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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Fuzzy geometry, entropy, and image information

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

Pal, Sankar K.↗
DOE OSTI2022

Case study on the novel permitting and authorization of PacWave South, a US grid-connected wave energy test facility: Development, challenges, and insights

Marine energy (i.e., energy from waves, tides, currents, and rivers) in the United States is a nascent industry. In particular, permitting processes—an uncertainty for industry advancement that can be costly and time consuming to navigate—have rarely been tested and used for marine energy. The novelty of the marine energy industry and utilization of open ocean permitting processes that were not originally developed for marine energy have led to extensive efforts to gain consensus amongst state and federal regulatory agencies to authorize marine energy projects. In 2021, Oregon State University successfully completed permitting of a wave energy test facility, called PacWave South, off the coast of Oregon, which is designed to advance wave energy research and development. This article documents the multi-year process that Oregon State University used to receive federal and state authorization for a pre-permitted commercial-scale grid connected facility by detailing the development of the test facility, management of uncertainty and challenges, and key decisions. The PacWave South case study provides insights for the larger marine energy community as the industry advances towards commercialization.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
DOE OSTI2022

Flexibility Auctions: A Framework for Managing Imbalance Risk

As the electricity generated by variable resources grows, system operators and variable resources have to manage challenging imbalances between forward and real-time markets. The Flexibility Auction is a novel approach for managing imbalances as it will allow resources with imbalance risk to hedge their production by buying flexibility options. The flexibility options are offered by grid-connected resources that can provide physical flexibility. This presentation will focus on the design of the Flexibility Auction, its properties, and how it can complement system-level services such as CAISO's proposed imbalance reserves. The presentation will include simple examples to illustrate the impact of the Flexibility Auction on the market participants and the system's imbalance risk.

auction↗
DOE OSTI2021

Modelling and control coordination scheme of a wind‐to‐hydrogen set for future renewable‐based power systems

New challenges regarding system stability and efficiency arise when power systems operate with a high penetration level of inverter‐based renewable sources (IBRSs) and few synchronous generators. Since IBRSs have been on the rise, to secure the stable operation of future power systems, IBRSs will be required to support systems without having to rely on remaining synchronous generators. Also, to efficiently manage the uncertainty of renewable production, power‐to‐gas technology can provide the required flexibility. This study proposes modelling and a control coordination scheme (CCS) of a wind‐to‐hydrogen (W2H) set to optimise electricity production from a variable‐speed wind turbine generator (WTG) while helping balance between supply and demand in a system. To achieve this, a grid‐forming (GFM) inverter‐based WTG is modelled and a set of electrolyser and fuel cell is integrated at the DC circuit of a GFM‐WTG to be coordinated. Furthermore, the CCS offers an opportunity to reduce the investment cost for deploying a W2H set by utilising the control capabilities of a WTG and reducing the need for an additional device. The performance of the proposed W2H set with the CCS was verified considering the variations in system load and wind speed by using Power System Computer Aided Design (PSCAD)/ElectroMagnetic Transients including Direct Current (EMTDC).

Kim, Jinho↗
DOE OSTI2022

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Physics↗
DOE OSTI2020

Collaborative Distribution System Restoration Planning and Real-Time Dispatch Considering Behind-the-Meter DERs

Modern distribution systems are becoming more resilient against power outages thanks to the flexible generation capacity provided by the increasing integration of distributed energy resources (DERs). Compared to utility-scale DERs, which can be directly controlled by system operators, small-scale, behind-the-meter DERs are much more difficult to coordinate despite being the vast majority. Two major obstacles to the implementation of DERs for service restoration are the coordination of behind-the-meter DERs and the management of uncertainty. In this paper, a novel distribution system restoration strategy is proposed to overcome these two obstacles. The flexibility of be-hind-the-meter DERs is quantified and integrated into the restoration planning models within a hierarchical structure. The model predictive control technique is employed to enable adaptive restoration planning solution adjustments to accommodate fault scenarios and forecast errors. Besides, real-time power dispatch models are proposed to maintain power balance using available DER capability. Comprehensive integration of adaptive restoration planning schemes and real-time dispatch results are achieved through a collaborative restoration framework. Finally, the effectiveness of the proposed restoration strategy is verified through a modified IEEE 123-bus system and a real-world utility system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AutoUncertainties: A Python Package for Uncertainty Propagation

Propagation of uncertainties is of great utility in the experimental sciences. While the rules of (linear) uncertainty propagation are straightforward, managing many variables with uncertainty information can quickly become complicated in large scientific software stacks. Often, this requires programmers to keep track of many variables and implement custom error propagation rules for each mathematical operator and function. The Python package AutoUncertainties, described here, provides a solution to this problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Interfacing the expert: Characteristics and requirements for the user interface in expert systems

Because expert systems deal with new sets of problems presenting unique interface requirements, special issues requiring special attention are presented to user interface designers. External knowledge representation (how knowdedge is represented across the user interface), modes of user-system interdependence (advisory, cooperative, and autonomous), and management of uncertainty (deciding what actions to take or recommend based on incomplete evidence) are discussed.

