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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Validation of Wichner Predictive Model for Chronic Oxidation by Moisture of Nuclear Graphite

This document reports the results of multi-year extensive research on chronic oxidation of nuclear graphite by traces of moisture that might be present in the helium coolant of high-temperature gas-cooled reactors (HTGRs). The research focuses on four grades of nuclear graphite that are considered potential candidates for component fabrication in advanced nuclear reactors. The four grades are (1) PCEA (medium-fine grain, extruded, manufactured by GrafTech Inc., USA); (2) NBG-17 (medium-fine grain, vibrationally molded, manufactured by SGL Carbon, Germany/France); (3) IG-110 (microfine grain, isostatically molded, manufactured by Toyo Tanso, Japan); and (4) 2114 (microfine grain, isostatically pressed, manufactured by Mersen, USA). The experimental part of this research comprised three objectives that were consistently pursued for each graphite material: (1) accelerated oxidation tests in well-controlled conditions (temperature, gas composition) and analysis of chemical kinetic models and of their parameters for each graphite grade; (2) measurements of water vapor transport rates from H 2 O/He mixtures through thin graphite slabs for estimation of the water effective diffusivity specific to each material; (3) controlled oxidation of graphite specimens in H 2 O/He mixtures and characterization of the effects of oxidation temperatures on the density profiles of oxidized layers. These three groups of experimental results were interpreted using the model developed by Wichner et al. for predicting the penetration depth of oxidants (moisture or oxygen) as a function of oxidation conditions. Basically, starting from the kinetic and transport properties of each graphite grade, the ultimate goal was predicting the stable density profile in the oxidized layer and its dependence on the oxidation temperature. The profiles obtained this way were then compared against direct observations of oxidized layers in specimens obtained in well-controlled oxidation conditions. The general agreement between predictions and observations validated the general applicability of the Wichner model. The results obtained in this project were presented at annual Department of Energy–Advanced Reactor Technologies program reviews and at specialist meetings and conferences and were published in Oak Ridge National Laboratory (ORNL) reports and in professional journals. The results published in 2018 had an immediate impact in 2019, when a group from three US universities used ORNL kinetic data as input information in a complex simulation of chronic oxidation by moisture using the same graphite grades during long-time operation in a modular high-temperature gas-cooled reactor. Their conclusion, in line with the conclusions obtained at the end of this project, was that chronic graphite oxidation by moisture during normal operation conditions of an HTGR will be limited to a thin surface layer of graphite blocks at the reactor bottom, where the highest temperatures are. That will not affect the integrity of graphite components and reactor safety, provided the impurity concentration in the helium coolant is rigorously controlled and maintained at the design specifications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A semi-coupled aero-servo-hydro numerical model for floating vertical axis wind turbines operating on TLPs

Floating vertical axis wind turbines (VAWTs) have many advantages over floating horizontal axis wind turbines (HAWTs) at large scales in deep water; however, there are several key challenges to overcome as well. One of the challenges is accurate prediction of the dynamic motion and loads performance of a floating VAWT. Here, a new semi-coupled aero-servo-hydro method is developed to assess dynamic responses of a floating VAWT by modeling the system as a 7-degree-of-freedom (7-DOF) model: the supporting platform is considered as a 6-DOF rigid body; the rotation of the rotor is considered as the 7th DOF. Aerodynamic, hydrodynamic, and mooring loads and control of the rotor speed are fully considered. This model can predict performance of floating VAWTs with reasonable fidelity according to validation with OrcaFlex through static and dynamic responses of a floating VAWT with Darrieus rotor operating on a new tension-leg platform (TLP). Being a reduced complexity model, the 7-DOF model can be efficiently applied to assess performance of the newly designed floating VAWT. This model is used to examine the relative contributions of aerodynamic and wave loads imparted to the floating system and the benefits of a three-bladed VAWT over a two-bladed VAWT through dynamic and fatigue analysis.

