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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cryogenic Plant Integration for the Colossus milli-Kelvin Platform at Fermilab

The Colossus platform at Fermilab will be the largest and most powerful 3He/4He dilution-cooled cryogenic system constructed to-date. Perhaps its primary innovation will be in the integration of a liquid helium cryogenics plant to cool the stages typically cooled by mechanical cryocoolers in commercially available cryogen-free dilution refrigerators. This design shift carries with it important implications for the future of cryogenics associated with quantum computing due to the inherently greater efficiencies of helium cryogenic plants when compared to the use of multitude of independent mechanical cryocoolers. Construction of Colossus is expected to begin in 2023 with a target of commencing operations in 2025.

Tatkowski, G.↗
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation

Negatively weighted events, which appear in the Monte Carlo (MC) simulation of particle collisions, significantly increases the computational resource requirements of current and future collider experiments. This paper introduces and theoretically discusses an MC technique called ARCANE reweighting for reducing or eliminating negatively weighted events. The technique works by redistributing (via an additive reweighting) the contributions of different pathways within an event generator that lead to the same final event. The technique is exact and does not introduce any biases in the distributions of physical observables. A companion paper demonstrates the technique for a physics example.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

WRPS MEETING THE CHALLENGE OF TANK WASTE

Washington River Protection Solutions (WRPS) is the Hanford tank operations contractor, charged with managing one of the most challenging environmental cleanup projects in the nation. The U.S. Department of Energy hired WRPS to manage 56 million gallons of high-level radioactive waste stored in 177 underground tanks. The waste is the legacy of 45 years of plutonium production for the U. S. nuclear arsenal. WRPS mission is three-fold: safely manage the waste until it can be processed and immobilized; develop the tools and techniques to retrieve the waste from the tanks, and build the infrastructure needed to deliver the waste to the Waste Treatment Plant (WTP) when it begins operating. WTP will 'vitrify' the waste by mixing it with silica and other materials and heating it in an electric melter. Vitrification turns the waste into a sturdy glass that will isolate the radioactivity from the environment. It will take more than 20 years to process all the tank waste. The tank waste is a complex highly radioactive mixture of liquid, sludge and solids. The radioactivity, chemical composition of the waste and the limited access to the underground storage tanks makes retrieval a challenge. Waste is being retrieved from aging single-shell tanks and transferred to newer, safer double-shell tanks. WRPS is using a new technology known as enhanced-reach sluicing to remove waste. A high-pressure stream of liquid is sprayed at 100 gallons per minute through a telescoping arm onto a hard waste layer several inches thick covering the waste. The waste is broken up, moved to a central pump suction and removed from the tank. The innovative Mobile Arm Retrieval System (MARS) is also being used to retrieve waste. MARS is a remotely operated, telescoping arm installed on a mast in the center of the tank. It uses multiple technologies to scrape, scour and rake the waste toward a pump for removal. The American Reinvestment and Recovery Act (ARRA) provided nearly $326 million over two-and-a-half years to modernize the infrastructure in Hanford's tank farms. WRPS issued 850 subcontracts totaling more than $152 million with nearly 76 percent of that total awarded to small businesses. WRPS used the funding to upgrade tank farm infrastructure, develop technologies to retrieve and consolidate tank waste and extend the life of two critical operating facilities needed to feed waste to the WTP. The 222-S Laboratory analyzes waste to support waste retrievals and transfers. The laboratory was upgraded to support future WTP operations with a new computer system, new analytical equipment, a new office building and a new climate-controlled warehouse. The 242-A Evaporator was upgraded with a control-room simulator for operator training and several upgrades to aging equipment. The facility is used to remove liquid from the tank waste, creating additional storage space, necessary for continued waste retrievals and WTP operation. The One System Integrated Project Team is ajoint effort ofWRPS and Bechtel National to identify and resolve common issues associated with commissioning, feeding and operating the Waste Treatment Plant. Two new facilities are being designed to support WTP hot commlsslomng. The Interim Hanford Storage project is planned to store canisters of immobilized high-level radioactive waste glass produced by the vitrification plant. The facility will use open racks to store the 15-foot long, two-foot diameter canisters of waste, which require remote handling. The Secondary Liquid Waste Treatment Project is a major upgrade to the existing Effluent Treatment Facility at Hanford so it can treat about 10 million gallons of liquid radioactive and hazardous effluent a year from the vitrification plant. The One System approach brings the staff of both companies together to identify and resolve WTP safety issues. A questioning attitude is encouraged and an open forum is maintained for employees to raise issues. WRPS is completing its mission safely with record-setting safety performance. Since WRPS took over the Hanford Tank Operations Contract in October 2008, the recordable injury rate has decreased 43 percent, while the lost work-days rate decreased by 30 percent. The company recently surpassed three million hours worked without a lost workday accident.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

