical structures. Examples include interfaces between the core and shell, twin or domain boundaries, or phase boundaries within single catalyst particles. In supported catalyst nanoparticles (NPs), the interface between the metallic NP and support serves as a critical tuning factor for enhancing catalytic activity. Surface electronic structure can be indirectly tuned and catalytically active sites can be increased through the use of supporting oxides. Additionally, tuning interfaces in catalyst systems has been identified as an important strategy in the design of novel catalysts. However, the governing principle of how interfaces contribute to catalyst behavior, especially in terms of interactions with intermediates and their stability during electrochemical operation, are largely unknown. This is mainly due to the evolving nature of such interfaces. Small changes in the structural and chemical configuration of these interfaces may result in altering the catalytic performance. These interfacial arrangements evolve continuously during synthesis, processing, use, and even static operation. A technique that can probe the local atomic and electronic interfacial structures with high precision while monitoring the dynamic interfacial behavior in situ is essential for elucidating the role of interfaces and providing deeper insight for fine-tuning and optimizing catalyst properties.Scanning transmission electron microscopy (STEM) has long been a primary characterization technique used for studying nanomaterials because of its exceptional imaging resolution and simultaneous chemical analysis. Over the past decade, advances in STEM, that is, the commercialization of both aberration correctors and monochromators, have significantly improved the spatial and energy resolution. Imaging atomic structures with subangstrom resolution and identifying chemical species with single-atom sensitivity are now routine for STEM. These advancements have greatly benefitted catalytic research. For example, the roles of lattice strain and surface elemental distribution and their effect on catalytic stability and reactivity have been well documented in bimetallic catalysts. In addition, three-dimensional atomic structures revealed by STEM tomography have been integrated in theoretical modeling for predictive catalyst NP design. Recent developments in stable electronic and mechanical devices have opened opportunities to monitor the evolution of catalysts in operando under synthesis and reaction conditions; high-speed direct electron detectors have achieved sub-millisecond time resolutions and allow for rapid structural and chemical changes to be captured. Investigations of catalysts using these latest microscopy techniques have provided new insights into atomic-level catalytic mechanisms. Further integration of new microscopy methods is expected to provide multidimensional descriptions of interfaces under relevant synthesis and reaction conditions.In this Account, we discuss recent insights on understanding catalyst activity, selectivity, and stability using advanced STEM techniques, with an emphasis on how critical interfaces dictate the performance of precious metal-based heterogeneous catalysts. The role of extended interfacial structures, including those between core and shell, between separate phases and twinned grains, between the catalyst surface and gas, and between metal and support are discussed. We also provide an outlook on how emerging electron microscopy techniques, such as vibrational spectroscopy and electron ptychography, will impact future catalysis research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗

DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Predicting Ice Sheet and Climate Evolution at Extreme Scales

A main research objectives of PISCEES is the development of formal methods for quantifying uncertainties in ice sheet modeling. Uncertainties in simulating and projecting mass loss from the polar ice sheets arise primarily from initial conditions, surface and basal boundary conditions, and model parameters. In general terms, two main chains of uncertainty propagation may be identified: 1. inverse propagation of observation and/or prior onto posterior control variable uncertainties; 2. forward propagation of prior or posterior control variable uncertainties onto those of target output quantities of interest (e.g., climate indices or ice sheet mass loss). A related goal is the development of computationally efficient methods for producing initial conditions for an ice sheet that are close to available present-day observations and essentially free of artificial model drift, which is required in order to be useful for model projections (“initialization problem”). To be of maximum value, such optimal initial states should be accompanied by “useful” uncertainty estimates that account for the different sources of uncerainties, as well as the degree to which the optimum state is constrained by available observations. The PISCEES proposal outlined two approaches for quantifying uncertainties. The first targets the full exploration of the uncertainty in model projections with sampling-based methods and a workflow managed by DAKOTA (the main delivery vehicle for software developed under QUEST). This is feasible for low-dimensional problems, e.g., those with a handful of global parameters to be inferred. This approach can benefit from derivative/adjoint information, but it is not necessary, which is why it often referred to as “non-intrusive”. The second approach makes heavy use of derivative information from model adjoints to address quantifying uncertainty in high-dimensions (e.g., basal boundary conditions in ice sheet models). The use of local gradient, or Hessian information (i.e., second derivatives of the cost function), requires additional code development and implementation, and is thus often referred to as an “intrusive” approach. Within PISCEES, MIT has been tasked to develop methods for derivative-based UQ, the ”intrusive” approach discussed above. These methods rely on the availability of first (adjoint) and second (Hessian) derivative code, developed through intrusive methods such as algorithmic differentiation (AD). While representing a significant burden in terms of code development, derivative-baesd UQ is able to cope with very high-dimensional uncertainty spaces. That is, unlike sampling methods (all variations of Monte Carlo), calculational burden is independent of the dimension of the uncertainty space. This is a significant advantage for spatially distributed uncertainty fields, such as threedimensional initial conditions, three-dimensional parameter fields, or two-dimensional surface and basal boundary conditions. Importantly, uncertainty fields for ice sheet models generally fall into this category.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

DOE OSTI2014

Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis

This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/ . Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN, and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM 2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (-0.1%) and 0.4% (-5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM 2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO 4 2- by -23.0% (-27.7%), -12.5% (-18.9%) and -7.9% (-14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM 2.5 due to the overestimations of SO 4 2- , NH 4 + , NO 3 - , and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. Finally, this work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.

indirect aerosol forcing↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2011

