Results for “Distributed model predictive control” — Page 15

DOE OSTI2015

WE-B-304-03: Biological Treatment Planning

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-B-304-02: Treatment Planning Evaluation and Optimization Should Be Biologically and Not Dose/volume Based

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Controlled Design of Phase- and Size-Tunable Monodisperse Ni 2 P Nanoparticles in a Phosphonium-Based Ionic Liquid through Response Surface Methodology

Nanoparticles of nickel phosphide are finding wide ranging utility as catalysts for hydrodesulfurization, hydrogen evolution reaction, and hydrodeoxygenation of bio-oils. Herein, we present a methodology to tailor monodisperse nickel phosphide nanoparticles in terms of size and phase through the use of a statistical response surface methodology. Colloidal nickel phosphide nanoparticles were synthesized by replacing octadecene (ODE), a commonly used organic solvent, by a more sustainable phosphonium-based ionic liquid (IL). The replacement of ODE with the phosphonium-based IL resulted in faster crystallization at lower temperatures to yield phase-pure, monodisperse Ni2P nanoparticles. Using a first-order design, the PPh3/Ni precursor ratio was identified as the most critical factor influencing the resulting size and phase of the nanoparticles. Optimization using a Doehlert matrix for second-order design yielded a second-degree polynomial equation used to predict the mean diameter of the nanoparticles (over a range of 4-12 nm) as a function of the PPh3/Ni precursor ratio and the temperature used during synthesis. The resulting model was validated by performing reactions using randomly chosen sets of conditions; the experimentally determined nanoparticle sizes were in excellent agreement with the theoretical sizes predicted by our model. This demonstrates the utility of a multivariate experimental design as a powerful tool in the development of synthetic strategies toward the preparation of colloidal nanoparticles with highly controlled size, size distribution, and phase.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Predicting the Evolution of Shallow Cumulus Clouds With a Lotka‐Volterra Like Model

Abstract In numerical weather prediction and climate models, boundary‐layer clouds are controlled by a wide range of subgrid‐scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large‐eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka‐Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale‐aware parameterizations of shallow‐convective clouds in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS1991

A new method of optimal design for a two-dimensional diffuser by using dynamic programming

A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.

Gu, Chuangang↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Engineering Nucleation Kinetics of Graphite Nodules in Inoculated Cast Iron for Reducing Porosity

Inoculation of cast iron with spherical graphite (SGI) controls nucleation kinetics of graphite nodules and is used for improving casting performance and eliminating solidification micro-porosity. In this study, thermodynamic simulations were used to design inoculants. Thermal and chemical stability of different potential heterogeneous nuclei formed in the melt above the liquidus temperature and in the mushy zone during solidification was predicted. Several inoculation treatments of SGI were performed in laboratory heats. An automated SEM/EDX analysis was applied to examine the graphite nodule size distribution and the family of the non-metallic inclusions in the experimental castings. The data were used to reconstruct a relative graphite nodule nucleation rate in the castings. It was shown that the graphite nodule nucleation kinetics in the castings were significantly different from those predicted by classical nucleation models. In inoculated SGI, the observed bi-modal distribution of graphite nodules was related to a continuous nucleation that occurred towards the end of solidification. The origin of the continuous nucleation and the possibility of engineering graphite nodules nucleation kinetics to control SGI casting soundness by inoculation are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Failure-probability driven dose painting

Purpose: To demonstrate a data-driven dose-painting strategy based on the spatial distribution of recurrences in previously treated patients. The result is a quantitative way to define a dose prescription function, optimizing the predicted local control at constant treatment intensity. A dose planning study using the optimized dose prescription in 20 patients is performed.Methods: Patients treated at our center have five tumor subvolumes from the center of the tumor (PET positive volume) and out delineated. The spatial distribution of 48 failures in patients with complete clinical response after (chemo)radiation is used to derive a model for tumor control probability (TCP). The total TCP is fixed to the clinically observed 70% actuarial TCP at five years. Additionally, the authors match the distribution of failures between the five subvolumes to the observed distribution. The steepness of the dose–response is extracted from the literature and the authors assume 30% and 20% risk of subclinical involvement in the elective volumes. The result is a five-compartment dose response model matching the observed distribution of failures. The model is used to optimize the distribution of dose in individual patients, while keeping the treatment intensity constant and the maximum prescribed dose below 85 Gy.Results: The vast majority of failures occur centrally despite the small volumes of the central regions. Thus, optimizing the dose prescription yields higher doses to the central target volumes and lower doses to the elective volumes. The dose planning study shows that the modified prescription is clinically feasible. The optimized TCP is 89% (range: 82%–91%) as compared to the observed TCP of 70%.Conclusions: The observed distribution of locoregional failures was used to derive an objective, data-driven dose prescription function. The optimized dose is predicted to result in a substantial increase in local control without increasing the predicted risk of toxicity.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2017