White Paper on DOE-HEP Accelerator Modeling Science Activities

Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within the Department of Energy (DOE) Office of High Energy Physics (HEP) and (b) increasing the community-wide coordination and integration of code development.

Vay, Jean-Luc↗
DOE OSTI2016

Turbulence Model Study for Pressure and Velocity Distributions for Molten Salts in a Crossflow Tube Bundle

Empirical pressure drop correlations for various compositions of molten salts in crossflow across tube bundles/banks are limited due to the scarcity of experimental data for these fluids. This lack of experimental data presents a limitation when designing heat exchangers for Molten Salt Reactors (MSRs) that utilize cross-flow configurations with turbulent molten salts flowing in the shell side. In an effort to provide a starting point for future design and experimental work, a computational fluid dynamics (CFD) campaign is being conducted to estimate pressure drops over a range of Reynolds numbers, pitch to diameter ratios, and for both staggered and in-line tube banks. In an effort to continue this work, the study of various turbulence models were conducted to determine the applicability for steady RANS simulations. An in-line tube bank with FLiBe (LiF-BeF{sub 2}), a frequently selected molten salt in MSRs, was selected for these simulations. The estimated pressure drops and pressure and velocity distributions for each turbulence model was compared to determine the applicability. The k-ε turbulence model family was found to be the most appropriate model for steady Reynolds averaged Navier Stokes within this type of domain. Difference turbulence models were investigated for simulation of molten salt experiencing crossflow within an in-line tube bundle. The flow was simulated using a steady RANS formulation with different families of turbulence models. The different turbulence models provide a view of this specific flow behavior potential requirements and applicable for steady RANS. Of particular note, the k-ε models were able to converge easily within this domain. The predicted behavior for both models were observed to be mostly similar for both velocity and pressure distributions. Whereas, k-ω SST and SA models did not converge well and had observable differences for the predictions of pressure and velocity distributions. The global behaviors were generally similar, but the variations occurring suggest k-ω SST and SA turbulence models are not useful in the steady RANS formulation of this domain. When comparing to the empirical pressure drop, each turbulence model resulted in overpredictions ranging from 8.7% and 19.75% difference. The standard low-Re k-ε model having the lowest difference is likely the better model for simplified predictions for pressure drops. Further investigations will be conducted to determine the convergence issues for the k-ω SST and SA turbulence models. These might be due to the flow geometry or potentially a revision of the meshing is needed. The flow behavior may not be resolved using these models in a steady formulation due to inherent unsteadiness of the flow. (authors)

42 ENGINEERING↗
DOE OSTI2024

Preliminary analysis of TREAT free-field experiments using OpenMC

This work analyses activation calculations for dosimetry materials during a steady-state irradiation in the Transient Reactor Test (TREAT) reactor core. Hence, we developed a workflow based on the Monte Carlo code OpenMC alongside a custom depletion solver. The irradiation-induced activity as a function of time is computed, and several sensitivity studies are performed to evaluate uncertainty. This study has shown activity computations are sensitive to flux amplitude, irradiation time, atoms quantity and microscopic cross sections. Stochastic uncertainties have been propagated to evaluate the activity uncertainty for each dosimetry material. Most uncertainties are below our target of 3%, which demonstrates OpenMC as a powerful predictive and analysis tool. The precise results obtained through this newly developed computation scheme will be used in future experiments to characterize quantities of interest when operating the TREAT reactor in new configurations.

