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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control

Here, we study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

42 ENGINEERING↗
DOE OSTI2011

MAR ASSESSMENTS OF THE HIGH LEVEL WASTE SYSTEM PLAN REVISION 16

High-level waste (HLW) throughput (i.e., the amount of waste processed per unit of time) is primarily a function of two critical parameters: waste loading (WL) and melt rate. For the Defense Waste Processing Facility (DWPF), increasing HLW throughput would significantly reduce the overall mission life cycle costs for the Department of Energy (DOE). Significant increases in waste throughput have been achieved at DWPF since initial radioactive operations began in 1996. Key technical and operational initiatives that supported increased waste throughput included improvements in facility attainment, the Chemical Processing Cell (CPC) flowsheet, process control models and frit formulations. As a result of these key initiatives, DWPF increased WLs from a nominal 28% for Sludge Batch 2 (SB2) to {approx}34 to 38% for SB3 through SB6 while maintaining or slightly improving canister fill times. Although considerable improvements in waste throughput have been obtained, future contractual waste loading targets are nominally 40%, while canister production rates are also expected to increase (to a rate of 325 to 400 canisters per year). Although implementation of bubblers have made a positive impact on increasing melt rate for recent sludge batches targeting WLs in the mid30s, higher WLs will ultimately make the feeds to DWPF more challenging to process. Savannah River Remediation (SRR) recently requested the Savannah River National Laboratory (SRNL) to perform a paper study assessment using future sludge projections to evaluate whether the current Process Composition Control System (PCCS) algorithms would provide projected operating windows to allow future contractual WL targets to be met. More specifically, the objective of this study was to evaluate future sludge batch projections (based on Revision 16 of the HLW Systems Plan) with respect to projected operating windows using current PCCS models and associated constraints. Based on the assessments, the waste loading interval over which a glass system (i.e., a projected sludge composition with a candidate frit) is predicted to be acceptable can be defined (i.e., the projected operating window) which will provide insight into the ability to meet future contractual WL obligations. In this study, future contractual WL obligations are assumed to be 40%, which is the goal after all flowsheet enhancements have been implemented to support DWPF operations. For a system to be considered acceptable, candidate frits must be identified that provide access to at least 40% WL while accounting for potential variation in the sludge resulting from differences in batch-to-batch transfers into the Sludge Receipt and Adjustment Tank (SRAT) and/or analytical uncertainties. In more general terms, this study will assess whether or not the current glass formulation strategy (based on the use of the Nominal and Variation Stage assessments) and current PCCS models will allow access to compositional regions required to targeted higher WLs for future operations. Some of the key questions to be considered in this study include: (1) If higher WLs are attainable with current process control models, are the models valid in these compositional regions? If the higher WL glass regions are outside current model development or validation ranges, is there existing data that could be used to demonstrate model applicability (or lack thereof)? If not, experimental data may be required to revise current models or serve as validation data with the existing models. (2) Are there compositional trends in frit space that are required by the PCCS models to obtain access to these higher WLs? If so, are there potential issues with the compositions of the associated frits (e.g., limitations on the B{sub 2}O{sub 3} and/or Li{sub 2}O concentrations) as they are compared to model development/validation ranges or to the term 'borosilicate' glass? If limitations on the frit compositional range are realized, what is the impact of these restrictions on other glass properties such as the ability to suppress nepheline formation or influence melt rate? The model based assessments being performed make the assumption that the process control models are applicable over the glass compositional regions being evaluated. Although the glass compositional region of interest is ultimately defined by the specific frit, sludge, and WL interval used, there is no prescreening of these compositional regions with respect to the model development or validation ranges which is consistent with current DWPF operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)

Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employs the pseudo-two-dimensional (P2D) model for making predictions. Using robust and efficient model simulation algorithms developed previously, the computational time of the nonlinear model predictive control algorithm is quantified, and the ability to use such models for nonlinear model predictive control for ABMS is established.

