Search NASA⌕ Search

SEARCH · Search NASA

Results for “controller information tool”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Information Management Workflow and Tools Enabling Multiscale Modeling Within ICME Paradigm

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to establish workflow for and demonstrate a unique set of web application tools for linking NASA GRC's Integrated Computational Materials Engineering (ICME) Granta MI database schema and NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset are presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.

Materials Engineering↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
NASA NTRS2003

Discrete Abstractions of Hybrid Systems: Verification of Safety and Application to User-Interface Design

Human interaction with a complex control system involves the user, the automation’s discrete mode logic, and the underlying continuous dynamics of the physical system. The user-interface of such systems always displays a reduced set of information about the entire system. Designing interfaces such that all the pertinent information is available and assuring that this information is correct is important for any user-interface, but especially so for safety-critical systems such as automotive systems and autopilots. Here we describe a methodology for the analysis of hybrid control systems that incorporate user interaction, with the goal of assuring that the information provided to the user is correct. That is, the user-interface must contain all information necessary to safely complete a desired procedure or task. We begin with a hybrid system model which incorporates discrete mode logic as well as nonlinear continuous dynamics. Using a hybrid computational tool for reachability, we find the largest region of the state-space in which we can guarantee the state of the system can always remain – this is the safe region of operation. By implementing a controller for safety which arises from this computation, we mathematically guarantee that this safe region is invariant, meaning that the system will always remain within the safe region if the determined controller is used on the boundary of the safe region. Verification within a hybrid framework allows us to account for the continuous dynamics underlying the discrete representations displayed to the user. Using the computed invariant regions as discrete states, we can abstract a discrete event system from this hybrid system with safety restrictions. This abstraction can be used to determine what information must be provided on the display. Furthermore, in cases in which an interface already exists, the abstraction provides the necessary input into existing interface verification methods. We provide two examples: a car traveling through a yellow light at an intersection and an aircraft autopilot in an automatic landing/go-around maneuver. The examples demonstrate the applicability of this methodology to hybrid systems that have operational constraints we can pose in terms of safety. This methodology differs from existing work in hybrid system verification in that we directly account for the user’s interactions with the system.

Meeko Oishi↗
NASA NTRS1991

The Generic Spacecraft Analyst Assistant (GenSAA): A tool for automating spacecraft monitoring with expert systems

Flight Operations Analysts (FOAs) in the Payload Operations Control Center (POCC) are responsible for monitoring a satellite's health and safety. As satellites become more complex and data rates increase, FOAs are quickly approaching a level of information saturation. The FOAs in the spacecraft control center for the COBE (Cosmic Background Explorer) satellite are currently using a fault isolation expert system named the Communications Link Expert Assistance Resource (CLEAR), to assist in isolating and correcting communications link faults. Due to the success of CLEAR and several other systems in the control center domain, many other monitoring and fault isolation expert systems will likely be developed to support control center operations during the early 1990s. To facilitate the development of these systems, a project was initiated to develop a domain specific tool, named the Generic Spacecraft Analyst Assistant (GenSAA). GenSAA will enable spacecraft analysts to easily build simple real-time expert systems that perform spacecraft monitoring and fault isolation functions. Lessons learned during the development of several expert systems at Goddard, thereby establishing the foundation of GenSAA's objectives and offering insights in how problems may be avoided in future project, are described. This is followed by a description of the capabilities, architecture, and usage of GenSAA along with a discussion of its application to future NASA missions.

Hughes, Peter M.↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis

Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities a significant computational challenge for complex real-world problems, particularly when failure is a rare event. To implement multifidelity modeling, estimators that efficiently combine information from multiple models/sources are necessary. In past works, the variance reduction techniques of control variates (CV) and importance sampling (IS) have been leveraged for this task. In this paper, we present the CVIS framework—a creative take on a coupled CV and IS estimator for bifidelity reliability analysis. The framework addresses some of the practical challenges of the CV method by using an estimator for the control variate mean and sidestepping the need to estimate the covariance between the original estimator and the control variate through a clever choice for the tuning constant. Furthermore, the task of selecting an efficient IS distribution is also considered, with a view towards maximally leveraging the bifidelity structure and maintaining expressivity. Additionally, a diagnostic is provided that indicates both the efficiency of the algorithm as well as the relative predictive quality of the models utilized. Finally, the behavior and performance of the framework is explored through analytical and numerical examples.

Markov chain Monte Carlo↗
DOE OSTI2021

Synchronized Waveforms – A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1972

The management process, management information and control systems, and cybernetics

An attempt has been made to analyze the strengths and weaknesses of the cybernetics approach as applied to management. The conclusion is that cybernetics can serve not only as a conceptual philosophical aid, but also as an operational tool in both managerial planning and control. So far, however, most of its promise is yet unrealized; especially in the planning sphere. Only in the area of control of operations has the impact of this promising field shown tangible results.