61 - RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

TINES - Time Integration, Newton and Eigen Solver v. 1.0

SAND2021-1505 O. TINES is an open source software providing math infrastructure for solving many stiff time ordinary differential equations (ODEs) and/or differential algebraic equations (DAEs) using a batch hierarchical parallelism. The code is written using a parallel programming model (i.e., Kokkos) to future-proof the next generation parallel computing platforms such as GPU accelerators. This code is developed to support Exascale Catalytic Chemistry (ECC) Project. The code provides fundamental math helpers that can aid other research projects. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kim, Kyungjoo↗
DOE OSTI2015

Performance of today’s dual energy CT and future multi energy CT in virtual non-contrast imaging and in iodine quantification: A simulation study

Purpose: To study the performance of different dual energy computed tomography (DECT) techniques, which are available today, and future multi energy CT (MECT) employing novel photon counting detectors in an image-based material decomposition task. Methods: The material decomposition performance of different energy-resolved CT acquisition techniques is assessed and compared in a simulation study of virtual non-contrast imaging and iodine quantification. The material-specific images are obtained via a statistically optimal image-based material decomposition. A projection-based maximum likelihood approach was used for comparison with the authors’ image-based method. The different dedicated dual energy CT techniques are simulated employing realistic noise models and x-ray spectra. The authors compare dual source DECT with fast kV switching DECT and the dual layer sandwich detector DECT approach. Subsequent scanning and a subtraction method are studied as well. Further, the authors benchmark future MECT with novel photon counting detectors in a dedicated DECT application against the performance of today’s DECT using a realistic model. Additionally, possible dual source concepts employing photon counting detectors are studied. Results: The DECT comparison study shows that dual source DECT has the best performance, followed by the fast kV switching technique and the sandwich detector approach. Comparing DECT with future MECT, the authors found noticeable material image quality improvements for an ideal photon counting detector; however, a realistic detector model with multiple energy bins predicts a performance on the level of dual source DECT at 100 kV/Sn 140 kV. Employing photon counting detectors in dual source concepts can improve the performance again above the level of a single realistic photon counting detector and also above the level of dual source DECT. Conclusions: Substantial differences in the performance of today’s DECT approaches were found for the application of virtual non-contrast and iodine imaging. Future MECT with realistic photon counting detectors currently can only perform comparably to dual source DECT at 100 kV/Sn 140 kV. Dual source concepts with photon counting detectors could be a solution to this problem, promising a better performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗
DOE OSTI2018

A learning-based data-driven forecast approach for predicting future reservoir performance

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. Here in this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO 2 ) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.

42 ENGINEERING↗
DOE OSTI2024

Quantum Computing for Energy-Related Applications

Growing interest in quantum computing and simulations have created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. While quantum computing is considered as a paradigm shift in our basic understanding of physical computation, effective implementation of quantum computing in energy applications also depends on progress and development in the dimensions of both quantum computing hardware and quantum computing algorithms. To fully address the status and future challenges of quantum information science (QIS) applied within the energy sector, in this presentation, we firstly summarize recent advancements on the applications of quantum computing to energy infrastructure and materials, complex energy system processes, advanced manufacturing, and energy system security. Then, we will demonstrate the results of quantum computing both on a simulator and a quantum device accessing from OLCF. Our first example is to use the variational quantum eigensolver (VQE) with a unitary coupled cluster with singles and doubles (UCCSD) ansatz to simulate a series of LixHyq molecules (q=-1, 0, +1). The obtained results showed that the quantum computing VQE-UCCSD is comparable to classical CCSD for small systems like LiH with respect to full configuration interaction (FCI). Targeting on CO2 capture application, our second example is to use VQE to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3 to form H2NCOOH. This research showcases quantum computing applications in the study of CO2 capture reactions.