25 ENERGY STORAGE↗
DOE OSTI2021

Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation

This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (DER) control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, a strategy combining delay threshold and modified message update rules is proposed to immune the asynchrony resulting from the communications network traffic and it avoids possible numerical instabilities and sensitivities of the tracking and regulation capabilities of this DER control algorithm. By implementing the proposed cyber-physical algorithm on the modified IEEE 37-node system, our preliminary results exhibit that the uncertainties of the underlying communications infrastructure must be considered for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS), because the delayed voltage measurements in the uplink/bi-link cases result in the off-track in VPP dispatch and jittery in voltage regulation.

cyber-physical algorithm↗
DOE OSTI2023

Redox-enabled electronic interrogation and feedback control of hierarchical and networked biological systems

Abstract Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity. E. coli ’s stress response regulon, oxyRS , is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗
DOE OSTI2017

Novel Controls for Economic Dispatch of Combined Cooling, Heating and Power (CHP) Systems

This fact sheet describes a project by University of California, Irvine, in collaboration with Siemens Corporate Research, to develop and demonstrate novel algorithms and dynamic control technology for optimal economic use of CHP systems under 5 MW. The control systems and technologies are increasing market penetration of CHP systems in the light industrial, commercial, and institutional markets.

Algorithm↗
DOE OSTI2020

Geometry-independent microstructure optimization for electron beam powder bed fusion additive manufacturing

The ability to manipulate the formation of microstructure is one of the potential advantages of additive manufacturing. Yet, the additive manufacturing process is riddled with complex interactions between processing parameters, geometry, and spot melting sequence which makes the task of controlling microstructure challenging. This paper proposes a method to solve the microstructure texture optimization problem by leveraging established optimization techniques and increasingly fast and accurate analytical heat transfer models. Respectively, we apply two different optimization algorithms, genetic algorithm and concurrent greedy line search, in order to induce arbitrarily shaped microstructure regions and we use a heat transfer model to evaluate the fitness of solutions in an iterative approach that is far more efficient than pure trial and error. The objective of the optimization algorithm is to control local thermal conditions – hence microstructure formation – during the melting process by manipulating the order and the dwell time of all the points of the infill pattern sequence. We then analyze the optimized sequence to determine important path characteristics that affect the microstructure formation. Through examples, we show that relative order and frequency, as measured by weighted average and variance of spot order, are important factors that influence the final characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enabling integrated AI control on DIII-D: a control system design with state-of-the-art experiments

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learning (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode (TM) free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine TM predictor-controller. There is also discussion on guiding principles for real-time ML controller design and implementation.

machine learning↗
DOE OSTI2026

pnnl/HADREC (33077-E)

This HADREC GUI is designed to visualize power system emergency control time-series data and facilitate the comparison of the newly developed meta strategy optimization algorithm-based emergency control with existing out-of-step-based emergency control approach, and a no-action case for reference.

Wang, Heng [Pacific Northwest National Laboratory ↗
DOE OSTI2021

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

TH-E-BRE-04: An Online Replanning Algorithm for VMAT

Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filtered (FF) and flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT planning system ((Monaco, Elekta), enabling the output of detailed beam/machine parameters of original VMAT plans generated based on planning CTs for FF or FFF beams. A SAM algorithm, previously developed for fixed-beam IMRT, was modified to allow the algorithm to correct for interfractional variations (e.g., setup error, organ motion and deformation) by morphing apertures based on the geometric relationship between the beam's eye view of the anatomy from the planning CT and that from the daily CT for each control point. The algorithm was tested using daily CTs acquired using an in-room CT during daily IGRT for representative prostate cancer cases along with their planning CTs. The algorithm allows for restricted MLC leaf travel distance between control points of the VMAT delivery to prevent SAM from increasing leaf travel, and therefore treatment delivery time. Results: The VMAT plans adapted to the daily CT by SAM were found to improve the dosimetry relative to the IGRT repositioning plans for both FF and FFF beams. For the adaptive plans, the changes in leaf travel distance between control points were < 1cm for 80% of the control points with no restriction. When restricted to the original plans' maximum travel distance, the dosimetric effect was minimal. The adaptive plans were delivered successfully with similar delivery times as the original plans. The execution of the SAM algorithm was < 10 seconds. Conclusion: The SAM algorithm can quickly generate deliverable online-adaptive VMAT plans based on the anatomy of the day for both FF and FFF beams.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Lyapunov controlled counterdiabatic quantum optimization