Potter, Andrew↗
NASA NTRS2000

A Probabilistic Framework for the Validation and Certification of Computer Simulations

The paper presents a methodology for quantifying, propagating, and managing the uncertainty in the data required to initialize computer simulations of complex phenomena. The purpose of the methodology is to permit the quantitative assessment of a certification level to be associated with the predictions from the simulations, as well as the design of a data acquisition strategy to achieve a target level of certification. The value of a methodology that can address the above issues is obvious, specially in light of the trend in the availability of computational resources, as well as the trend in sensor technology. These two trends make it possible to probe physical phenomena both with physical sensors, as well as with complex models, at previously inconceivable levels. With these new abilities arises the need to develop the knowledge to integrate the information from sensors and computer simulations. This is achieved in the present work by tracing both activities back to a level of abstraction that highlights their commonalities, thus allowing them to be manipulated in a mathematically consistent fashion. In particular, the mathematical theory underlying computer simulations has long been associated with partial differential equations and functional analysis concepts such as Hilbert spares and orthogonal projections. By relying on a probabilistic framework for the modeling of data, a Hilbert space framework emerges that permits the modeling of coefficients in the governing equations as random variables, or equivalently, as elements in a Hilbert space. This permits the development of an approximation theory for probabilistic problems that parallels that of deterministic approximation theory. According to this formalism, the solution of the problem is identified by its projection on a basis in the Hilbert space of random variables, as opposed to more traditional techniques where the solution is approximated by its first or second-order statistics. The present representation, in addition to capturing significantly more information than the traditional approach, facilitates the linkage between different interacting stochastic systems as is typically observed in real-life situations.

Ghanem, Roger↗
NASA NTRS2016

Model Based Mission Assurance: Emerging Opportunities for Robotic Systems

The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).

Mission Assurance↗
NASA NTRS2017

A Framework for Reliability and Safety Analysis of Complex Space Missions

Long duration and complex mission scenarios are characteristics of NASA's human exploration of Mars, and will provide unprecedented challenges. Systems reliability and safety will become increasingly demanding and management of uncertainty will be increasingly important. NASA's current pioneering strategy recognizes and relies upon assurance of crew and asset safety. In this regard, flexibility to develop and innovate in the emergence of new design environments and methodologies, encompassing modeling of complex systems, is essential to meet the challenges.

Safety Analysis↗
NASA NTRS2018

Coordinating AgMIP Data and Models Across Global and Regional Scales for 1.5°C and 2.0°C Assessments

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some bread basket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’.

interdisciplinary↗
NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health and off-nominal battery power consumption. Machine learning methods such as Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption, and operation time. The UAV navigates the grid by taking actions to move in either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗
NASA NTRS2024

NASA Risk Management Handbook: Version 2.0, Part 2

The purpose of this handbook is to provide an in-depth reference for the practice of risk management in NASA, updating the guidance offered in its original version, NASA/SP-2011-3422 (November 2011), and closely aligning the updated guidance with the current NASA Procedural Requirements for Agency Risk Management, NPR 8000.4, and the parent NASA Policy Directive for NASA Governance and Strategic Management, NPD 1000.0 (January 2020). NPD 1000.0 introduces with emphasis the concept of “Risk Leadership,” making it a fundamental tenet and pillar of the risk management culture that it advocates for the Agency. NPR 8000.4 applies this concept and establishes Risk Management (RM) requirements for the Agency as an integrated enterprise, as well as the RM requirements for portfolio elements within the enterprise. Such elements include the various programs and projects that contribute to the Agency’s objectives and the various institutional activities carried out by entities that contribute to mission support. The present version of the handbook also emphasizes the integration of risk management processes across activity and project life cycles and their coordination and interaction with day-to-day programmatic and organizational functions. Areas of application of risk assessment and management that were not covered with specific guidance in the preceding version are addressed in this version with in-depth examples. The handbook is structured into two parts, whose chapters are in turn organized in a sequential order intended to facilitate a gradual and progressive introduction of the reader to risk management principles and practices. Part 1 of the handbook is dedicated to the introduction of the basic foundations of the NASA integrated risk management framework, the related fundamental risk concepts, the description of the risk management and decision processes that are to be implemented within the framework, the discussion of the risk assessment techniques that should be utilized in support of such processes, and the management and organizational interactions and interfaces that should be enabled to implement an effective integration of risk management activities within the Agency. Part 2 provides self-contained, end-to-end examples of application of the processes and techniques introduced in Part 1, in the context of both programmatic (i.e., project and/or mission related) and institutional activities.

Risk Leadership↗