Duan, Yuhua↗
DOE OSTI2021

America Resilient Climate Conference

On April 14, 2021, scientists, policymakers, and other interested parties from research institutes, academia, and other organizations gathered together virtually at the America Resilient Climate Conference to discuss one of the most pressing challenges of the 21st century: building resilience to climate change. Climate change affects the security and health of all Americans. Coastal areas are enduring more frequent and severe flooding due to sea level rise and storm surge; western states and Alaska have experienced increasingly devastating wildfires, driven in part by hotter, drier, and longer fire seasons; and communities across the nation have suffered through extreme precipitation events and heat waves. Even if emissions are reduced aggressively in the near future, the world—and the United States—will continue to feel the impacts of climate change for decades to come, due to the continued accumulation of greenhouse gasses in the atmosphere. Consequently, it is essential to act now to protect natural and human assets from the gradual—as well as extreme—impacts of a changing climate. To build resilient communities, leaders and community members need science-based information about the potential impacts climate change will have decades into the future and for specific regions. Therefore, it is essential to develop high-resolution climate models that can project both various climate impacts and the interactions between earth system variables and humans down to regional and local scales. Collecting and curating data for such models and their computational requirements poses large challenges. In the future, artificial intelligence will be needed to increase their accuracy and reduce associated uncertainties. The investments in Earth system science and artificial intelligence made by the U.S. Department of Energy and other federal entities will be essential in addressing these challenges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗
DOE OSTI2018

Experimental and computational analysis of failure mechanisms in unidirectional carbon fiber reinforced polymer laminates under longitudinal compression loading

Here, his study investigates the failure mechanisms of notched unidirectional (UD) carbon fiber reinforced polymer (CFRP) laminates subjected to longitudinal compressive loading. A sequence of failure initiation and propagation is observed based on optical microscopy images of specimens during failure. The micrographs reveal that the main failure mechanism in UD laminates is fiber kinking failure. The influence of manufacturing defects and voids on fiber kinking mechanisms is also analyzed. The results show that the voids in the resin-rich area lead to kink-band splitting, while in some cases the voids cause the kink-band to deflect in a new direction depending on the location of the voids. The next focus of this study is to propose a computational micromechanics model considering local fiber waviness to study the fiber kinking failure mechanism. The computational results on kink-band formation and stress-strain response show good consistency with our experimental analysis. Lastly, the failure envelopes of σ 11 -σ 22 and σ 11 -τ 12 subjected to combined loading conditions are obtained from the computational model, which provide essential inputs for future theoretical failure criteria.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Interdisciplinary Education through “Edutainment”: Electric Grid Resilient Control Systems Course

As energy companies and governments attempt to get more from the existing power grid and other critical infrastructures, more automatic control systems are being applied 1-2. With this greater reliance on network-based, digital automation and the stresses of pressing existing infrastructure for greater performance, the power grid and underlying systems have become more susceptible to both malicious attacks and unexpected, natural threats. Governments and other stakeholders have chosen to address infrastructure issues by the implementation of a smarter grid. In the smart grid, operators and control systems supervise power generation, distribution, transmission, and loads to utilize these assets most efficiently3. Such extensive monitoring and control over a distributed system cause complexity that challenge systems designers and human operators in new ways. In addition, cyber vulnerability of these systems has been illustrated in many recent articles on malicious attacks to electric power systems and other similar infrastructure for natural gas, water and communications4. It is therefore critical in the next generation of control systems that resilience plays a large and critical role in the grid design and development. Resilient control systems is a field of research that seeks solutions to the complexity through a holistic approach that combines cognitive science, computer security, communications, and control systems. To enable future researchers and practitioners to assist with designing more resilient systems, science, technology, engineering and mathematics education needs to incorporate interdisciplinary topics. While electrical engineering and computer science programs in the nation include a cyber-security perspective, few if any have focused on the unique control system aspects. Human cognitive aspects are most definitely not addressed in technology education discourse. To this end, a class and education tools in resilient control systems have been created.

17 WIND ENERGY↗