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DOE OSTI2015

Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Diagnostics and required R and D for control of DEMO grade plasmas

Even if the diagnostics of ITER performs as expected, installation and operation of the diagnostic systems in Demo device will be much harsher than those of the present ITER device. In order to operate the Demo grade plasmas, which may have a higher beta limit, safely with very limited number of simple diagnostic system, it requires a well defined predictable plasma modelling in conjunction with the reliable control system for burn control and potential harmful instabilities. Development of such modelling in ITER is too risky and the logical choice would be utilization of the present day steady state capable devices such as KSTAR and EAST. In order to fulfill this mission, sophisticated diagnostic systems such as 2D/3D imaging systems can validate the physics in the theoretical modeling and challenge the predictable capability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Stabilizing a strongly nonlinear structure through shaker dynamics in fixed frequency voltage control tests

Bifurcations are commonly encountered during force controlled swept and stepped sine testing of nonlinear structures, which generally leads to the so-called jump-down or jump-up phenomena between stable solutions. There are various experimental closed-loop control algorithms, such as control-based continuation and phase-locked loop, to stabilize dynamical systems through these bifurcations, but they generally rely on specialized control algorithms that are not readily available with many commercial data acquisition software packages. A recent method was developed to experimentally apply sequential continuation using the shaker voltage that can be readily deployed using commercially available software. By utilizing the stabilizing effects of electrodynamic shakers and the force dropout phenomena in fixed frequency voltage control sine tests, this approach has been demonstrated to stabilize the unstable branch of a nonlinear system with three branches, allowing for three multivalued solutions to be identified within a specific frequency bandwidth near resonance. Recent testing on a strongly nonlinear system with vibro-impact nonlinearity has revealed jumping behavior when performing sequential continuation along the voltage parameter, like the jump phenomena seen during more traditional force controlled swept and stepped sine testing. Here, this paper investigates the stabilizing effects of an electrodynamic shaker on strongly nonlinear structures in fixed frequency voltage control tests using both numerical and experimental methods. The harmonic balance method is applied to the coupled shaker-structure system with an electromechanical model to simulate the fixed voltage control tests and predict the stabilization for different parameters of the model. The simulated results are leveraged to inform the design of a set of experiments to demonstrate the stabilization characteristics on a fixture-pylon assembly with a vibro-impact nonlinearity. Through numerical simulation and experimental testing on two different strongly nonlinear systems, the various parameters that influence the stability of the coupled shaker-structure are revealed to better understand the performance of fixed frequency voltage control tests.

42 ENGINEERING↗

DOE OSTI2017

{sup 18}F-Fluorodeoxyglucose/Positron Emission Tomography Predicts Patterns of Failure After Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer

Background: We previously reported that pretreatment positron emission tomography (PET) identifies lesions at high risk for progression after concurrent chemoradiation therapy (CRT) for locally advanced non-small cell lung cancer (NSCLC). Here we validate those findings and generate tumor control probability (TCP) models. Methods: We identified patients treated with definitive, concurrent CRT for locally advanced NSCLC who underwent staging {sup 18}F-fluorodeoxyglucose/PET/computed tomography. Visible hypermetabolic lesions (primary tumors and lymph nodes) were delineated on each patient's pretreatment PET scan. Posttreatment imaging was reviewed to identify locations of disease progression. Competing risks analyses were performed to examine metabolic tumor volume (MTV) and radiation therapy dose as predictors of local disease progression. TCP modeling was performed to describe the likelihood of local disease control as a function of lesion size. Results: Eighty-nine patients with 259 hypermetabolic lesions (83 primary tumors and 176 regional lymph nodes) met the inclusion criteria. Twenty-eight patients were included in our previous report, and the remaining 61 constituted our validation cohort. The median follow-up time was 22.7 months for living patients. In 20 patients, the first site of progression was a primary tumor or lymph node treated with radiation therapy. The median time to progression for those patients was 11.5 months. Data from our validation cohort confirmed that lesion MTV predicts local progression, with a 30-month cumulative incidence rate of 23% for lesions above 25 cc compared with 4% for lesions below 25 cc (P=.008). We found no evidence that radiation therapy dose was associated with local progression risk. TCP modeling yielded predicted 30-month local control rates of 98% for a 1-cc lesion, 94% for a 10-cc lesion, and 74% for a 50-cc lesion. Conclusion: Pretreatment FDG-PET identifies lesions at risk for progression after CRT for locally advanced NSCLC. Strategies to improve local control should be tested on high-risk lesions, and treatment deintensification for low-risk lesions should be explored.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

SU-C-BRE-03: Dual Compartment Mathematical Modeling of Glioblastoma Multiforme (GBM)