17 WIND ENERGY↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Review of the impacts of leaking CO 2 gas and brine on groundwater quality

This review paper provides a synthetic view of the existing knowledge and summarizes data and findings of the recent literature on the subject of the potential leaking of CO2 from the deep subsurface storage reservoirs and the effects on aquifer quality. New ideas and concepts are developed and insights are also provided. The objectives of this paper are to: 1) present and discuss potential risks for groundwater degradation due to CO2 gas and brine exposure; 2) identify the set of geochemical data required to assess and predict aquifer responses to CO2 and brine leakage. Specifically, this paper will discuss the following issues: 1) Aquifer responses (such as changes in aqueous phase/groundwater chemical composition; changes in solid phase chemistry and mineralogy; changes in the extent and rate of reactions and processes and possible establishment of a new network of reactions and processes affecting or controlling overall mobility of major, minor, and trace elements; development of conceptual and reduced order models (ROMs) to describe and predict aquifer responses); 2) The degree of impact such as significant or insignificant changes in pH and major, minor, and trace element release that depend on the following controlling variables; the effect of leaking plume characteristics (gas composition, pure CO2 and/or CO2 -CH4 -H2S mixtures and brine concentration and composition (trace metals); aquifer properties [such as initial aqueous phase conditions and mineralogy: minerals controlling sediments’ response (e.g., calcite, Si bearing minerals, etc.)]; overview of relevant hydrogeological and geochemical processes related to the impact of CO2 gas and brine on groundwater quality; the fate of the elements released from sediments or transported with brine (such as precipitation/incorporation into minerals (calcite and other minerals), adsorption, electron transfer reactions, the role of natural attenuation; whether or not the release of metals following exposure to CO2 harmful (risk assessment).

Qafoku, Nikolla P.↗
NASA NTRS1974

Skylab contamination control

The optical contamination control systems of Skylab are reviewed, covering contamination sources, critical elements, flight hardware configuration, contamination monitoring sensors, mathematical contamination prediction models, contamination cloud and deposition models, and hardware implementation. Also considered are supportive contamination tests, contamination mission support activities, Skylab contamination evaluation, contamination measurement experiments, and the effectiveness of contamination control measures. Sources of contamination are identified, Skylab system susceptibility to contamination is determined, and predictions are made for surface contamination deposition and background brightness levels. Mission evaluation results indicate that, barring anomalous conditions, Skylab mission equipment and activities are adequate to reduce the general contamination level to the sensitivity threshold levels for experiments and affected subsystems.

Davis, C. M.↗
DOE OSTI2017

Electron Temperature Fluctuation Measurements and Transport Model Validation at Alcator C-Mod