Pore-scale water dynamics during drying and the impacts of structure and surface wettability

Plants and microbes secrete mucilage into soil during dry conditions, which can alter soil structure and increase contact angle. Structured soils exhibit a broad pore size distribution with many small and many large pores, and strong capillary forces in narrow pores can retain moisture in soil aggregates. Meanwhile, contact angle determines the water repellency of soils, which can result in suppressed evaporation rates. Although they are often studied independently, both structure and contact angle influence water movement, distribution, and retention in soils. Here drying experiments were conducted using soil micromodels patterned to emulate different aggregation states of a sandy loam soil. Micromodels were treated to exhibit contact angles representative of those in bulk soil (8.4° ± 1.9°) and the rhizosphere (65° ± 9.2°). Drying was simulated using a lattice Boltzmann single-component, multiphase model. In our experiments, micromodels with higher contact angle surfaces took 4 times longer to completely dry versus micromodels with lower contact angle surfaces. Microstructure influenced drying rate as a function of saturation and controlled the spatial distribution of moisture within micromodels. In conclusion, lattice Boltzmann simulations accurately predicted pore-scale moisture retention patterns within micromodels with different structures and contact angles.

4↗

DOE OSTI2022

Current distribution monitoring enables quench and damage detection in superconducting fusion magnets

Abstract Fusion magnets made from high temperature superconducting ReBCO CORC ® cables are typically protected with quench detection systems that use voltage or temperature measurements to trigger current extraction processes. Although small coils with low inductances have been demonstrated, magnet protection remains a challenge and magnets are typically operated with little knowledge of the intrinsic performance parameters. We propose a protection framework based on current distribution monitoring in fusion cables with limited inter-cable current sharing. By employing inverse Biot-Savart techniques to distributed Hall probe arrays around CORC ® Cable-In-Conduit-Conductor (CICC) terminations, individual cable currents are recreated and used to extract the parameters of a predictive model. These parameters are shown to be of value for detecting conductor damage and defining safe magnet operating limits. The trained model is then used to predict cable current distributions in real-time, and departures between predictions and inverse Biot-Savart recreated current distributions are used to generate quench triggers. The methodology shows promise for quality control, operational planning and real-time quench detection in bundled CORC ® cables for compact fusion reactors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

Visible light plasmonic heating of Au-ZnO for the catalytic reduction of CO 2

Plasmonic excitation of Au nanoparticles attached to the surface of ZnO catalysts using low power 532 nm laser illumination leads to significant heating of the catalyst and the conversion of CO 2 and H 2 reactants to CH 4 and CO products. Temperature-calibrated Raman spectra of ZnO phonons show that intensity-dependent plasmonic excitation can controllably heat Au–ZnO from 30 to ~600 °C and simultaneously tune the CH 4 : CO product ratio. The laser induced heating and resulting CH 4 : CO product distribution agrees well with predictions from thermodynamic models and temperature-programmed reaction experiments indicating that the reaction is a thermally driven process resulting from the plasmonic heating of the Au-ZnO. The apparent quantum yield for CO 2 conversion under continuous wave (cw) 532 nm laser illumination is 0.030%. The Au-ZnO catalysts are robust and remain active after repeated laser exposure and cycling. The light intensity required to initiate CO 2 reduction is low ( ~2.5 x 10 5 W m -2 ) and achievable with solar concentrators. Our results illustrate the viability of plasmonic heating approaches for CO 2 utilization and other practical thermal catalytic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2016