We introduce a quantum algorithm that integrates counterdiabatic (CD) protocols with quantum Lyapunov control (QLC) to address combinatorial optimization problems. This approach offers versatility, allowing implementation as either a digital-analog or purely digital algorithm based on selected control strategies. By examining spin-glass Hamiltonians, we illustrate how the algorithm can explore alternative paths to enhance solution outcomes compared to conventional CD techniques. This method reduces dependence on extensive higher-order CD terms and on classical optimization techniques, making it more suitable for existing quantum computing platforms. The combination of digital compression via CD protocols and the adaptable nature of QLC methods positions this approach as a promising candidate for near-term quantum devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Dynamic MLC leaf sequencing for integrated linear accelerator control systems

Purpose: Leaf positions for dynamic multileaf collimator (DMLC) intensity modulated radiation therapy must be closely synchronized with MU delivery. For the Varian C3 series MLC controller, if the planned trajectory (leaf position vs. MU) requires velocities exceeding the capability of the MLC, the leaves fall behind the planned positions, causing the controller to momentarily hold the beam and thereby introduce dosimetric errors. We investigated the merits of a new commercial linear accelerator, TrueBeam, that integrates MLC control with prospective dose rate modulation. If treatment is delivered at dose rates so high that leaves would fall behind, the controller reduces the dose rate such that harmony between MU and leaf position is preserved. Methods: For three sets of DMLC leaf trajectories, point doses and two-dimensional dose distributions were measured in phantom using an ionization chamber and film, respectively. The first set, delivered using both a TrueBeam and a conventional C3 controller, comprised a single leaf bank closing at planned velocities of 2.4, 7.1, and 14 cm/s. The maximum achievable leaf velocity for both systems was 3 cm/s. The remaining two sets were derived from clinical fluence maps using a commercial treatment planning system for a range of planned dose rates and were delivered using TrueBeam set to the maximum dose rate, 600 MU/min. Generating trajectories using a planned dose rate that is lower than the delivery dose rate effectively increased the leaf velocity constraint used by the planning system for trajectory calculation. The second set of leaf trajectories was derived from two fluence maps containing regions of zero fluence obtained from representative beams of two different patient treatment plans. The third set was obtained from all nine fields of a head and neck treatment plan. For the head and neck plan, dose-volume histograms of the spinal cord and target for each planned dose rate were obtained. Results: For the single closing leaf bank trajectories, the TrueBeam control system reduced the dose rate such that the leaf velocity was less than the maximum. Dose deviations relative to the 2.4 cm/s trajectory were less than 3%. For the conventional controller, the leaves repeatedly fell behind the planned positions until the beam hold threshold was reached, resulting in deviations of up to 19% relative to the 2.4 cm/s trajectory. For the two clinical fluence maps, reducing the planned dose rate reduced the dose in the zero fluence regions by 15% and 24% and increased the delivery time by 5 s and 14 s. No significant differences were noted in the high and intermediate dose regions measured using film. The DVHs for the head and neck plan showed a 10% reduction in cord dose for 20 MU/min relative to 600 MU/min sequencing dose rate, which was confirmed by measurement. No difference in target DVHs were observed. The reduction in cord dose increased total treatment time by 1.8 min. Conclusions: Leaf sequencing algorithms for integrated control systems should be modified to reflect the reduced importance of maximum leaf velocity for accurate dose delivery.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Ripple-Type Voltage Control for Extreme-Event Contingencies: Preprint

Frequent and intense extreme events make grid operation unprecedentedly challenging. Disruptive events could lead to dangerous voltage drops and even voltage collapse if corrective actions are not quickly taken. In this paper, we present a real-time algorithm for voltage control suitable for mitigating electric grid damage scenarios. In our strategy, when agents (generators, substations) experience a dangerous undervoltage, they first respond locally. When the local control resources are depleted, agents seek assistance from peer nodes over a communication network. The algorithm is simulated on a realistic test transmission system. Using fragility curve methodology, we simulate hurricane damages to the components of the synthetic 2000-bus grid representing the ERCOT system. Although being tested over a damaged grid after a hurricane event, our algorithm can be equally successfully applied to any other emergency low-voltage situation.

extreme weather events↗