The tokamak is a type of toroidal device used to confine a fusion plasma using large magnetic fields. Tokamaks and stellarators the leading devices for confining plasmas for fusion, and the capability to predict performance in these magnetically confined plasmas is essential for developing a sustainable fusion energy source. The magnetic configuration of tokamaks and stellarators does not exist in Nature, yet, the fundamental processes governing transport in fusion plasmas are universal – turbulence and instabilities, driven by inhomogeneity and asymmetry in the plasma, conspire to transport heat and particles across magnetic field lines and can play critical roles in impurity confinement and generation of intrinsic rotation. Turbulence exists in all plasmas, and in neutral fluids as well. The study of turbulence is essential to developing a fundamental understanding of the nature of the fourth state of matter, plasmas. Experimental studies of turbulence in tokamaks date back to early scattering observations from the late 1970s. Since that time, great advances in turbulence diagnostics have been made, all of which have significantly enhanced our knowledge and understanding of turbulence in tokamaks. Through comparisons with advanced gyrokinetic theory and turbulent-transport models a great deal of evidence exists to implicate turbulent-driven transport as an important mechanism determining transport in all channels: heat, particle and momentum However, prediction and control of turbulent-driven transport remains elusive. Key to development of predictive transport models for magnetically confined fusion plasmas is validation of the nonlinear gyrokinetic transport model, which describes transport due to turbulence. Validation of gyrokinetic codes must include detailed and quantitative comparisons with measured turbulence characteristics, in addition to comparisons with inferred transport levels and equilibrium profiles. For this reason, advanced plasma diagnostics for studying core turbulence are needed in order to assess the accuracy of gyrokinetic models for turbulent-driven particle, heat and momentum transport. New core turbulence diagnostics at the world-class tokamaks Alcator C-Mod at MIT and ASDEX Upgrade at the Max Planck Institute for Plasma Physics have been designed, developed, and operated over the course of this project. These new instruments are capable of measuring electron temperature fluctuations and the phase angle between density and temperature fluctuations locally and quantitatively. These new data sets from Alcator C-Mod and ASDEX Upgrade are being used to fill key gaps in our understanding of turbulent transport in tokamaks. In particular, this project has results in new results on the topics of the Transport Shortfall, the role of ETG turbulence in tokamak plasmas, profile stiffness, the LOC/SOC transition, and intrinsic rotation reversals. These data are used in a rigorous process of “Transport model validation”, and this group is a world-leader on using turbulence models to design new hardware and new experiments at tokamaks. A correlation electron cyclotron emission (CECE) diagnostic is an instrument used to measure micro-scale fluctuations (mm-scale, compared to the machine size of meters) of electron temperature in magnetically confined fusion plasmas, such as those in tokamaks and stellarators. These micro-scale fluctuations are associated with drift-wave type turbulence, which leads to enhanced cooling and mixing of particles in fusion plasmas and limits achieving the required temperatures and densities for self-sustained fusion reactions. A CECE system can also be coupled with a reflectometer system that measured micro-scale density fluctuations, and from these simultaneous measurements, one can extract the phase between the density (n) and temperature (T) fluctuations, creating an nT phase diagnostic. Measurements of the fluctuations and the phase angle between them are extremely useful for testing and validating predictive models for the transport of heat and particles in fusion plasmas due to turbulence. Once validated, the models are used to predict performance in ITER and other burning plasmas, such as the MIT ARC design. Most recently, data from the newly developed, so-called “CECE diagnostic” [Cima 1995, White 2008] and “nT phase angle measurements” [Haese 1999, White 2010] ]will be combined with data from density fluctuation diagnostics at ASDEX Upgrade to support a long-term program of physics research in turbulence and transport that will allow for more stringent testing and validation of gyrokinetic turbulent-transport codes. This work directly impacts the development of predictive transport models in the U.S. FES program, such as TGLF, developed by General Atomics, which are used to predict performance in ITER and other burning plasma devices as part of advancing the development of fusion energy sciences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

A trust region algorithm for PDE-constrained optimization with bound constraints using reduced order modeling

This paper presents a novel trust region algorithm that relies on proper orthogonal de-composition techniques to construct accurate reduced order models during optimization.The algorithm samples high fidelity snapshots to compute the POD functions that are used to generate reduced order models. The reduced order models are used to replace the computationally intensive high fidelity finite element evaluations during optimization.The proposed algorithm employs a trust region framework to detect loss in predictive ac-curacy in the reduced order model and automatically update the POD functions during optimization. The trust region framework allows the algorithm to use sound mathematical metrics to effectively improve the accuracy and robustness of the reduced order models during optimization. The algorithm also employs a projected gradient algorithm to model bound constraints and compute optimal and feasible controls.This paper also presents an accurate Hessian formulation for topology optimization problems. The proposed trust region framework relies on a quadratic model to update the control. This quadratic model needs reliable second order derivative information to predict the behavior of the objective function within a suitable trust region. If a nonlinear Hessian formulation is used, the computational effort increases due to additional finite element evaluations. The proposed linear Hessian formulation reduces the computational effort and enables the calculation of the second order derivative information without additional finite element model evaluations. Examples in topology optimization are presented to demonstrate the applicability of the proposed algorithm and linear Hessian formulation for large-scale PDE-constrained optimization.

bound constraints↗
NASA NTRS2017

Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Machine Learning Model.↗
DOE OSTI2021

Investigations into Balancing Peak-to-Average Power Ratio and Mean Power Extraction for a Two-Body Point-Absorber Wave Energy Converter