Purpose: To explore the aggressive recurrence and radioresistence of GBM with a dual compartment tumor survival mathematical model based on intrinsic tumor heterogeneity, cancer stem cells (CSC) and differentiated cancer cells (DCC). Methods: The repopulation and differentiation responses to radiotherapy of a solid tumor were simulated using an Ordinary Differential Equation (ODE). To obtain the tumor radiobiological parameters, we assumed that a tumor consists of two subpopulations, each with its distinctive linear quadratic parameters. The dual compartment cell survival model was constructed as SF(D)=F × exp(-α{sub 1} D-β{sub 1}D{sup 2}) + (1-F) × exp(-α{sub 2}D-β{sub 2}D{sup 2}) for a single fraction of treatment, with F as the fraction of CSC, and α and β describing the radiological properties of each population. Robust least square fitting was performed on clonogenic survival data from one GBM (U373MG) and one NSCLC (H460) cell line. The fit parameters were then used in the ODE model to predict treatment outcome of various treatment schemes. Results: The fit parameters from GBM cell survival data were (F, α{sub 1}, β{sub 1}, α{sub 2}, β{sub 2})=(0.0396, 0.0801, 0.0006, 0.1363, 0.0279), exhibiting two populations with distinctive radiological properties, CSC more radioresistant than DCC. The GBM cell line exhibited significantly poorer tumor control than its single compartment model prediction and NSCLC, which responded well to hypofrationation. The increased radioresistance was due to rapid regrowth of the DCC compartment triggered by its depletion while maintaining a viable CSC population. The rapid regrowth can be reduced by treating dose fractions ≤ 2 Gy with a prolonged treatment period. Conclusion: The interaction between a radioresistant CSC compartment and DCC compartment can explain the poor clinical outcome of GBM after radiotherapy despite dose escalation and hypofractionation attempts. Lower dose fractions result in better treatment outcome but still eventually recurs. Dose escalation beyond 100 Gy and/or differentiation therapy will be vital in achieving GBM tumor control.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Bayesian Optimization of a Free-Electron Laser

The Linac Coherent Light Source X-ray free-electron laser is a complex scienti c apparatus which changes con gurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing X- ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. Furthermore, the result is a sample-e cient optimization routine, combining both historical data and knowledge of accelerator physics to signi cantly outperform existing optimizers.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗

DOE OSTI2012

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗

DOE OSTI2019

A New Efficient Quantitative Multi-component Phase Field: Lattice Boltzmann Model for Simulating Ti6Al4V Solidified Dendrite Under Forced Flow

Ti6Al4V is a widely used, multi-component alloy in additive manufacturing, during which the fluid flow in the molten pool significantly affects the solidified dendrites. To predict and further control the microstructure, modeling and simulating the microstructure evolution play a critical role. In this study, a newly developed, efficient, quantitative multi-component phase-field (PF) model is coupled with a lattice Boltzmann (LB) model to simulate Ti6Al4V solidified dendrite evolution under fluid flow. The accuracy and convergence behavior of the model is validated by the Gibbs–Thomson relation at the dendrite tip. Single and multiple two-dimensional (2D) equiaxed dendrite evolution cases under forced flow were simulated. Results show that the dendrite pattern is influenced remarkably by the fluid flow. Underlying mechanisms of the asymmetrical evolution are revealed by discussing the interaction among the flow, composition distribution and dendrite morphology, quantitatively. The dendrite kinetics are also derived, which ascertains the relationship between tip velocity and undercooling and inlet velocity and is the foundation for larger-scale simulation. We believe that the coupled quantitative multi-component PF–LB framework employed in this study helps in investigating the solidified dendrite morphology evolution in a deep and quantitate manner.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Experimental investigation of advanced turbine control strategies and load-mitigation measures with a model-scale floating offshore wind turbine system

To advance the control co-design of offshore wind energy systems, the authors perform basin-scale experiments with a fully instrumented and actuated floating offshore wind turbine model. The model consists of a 1:70 scale performance-matched model of the International Energy Agency Wind Technology Collaboration Programme 15-MW reference turbine atop the VolturnUS-S semisubmersible platform. The Reference OpenSource Controller provides real-time blade pitch and generator torque control. Here we aim to develop an open data set for the validation of numerical models in predicting the influence of turbine control and load-mitigation measures. For this purpose, we measure the effects of advanced turbine control features, including peak shaving and floating feedback as well as hull-based control using tuned mass dampers on the system. Overall, the results demonstrate measurable and consistent influences from the control and load-mitigation measures, thus confirming the usefulness as a validation data set. Peak shaving attenuates the response to wind turbulence at near-rated wind speed. Floating feedback reduces the load and platform pitch motion associated with the negative damping induced by blade pitch control. The tuned mass dampers also attenuate the system response near the targeted frequencies under suitable conditions. We also identify detrimental side effects of each load-mitigation measure.

