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Risk Assessment of Obstacle Collision for UAVs under off-nominal conditions
Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace, demands the need of robust risk monitoring framework for assessing the safety of airspace, ground-structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV's predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV's trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.
Risk Assessment of Obstacle Collision for UAVs under Off-nominal Conditions
Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace demands the need of robust risk monitoring framework for assessing the safety of airspace, ground structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV’s predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV’s trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.
Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations
Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.
Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint
Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.
Lyapunov-Based Iterative Learning of Regions of Attraction for Autonomous Systems
This paper proposes a novel algorithm for estimating the region of attraction of equilibrium points for nonlinear discrete-time autonomous systems. The method iteratively expands an initial estimate of the region of attraction by constructing unions of sublevel sets of learned functions parametrized as neural networks. Unlike conventional techniques that rely on a single global Lyapunov function, the proposed approach provides a collection of local Lyapunov-like functions, enabling richer representations and potentially larger region of attraction estimates. These functions are trained using sampled state-space data, and their Lipschitz continuity ensures that desirable properties extend beyond the training samples. The devised strategy is tested via numerical simulations, demonstrating the effectiveness of the proposed approach.
Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces
A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.
Optical systems integrated modeling
An integrated modeling capability that provides the tools by which entire optical systems and instruments can be simulated and optimized is a key technology development, applicable to all mission classes, especially astrophysics. Many of the future missions require optical systems that are physically much larger than anything flown before and yet must retain the characteristic sub-micron diffraction limited wavefront accuracy of their smaller precursors. It is no longer feasible to follow the path of 'cut and test' development; the sheer scale of these systems precludes many of the older techniques that rely upon ground evaluation of full size engineering units. The ability to accurately model (by computer) and optimize the entire flight system's integrated structural, thermal, and dynamic characteristics is essential. Two distinct integrated modeling capabilities are required. These are an initial design capability and a detailed design and optimization system. The content of an initial design package is shown. It would be a modular, workstation based code which allows preliminary integrated system analysis and trade studies to be carried out quickly by a single engineer or a small design team. A simple concept for a detailed design and optimization system is shown. This is a linkage of interface architecture that allows efficient interchange of information between existing large specialized optical, control, thermal, and structural design codes. The computing environment would be a network of large mainframe machines and its users would be project level design teams. More advanced concepts for detailed design systems would support interaction between modules and automated optimization of the entire system. Technology assessment and development plans for integrated package for initial design, interface development for detailed optimization, validation, and modeling research are presented.
Dynamic modeling and control of a solid-sorbent CO{sub 2} capture process with two-stage bubbling fluidized bed adsorber reactor
Solid-sorbent-based CO{sub 2} capture processes have strong potential for reducing the overall energy penalty for post-combustion capture from the flue gas of a conventional pulverized coal power plant. However, the commercial success of this technology is contingent upon it operating over a wide range of capture rates, transient events, malfunctions, and disturbances, as well as under uncertainties. To study these operational aspects, a dynamic model of a solid-sorbent-based CO{sub 2} capture process has been developed. In this work, a one-dimensional (1D), non-isothermal, dynamic model of a two-stage bubbling fluidized bed (BFB) adsorber-reactor system with overflow-type weir configuration has been developed in Aspen Custom Modeler (ACM). The physical and chemical properties of the sorbent used in this study are based on a sorbent (32D) developed at National Energy Technology Laboratory (NETL). Each BFB is divided into bubble, emulsion, and cloud-wake regions with the assumptions that the bubble region is free of solids while both gas and solid phases coexist in the emulsion and cloud-wake regions. The BFB dynamic model includes 1D partial differential equations (PDEs) for mass and energy balances, along with comprehensive reaction kinetics. In addition to the two BFB models, the adsorber-reactor system includes 1D PDE-based dynamic models of the downcomer and outlet hopper, as well as models of distributors, control valves, and other pressure-drop devices. Consistent boundary and initial conditions are considered for simulating the dynamic model. Equipment items are sized and appropriate heat transfer options, wherever needed, are provided. Finally, a valid pressure-flow network is developed and a lower-level control system is designed. Using ACM, the transient responses of various process variables such as flue gas and sorbent temperatures, overall CO{sub 2} capture, level of solids in the downcomer and hopper have been studied by simulating typical disturbances such as change in the temperature, flowrate, and composition of the flue gas. To maintain the overall CO{sub 2} capture at a desired level in face of the typical disturbances, two control strategies were considered–a proportional-integral-derivative (PID)-based feedback control strategy and a feedforward-augmented feedback control strategy. Dynamic simulation results show that both the strategies result in unacceptable overshoot/undershoot and a long settling time. To improve the control system performance, a linear model predictive controller (LMPC) is designed. In summary, the overall results illustrate how optimizing the operation and control of carbon capture systems can have a significant impact on the extent and the rate at which commercial-scale capture processes will be scaled-up, deployed, and used in the years to come.
Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications
Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.
Nanoparticle self-assemblies with modern complexity
Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.
Interpreting and Stabilizing Machine-Learning Parametrizations of Convection
Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.
Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm
This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.
Application of a bayesian belief network model to reliability assessment of nuclear safety-related software - 207
As the instrumentation and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital-based systems, the need to incorporate software failures into NPP probabilistic risk assessments has arisen. In order to assess the probability of software failure on demand, a Bayesian belief network (BBN) model was developed which estimates the number of defects and the resulting probability of software failure on demand in nuclear safety-related software. To assess the feasibility of the BBN framework, the BBN model was applied to the prototype Integrated Digital Protection System-Reactor Protection System (IDiPS-RPS) to estimate the number of remaining faults and the software failure probability of a target software. The developmental- and V and V-activities carried out during the IDiPS-RPS development process were evaluated based on the well-defined checklist derived by the V and V team and were estimated based on expert elicitation. In addition, the attribute evaluations and the number of FPs of the target software is provided as the inputs for the BBN model. The application results showed the feasibility of using BBNs for quantifying software failure probabilities and several insights were gained from the applications of the BBN model. The proposed BBN framework can be applied to estimate the software failure probability for other safety-related NPP software and provide an insight on modeling the software development process that involves iterations between different development phases. (authors)
Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids
In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.
Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)
With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.
Vapor–liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks
Understanding fluid phase behavior, like VLE, in high P&T conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems and also forms an integral part of the design-modeling of the control processes in chemical industries. Two data-driven models have been proposed here in this study, each of which was competent in estimating VLE for the Type III binary systems of C 10 /N 2 and C 12 /N 2 , at pressures ranging up to 50–60 MPa. Both models showed better performance in predicting equilibrium pressure as compared to VLE modeled using PR-EOS. A modified model has also been proposed, capable of estimating the full phase envelope for the binary systems of C 10 /N 2 and C 12 /N 2 across a wide range of temperatures, and thus exhibit the mixture critical pressure at the concerned temperature. The diverse applicability of the proposed network architecture was further exhibited while estimating the VLE of a ternary system of C 1 /C 10 /N 2 .
Power and Communications Hardware-in-the-Loop CPS Architecture and Platform for DER Monitoring and Control Applications: Preprint
The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. The deployment of communications and computation has transformed the traditional physical power grid into a smart cyber-physical system (CPS). To fully understand the interdependency between physical grid and cyber netowrks, this study designed a power and communications hardware-in-the-loop (PCommHIL) CPS architecture, which enables the flexible verification of DER monitoring and control with hybrid communications architectures and Internet protocols. Design, development and case study of a PCommHIL testbed for the DER coordination are discussed in detail, and the proposed platform integrates DER devices, Advanced Metering Infrastructures (AMIs), and a suite of hybrid communications networks for distribution automation applications. Case study on DER situational awareness and Volt-Var control validates the efficacy of this proposed PCommHIL platform with hybrid communications designs. Results show that the HAN communication technologies play a critical role in hybrid designs and it is the bottleneck for DER applications. High performance communication technologies are highly recommended to be applied in the HAN for enhanced monitoring and real-time control of DERs.