The power harnessed by wave energy converters (WECs) in oceans is highly variable and, therefore, has a high peak-to-average power (PTAP) ratio. To minimize the cost of a WEC power take off (PTO) system, it is desirable to reduce the PTAP ratio while maximizing the mean power extracted by WECs. The important issue of how PTAP ratio reduction measures (such as adding an inertia element) can affect the mean power extracted in a reference model has not been thoroughly addressed in the literature. To investigate this correlation, this study focuses on the integration of the U.S. Department of Energy’s Reference Model 3, a two-body point absorber, with a slider-crank WEC for linear-to-rotational conversion. In the first phase of this study, a full-scale numerical model was developed that predicts how PTO system parameters, along with an advanced control algorithm, can potentially affect the proposed WEC’s PTAP ratio as well as the mean power extracted. In the second phase, an appropriate scaled-down model was developed, and extracted power results were successfully validated against the full-scale model. Finally, numerical and hardware-in-the-loop (HIL) simulations based on the scaled-down model were designed and conducted to optimize or make trade-offs between the operational performance and PTAP ratio. The initial results with numerical and HIL simulations reveal that gear ratio, crank radius, and generator parameters substantially impact the PTAP ratio and mean power extracted.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

We report building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1980

Solid-state switch modeling

A model for predicting voltages and currents of passive loads during turn on and turn off of remote power controllers is presented. The model is concerned with two devices, a five ampere device and a twenty ampere device. The model is presented as a general circuit diagram. The load current is determined by the load parameters and the conditions at switching and the steady state conditions are reached according to time constants determined by the load. The model is used to analyze the turn on and turn off conditions of certain loads including a R-C load and a R-L load.

Raburn, W. D.↗
DOE OSTI2022

Semitauonic b-hadron decays: A lepton flavor universality laboratory

The study of lepton flavor universality violation (LFUV) in semitauonic b-hadron decays has become increasingly important in light of long-standing anomalies in their measured branching fractions, and the large datasets anticipated from the LHC experiments and Belle II. Here, in this review, a comprehensive survey of the experimental environments and methodologies for semitauonic LFUV measurements at the B factories and LHCb is undertaken, along with an overview of the theoretical foundations and predictions for a wide range of semileptonic decay observables. The future prospects of controlling systematic uncertainties down to the percent level, matching the precision of standard model (SM) predictions, are examined. Furthermore, new perspectives and caveats on combinations of the LFUV data are discussed and the world averages for the R(D(*)) ratios are revisited. Here it is demonstrated that different treatments for the correlations of uncertainties from D** excited states can vary the current 3σ tension with the SM within a 1σ range. Prior experimental overestimates of D**τν contributions may further exacerbate this. The precision of future measurements is also estimated; their power to exploit full differential information, and solutions to the inherent difficulties in self-consistent new physics interpretations of LFUV observables, are explored.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

On-Orbit Model Refinement for Controller Redesign

High performance control design for a flexible space structure is challenging since high fidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. A new procedure for refining a multivariable open loop plant model based on closed-loop response data is presented. Using a minimal representation of the state space dynamics, a least squares prediction error method is employed to estimate the plant parameters. This control-relevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance. This paper presents an algorithm for iterative closed-loop system identification and controller redesign along with illustrative examples.

Whorton, Mark S.↗
DOE OSTI2019

Theory and Applications of Macroscale Models in Porous Media

Systems dominated by heterogeneity over a multiplicity of scales, like porous media, still challenge our modeling efforts. The presence of disparate length- and time-scales that control dynamical processes in porous media hinders not only models predictive capabilities, but also their computational efficiency. Macrosopic models, i.e., averaged representations of pore-scale processes, are computationally efficient alternatives to microscale models in the study of transport phenomena in porous media at the system, field or device scale (i.e., at a scale much larger than a characteristic pore size). We present an overview of common upscaling methods used to formally derive macroscale equations from pore-scale (mass, momentum and energy) conservation laws. This review includes the volume averaging method, mixture theory, thermodynamically constrained averaging, homogenization, and renormalization group techniques. We apply these methods to a number of specific problems ranging from food processing to human bronchial system, and from diffusion to multiphase flow, to demonstrate the methods generality and flexibility in handling different applications. The primary intent of such an overview is not to provide a thorough review of all currently available upscaling techniques, nor a complete mathematical treatment of the ones presented, but rather a primer on some of the tools available for upscaling, the basic principles they are based upon, and their specific advantages and drawbacks, so to guide the reader in the choice of the most appropriate method for particular applications and of the most relevant technical literature.