New approaches to quantifying aerosol influence on the cloud radiative effect

The topic of cloud radiative forcing associated with the atmospheric aerosol has been the focus of intense scrutiny for decades. The enormity of the problem is reflected in the need to understand aspects such as aerosol composition, optical properties, cloud condensation, and ice nucleation potential, along with the global distribution of these properties, controlled by emissions, transport, transformation, and sinks. Equally daunting is that clouds themselves are complex, turbulent, microphysical entities and, by their very nature, ephemeral and hard to predict. Atmospheric general circulation models represent aerosol-cloud interactions at ever-increasing levels of detail, but these models lack the resolution to represent clouds and aerosol-cloud interactions adequately. There is a dearth of observational constraints on aerosol-cloud interactions. We develop a conceptual approach to systematically constrain the aerosol-cloud radiative effect in shallow clouds through a combination of routine process modeling and satellite and surface-based shortwave radiation measurements. We heed the call to merge Darwinian and Newtonian strategies by balancing microphysical detail with scaling and emergent properties of the aerosol-cloud radiation system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗

NASA NTRS1998

Including Aeroelastic Effects in the Calculation of X-33 Loads and Control Characteristics

Up until now, loads analyses of the X-33 RLV have been done at Marshall Space Flight Center (MSFC) using aerodynamic loads derived from CFD and wind tunnel models of a rigid vehicle. Control forces and moments are determined using a rigid vehicle trajectory analysis and the detailed control load distributions for achieving the desired control forces and moments, again on the rigid vehicle, are determined by Lockheed Martin Skunk Works. However, static aeroelastic effects upon the load distributions are not known. The static aeroelastic effects will generally redistribute external loads thereby affecting both the internal structural loads as well as the forces and moments generated by aerodynamic control surfaces. Therefore, predicted structural sizes as well as maneuvering requirements can be altered by consideration of static aeroelastic effects. The objective of the present work is the development of models and solutions for including static aeroelasticity in the calculation of X-33 loads and in the determination of stability and control derivatives.

Zeiler, Thomas A.↗

DOE OSTI2015

PUNCS: Towards Predictive Understanding of Nitrogen Cycling in Soils

In anoxic environments, the major nitrate/nitrite-consuming processes are respiratory ammonification (also known as dissimilatory nitrate reduction to ammonium) and denitrification (i.e., the formation of the gaseous products N 2 O and N 2 ). Respiratory ammonification oxidizes more carbon per mole of nitrate than denitrification and generates a cation (NH 4+ ), which is retained in soils and bioavailable for plants. Thus, these processes have profoundly different impacts on N retention and greenhouse gas (CO 2 , N 2 O) emissions. Microbes capable of respiratory ammonification or denitrification coexist but the environmental controls over these competing nitrate/nitrite-reducing processes are largely unknown. With the current level of understanding, predictions under what environmental conditions respiratory ammonification activity predominates leading to N-retention rather than N-loss are tenuous. Further, the diversity of genes encoding the ammonium-forming nitrite reductase NrfA is poorly defined hampering the development of tools to assess and monitor this activity in environmental systems. Incomplete denitrification leads to N 2 O, a gas implicated in ozone layer destruction and climate change. The conversion of the greenhouse gas N 2 O to benign N 2 is catalyzed by N 2 O reductase, the characteristic enzyme system of complete denitrifiers. Thus, efforts to estimate N 2 O conversion to N 2 have focused on the well-characterized denitrifier nosZ genes; however, our understanding of the diversity of genes and organisms contributing to N 2 O consumption is incomplete. This paucity of information limits the development of more accurate, predictive models for C- and N-fluxes and greenhouse gas emissions. A comprehensive analysis of the key catalyst of respiratory ammonification, ammonia-forming nitrite reductase NrfA, revealed the evolutionary history of nrfA and identified novel diagnostic features, allowing optimized primer design for nrfA monitoring. Further, a novel group of functional “atypical” nosZ genes was found indicating that a much broader diversity of genes and organisms contribute to consumption of N 2 O. The atypical nosZ genes are distributed in soil ecosystems and often outnumber their typical counterparts, emphasizing their potential role in N 2 O consumption in soils and possibly other environments. Kinetic studies revealed that organisms with atypical NosZ exhibit significantly higher affinity to N 2 O, indicating that the relative activity of bacteria with typical versus atypical NosZ control N 2 O emissions and determine a soil’s N 2 O sink capacity. Collectively, the discoveries made under the PUNCS project improve understanding of N- and associated C-cycling processes in soils, enable the design of enhanced monitoring tools, and allow a larger research community to generate comprehensive datasets required to generate Earth System Models with higher predictive power.

54 ENVIRONMENTAL SCIENCES↗