Computational Needs for the Next Generation Electric Grid Proceedings

The April 2011 DOE workshop, 'Computational Needs for the Next Generation Electric Grid', was the culmination of a year-long process to bring together some of the Nation's leading researchers and experts to identify computational challenges associated with the operation and planning of the electric power system. The attached papers provide a journey into these experts' insights, highlighting a class of mathematical and computational problems relevant for potential power systems research. While each paper defines a specific problem area, there were several recurrent themes. First, the breadth and depth of power system data has expanded tremendously over the past decade. This provides the potential for new control approaches and operator tools that can enhance system efficiencies and improve reliability. However, the large volume of data poses its own challenges, and could benefit from application of advances in computer networking and architecture, as well as data base structures. Second, the computational complexity of the underlying system problems is growing. Transmitting electricity from clean, domestic energy resources in remote regions to urban consumers, for example, requires broader, regional planning over multi-decade time horizons. Yet, it may also mean operational focus on local solutions and shorter timescales, as reactive power and system dynamics (including fast switching and controls) play an increasingly critical role in achieving stability and ultimately reliability. The expected growth in reliance on variable renewable sources of electricity generation places an exclamation point on both of these observations, and highlights the need for new focus in areas such as stochastic optimization to accommodate the increased uncertainty that is occurring in both planning and operations. Application of research advances in algorithms (especially related to optimization techniques and uncertainty quantification) could accelerate power system software tool performance, i.e. speed to solution, and enhance applicability for new and existing real-time operation and control approaches, as well as large-scale planning analysis. Finally, models are becoming increasingly essential for improved decision-making across the electric system, from resource forecasting to adaptive real-time controls to online dynamics analysis. The importance of data is thus reinforced by their inescapable role in validating, high-fidelity models that lead to deeper system understanding. Traditional boundaries (reflecting geographic, institutional, and market differences) are becoming blurred, and thus, it is increasingly important to address these seams in model formulation and utilization to ensure accuracy in the results and achieve predictability necessary for reliable operations. Each paper also embodies the philosophy that our energy challenges require interdisciplinary solutions - drawing on the latest developments in fields such as mathematics, computation, economics, as well as power systems. In this vein, the workshop should be viewed not as the end product, but the beginning of what DOE seeks to establish as a vibrant, on-going dialogue among these various communities. Bridging communication gaps among these communities will yield opportunities for innovation and advancement. The papers and workshop discussion provide the opportunity to learn from experts on the current state-of-the-art on computational approaches for electric power systems, and where one may focus to accelerate progress. It has been extremely valuable to me as I better understand this space, and consider future programmatic activities. I am confident that you too will enjoy the discussion, and certainly learn from the many experts. I would like to thank the authors of the papers for sharing their perspectives, as well as the paper discussants, session recorders, and participants. The meeting would not have been as successful without your commitment and engagement. I also would like to thank Joe Eto and Bob Thomas for their vision and leadership in bringing together such a well-structured and productive forum.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Assessment of a Smoothed Particle Hydrodynamics Method for Thin Film on Textured Surfaces

A large number of previous studies suggest that the behavior of the near-wall liquid micro-layer plays a key role in boiling heat transfer and boiling crisis; see examples. The thin liquid layer interacts with heater surface and 'external' two-phase flow. The net effects of these interactions define the micro-hydrodynamics that leads to dryout. As a result, a good understanding of the thin-layer hydrodynamics is significant for the accurate prediction of CHF. Recently, with the advent of nanotechnology including micro-scale manufacturing, it becomes possible to perform high heat-flux boiling experiments with controlled surface conditions. Although many reports show rich observations about the effects of surface characteristics on CHF, physical mechanisms remain not fully understood. In order to have better understanding of the above phenomena, several computational models are developed, ranging from the lubrication theory to CFD methods. The former models are limited to one-dimensional formulation and unable to deal with the dry-out phenomena. It turns out that CFD methods have a greater potential for the mechanistic study of boiling. Towards the study objective, a particle-based CFD method called Smoothed Particle Hydrodynamics (SPH) is adopted. SPH is a Lagrangian particle method for solving systems of partial differential equations. Originally SPH is proposed for fluid dynamics in the context of astrophysical problems. Because mass and density are introduced during the particle approximation, SPH is also applied in fluid dynamic problems, where mass and density are important parameters to determine, without mass loss (a limitation of interface capturing schemes in mesh-based methods). Besides, geometrically complex and/or dynamic boundaries and interfaces can be handled without undue difficulty in SPH. Currently, most SPH simulations focus on large-scale scenarios (meter scale), like flooding and solitary wave. Also some works have been done with SPH in pore-scale. As for micro- or nano-scale simulation, people usually apply Dissipative Particle Dynamics (DPD) and Molecular Dynamics (MD) for particle-based simulation. Almost no work has been done on micro-scale with SPH, so assessments are needed to investigate the capability of SPH in application of micro-hydrodynamics. The Phenomena Identification and Ranking Table (PIRT) is applied to align application's technical requirements with associated physics and materials, models, simulation code capabilities and verification, validation and uncertainty quantification activities. The figures of merit are determined as: (1) Timing of micro-layer rewetting dry surface; (2) Effects of surface features. The corresponding key phenomena are determined as: (1) Hydrodynamics; (2) Interface Dynamics; (3) Energy; (4) Surface Effect. This study deals with isothermal scenario and no energy term is considered. Based on the current needs, a new SPH package, Micro-SPH, is compiled based on SPHysics with surface tension and wettability built in. Test cases are performed with Micro-SPH to assess the capability of SPH to each phenomenon identified. NEUTRINO with incompressible SPH solver is employed to investigate micro-fluid dynamics on a surface with uniformly distributed cylindrical pillars for application purposes. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Combustion Performance and Emissions Optimization through Integration of Miniaturized High Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2024

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

42 ENGINEERING↗