42 ENGINEERING↗
DOE OSTI2017

Detailed, sterically-resolved modeling of soot oxidation: Role of O atoms, interplay with particle nanostructure, and emergence of inner particle burning

A newly-developed detailed mechanism of soot oxidation was tested against experimental observations. The computations were performed at an atomistic level and with a detailed consideration of soot particle surface sites. Several additional reactions were investigated theoretically and one of them, oxidation of embedded five-member rings by O atoms, was included in the model. Here, the primary focus of the study was on the high-temperature shock-tube experiments of Roth et al. (1991). The reaction model was able to reproduce the experimental results, but required coupling to particle nanostructure: partial oxidation of PAH molecules and the decrease in PAH initial sizes along the oxidation path. The principle reaction mechanism was identified to be the formation of oxyradicals, their decomposition, formation of hard-to-oxidize embedded five-member rings, and oxidation of the latter predominantly by O atoms. The analysis identified O as the most effective oxidizer of the embedded five-member rings, which thus controls the rate of the overall oxidation. The model thus predicts fast oxidation during a brief initial period followed by a slow-oxidation one. The model of partial oxidation of an aromatic molecule and switching to the next intact molecule suggests pore formation and subsequent inner particle burning. We also investigated the ability of the present model to reproduce recent measurements of soot oxidation rates performed by Camacho et al. (2015) at about 1000 K. The initial reaction model failed to predict these results, and no adjustment could reconcile the differences. The only way to bring the model to experimental values was by assuming a catalytic decomposition of water on the reactor wall supplying additional radicals, H and OH, to the reacting gas mixture. Additional chemistry, oxidation through complex formation at neighboring surface sites, was required to fully reproduce the experimental observations. These additional reactions were found to play no role in the high-temperature simulations, nor were they sufficient to reproduce the low-temperature experiment on their own, without the assumed catalytic decomposition of water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

First snowflake divertor experiments in MAST-U tokamak

First snowflake (SF) divertor experiments in the MAST-U tokamak demonstrated steady-state snowflake-plus divertor configurations in 450 kA ohmic L-mode plasmas. The SF divertor configuration features a second poloidal field (PF) null in the divertor region close by or overlapping with the main X-point. The resulting low PF region and two additional divertor legs (strike points) may lead to additional power and particle flux sharing via a hypothesized convective cell, and increased plasma-wetted area and radiation. The free-boundary Grad–Shafranov equilibrium code FIESTA was used to design SF configurations with several inter-null distances and orientations. In the experiment, the SF configurations with inter-null distances 0.13–0.20 m and lasting 0.2–0.3 s were obtained. Parallel connection lengths between the midplane and the outer strike point in the SF configurations (evaluated at field lines 1–2 mm from the separatrix in the midplane) were 25–30 m, higher than in the standard divertor (20–25 m) or the Super-X divertor (25 m). Diagnostic measurements highlighted salient SF features. The infra-red video bolometer diagnostic showed that the radiated power peaking in the PF null region was not as pronounces as in the standard divertor. Divertor ion fluxes measured by target Langmuir probes showed increased ion flux in the plate region where a secondary SF strike point landed, concomitantly with the SF formation. These measurements may suggest that some particle and heat redistribution was taking place in the convective SF zone. The first SF experiments provide a basis for future SF studies in MAST-U tokamak with higher input power, improved plasma control and diagnostic measurements, to be compared with the modeling predictions of plasma convective SF mixing and lower density strike point detachment threshold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

59 BASIC BIOLOGICAL SCIENCES↗