17 WIND ENERGY↗

DOE OSTI2023

Automated power-following control for nuclear thermal propulsion startup and shutdown using MOOSE-based applications

This paper describes an investigation of various automated control strategies applied to a full-core multiphysics Griffin/Bison/RELAP-7 model of a prototypical nuclear thermal propulsion system. In all cases, control is achieved by actuating control drums based on the demanded power and predicted quantities from the numerical model. One key finding is that hybrid proportional integral derivative controllers – a novel type of controller that uses both power and reactivity predicted signals – can demonstrate a level of performance rivaling that of period-generated control. The former requires parameter tuning, while the latter mostly necessitates providing reactivity coefficients and temperature rates of change, which could be very challenging to accurately measure in real-time. In addition, decay heat plays an important role in determining cooling requirements during cooldown phases. A decay heat model, accounting for burn time and throttling, was derived, and is incorporated within the model to simulate the steady-state and shutdown phases and satisfyingly follow the power demand. However, temperature overshoots and non-constant specific impulse during throttling will necessitate further improvements.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Hollow pellet injection for magnetic fusion

The precise delivery of mass to burning plasmas is an area of growing interest in magnetic fusion (MF). The amount of mass that is necessary and sufficient can vary depending on such parameters as the type of atoms involved, the type of applications, plasma conditions, mass injector, and injection timing. Motivated by edge localized mode (ELM) control in H-mode plasmas, disruption mitigation and other applications in MF, we report the progress and new possibilities in mass delivery based on hollow pellets. Here, a hollow pellet refers to a spherical shell mass structure with a hollow core. Based on an empirical model of pellet ablation, coupled with BOUT++ simulations of the ELM triggering threshold, hollow pellets are found to be attractive in comparison with solid spheres for ELM control. By using hollow pellets, it is possible to tailor mass delivery to certain regions of edge plasmas while minimizing core contamination and reducing the total amount of mass needed. We also include the experimental progress in mass delivery experiments, in situ diagnostics and hollow pellet fabrication, and emphasize new experimental possibilities for ELM control based on hollow pellets. A related application is the disruption mitigation scheme using powder encapsulated inside hollow shells. Further experiments will also help to resolve known discrepancies between theoretical predictions and experiments in using mass injection for ELM control and leading to better predictive models for ELM stability and triggering.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗

DOE OSTI2016

UO 2 Grain Growth: Developing Phase Field Models for Pore Dragging, Solute Dragging and Anisotropic Grain Boundary Energies

A detailed phase field model for the effect of pore drag on grain growth kinetics was implemented in MARMOT. The model takes into consideration both the curvature-driven grain boundary motion and pore migration by surface diffusion. As such, the model accounts for the interaction between pore and grain boundary kinetics, which tends to retard the grain growth process. Our 2D and 3D simulations demonstrate that the model capture all possible pore-grain boundary interactions proposed in theoretical models. For high enough surface mobility, the pores move along with the migrating boundary as a quasi-rigid-body, albeit hindering its migration rate compared to the pore-free case. For less mobile pores, the migrating boundary can separate from the pores. For the pore-controlled grain growth kinetics, the model predicts a strong dependence of the growth rate on the number of pores, pore size, and surface diffusivity in agreement with theroretical models. An evolution equation for the grain size that includes these parameters was derived and showed to agree well with numerical solution. It shows a smooth transition from boundary-controlled kinetics to pore-controlled kinetics as the surface diffusivity decreases or the number of pores or their size increases. This equation can be utilized in BISON to give accurate estimate for the grain size evolution. This will be accomplished in the near future. The effect of solute drag and anisotropy of grain boundary on grain growth will be investigated in future studies.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Early Career: In-situ Monitoring of Dynamic Phenomena during Solidification

Solidification is critical to processes like casting and additive manufacturing and the manufacture of metallic alloy components we use in our everyday lives. State-of-the-art characterization techniques, now available at U.S. DOE User Facilities and in the laboratory, are not only enabling fundamental studies of metallic alloy solidification dynamics, but also in-operando, in-situ deformation, and manufacturing studies. Here we use x-ray, proton, and electron imaging to study solidification dynamics from the micro-scale to the macro-scale, at times ranging from microseconds to minutes. Our experimental results are used to inform, develop, and validate computational models at the same length and time-scales. Integrating in-situ characterization and modeling will yield the prediction and control of metallic alloy solidification dynamics and the creation of microstructures and properties by design with advanced manufacturing. Major research accomplishments during this period include: 1. Exploring dynamical microstructure selection by means of real-time synchrotron x-ray imaging of controlled directional solidification of aluminum alloys and quantitative phase field modeling. 2. In-situ and ex-situ electron microscopy of rapid solidification after laser melting of metallic alloys. 3. Exploring the impact of microstructural heterogeneities that develop during solidification on solid-state phase transitions, with an emphasis on phase growth, (meta)stability, and transition pathways in aluminum alloys. 4. Demonstrations of four-dimensional (three-dimensions plus time) x-ray microscopy during controlled directional solidification and the first quasi-static/dynamic transmission high energy electron microscopy of metallic alloy solidification dynamics. 5. Experiments and modeling of solidification structure evolution and forward modeling and testing of micromechanical behavior.

36 MATERIALS SCIENCE↗