Zannetos, Z. S.↗
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Recent Advancements in spoken language processing technologies have enabled the development of reliable automation tools to assist air traffic control (ATC) operations. These advancements present a unique opportunity to strategically implement digital taxi instructions for aircraft movement on the ground. Digital taxi instructions issue taxiing procedures to pilots as textual or graphical instructions. There are several benefits of digital instructions, including a reduction in radio congestion, elimination of communication errors, and improved aircraft monitoring capabilities. Natural language understanding (NLU) models can extract digital taxi instructions from verbal instructions issued by air traffic controllers. This capability enables the implementation of a digital taxi communication framework with minimal changes to the existing air traffic controller operational environment. We explore a novel application for NLU: automatically generating digital taxi instructions from air traffic controller speech. We describe the development of an annotation scheme to represent (aircraft) ground traffic communications in the US National Airspace System (NAS).Our annotation scheme uses intent classification and slot filling to extract taxi instructions, enabling NLU models to leverage syntactic information. Several neural network models were trained to categorize the controller’s intent and label information relevant to his or her intent.Our research demonstrates that it is feasible to use NLU to automatically generate digitaltaxi instructions, suggesting that it is a powerful tool for the implementation of digital taxi communications.

Hillel Steinmetz↗
NASA NTRS2013

Prospective Safety Analysis and the Complex Aviation System

Fatal accident rates in commercial passenger aviation are at historic lows yet have plateaued and are not showing evidence of further safety advances. Modern aircraft accidents reflect both historic causal factors and new unexpected "Black Swan" events. The ever-increasing complexity of the aviation system, along with its associated technology and organizational relationships, provides fertile ground for fresh problems. It is important to take a proactive approach to aviation safety by working to identify novel causation mechanisms for future aviation accidents before they happen. Progress has been made in using of historic data to identify the telltale signals preceding aviation accidents and incidents, using the large repositories of discrete and continuous data on aircraft and air traffic control performance and information reported by front-line personnel. Nevertheless, the aviation community is increasingly embracing predictive approaches to aviation safety. The "prospective workshop" early assessment tool described in this paper represents an approach toward this prospective mindset-one that attempts to identify the future vectors of aviation and asks the question: "What haven't we considered in our current safety assessments?" New causation mechanisms threatening aviation safety will arise in the future because new (or revised) systems and procedures will have to be used under future contextual conditions that have not been properly anticipated. Many simulation models exist for demonstrating the safety cases of new operational concepts and technologies. However the results from such models can only be as valid as the accuracy and completeness of assumptions made about the future context in which the new operational concepts and/or technologies will be immersed. Of course that future has not happened yet. What is needed is a reasonably high-confidence description of the future operational context, capturing critical contextual characteristics that modulate both the likelihood of occurrence of hazards, and the likelihood that those hazards will lead to negative safety events. Heuristics extracted from scenarios, questionnaires, and observed trends from scanning the aviation horizon may be helpful in capturing those future changes in a way conducive to safety assessment. What is also needed is a checklist of potential sources of emerging risk that arise from organizational features that are frequently overlooked. The ultimate goal is to develop a pragmatic, workable method for using descriptions of the future aviation context, to generate valid predictions of safety risks.

prospection↗
NASA NTRS2018

Multi-Operator Multi-UAV (MOMU) Control: Exploring the Influence of Sensor Tools and Playbook Task Delegation

New concepts of operations for Unmanned Aerial Vehicles (UAVs) will require a change from the current 2:1 operator to vehicle crew configuration. One particular control paradigm, largely driven by logistics, manpower, and training burdens, as well as the desire to force multiply, involves a single operator simultaneously managing multiple UAVs. This mode of operations has shown to significantly increase cognitive workload and decrease situation awareness, as operators are required to simultaneously attend to multiple sources of information. One potential way to mitigate potential drawbacks of multi-vehicle control by a single operator is to migrate to a multi-operator multi-UAV (MOMU) crew configuration, whereby M operators control N (> M) vehicles. This type of crew configuration can be organized in several ways to dynamically manage cognitive workload, match operator qualifications and skills to mission requirements, increase utilization of available assets, and thereby achieve maximum force multiplication. The present experiment examined task performance in a simulated MOMU environment and evaluated the potential benefits of sensor management aids ("Tools") as well as integrated sensor and flight automation ("Plays") compared to a fully manual condition ("Manual"). Tools support the operator by facilitating rapid understanding and management of sensor information, while the Plays support the operator by offloading/automating subtasks. Six pairs of participants were recruited for this study and tasked with sharing a pool of UAVs in order to conduct reconnaissance, surveillance, and target acquisition (RSTA) missions in adjacent Areas of Operation (AOs). Participants were given four tasks to accomplish, in order of priority: 1) prosecute High Value Targets (HVTs); 2) identify / track targets (military vehicles); 3) identify / mark civilian vehicles; and 4) respond to chat messages. Performance on the mission tasks was measured in terms of accuracy and reaction time. A composite mission score was also calculated using a payoff matrix that weighted each task according to priority. The results indicate that Playbook demonstrated better performance overall with higher accuracy rates and the highest composite score compared to Tools and Manual. The implications of these results to supporting future MOMU concepts of operations is discussed.

Playbook↗
DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

HAZARD ANALYSIS OF DIGITAL ENGINEERED SAFETY FEATURES ACTUATION SYSTEM IN ADVANCED NUCLEAR POWER PLANTS USING A REDUNDANCY-GUIDED APPROACH

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. This paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the triggers of potential single points of failure in the design of digital safety systems in advanced plant designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Deploying Software-Defined Networking in Operational Technology Environments

Software Defined Networking for Operational Technologies, referred to as OT-SDN, is a leading technology to secure critical infrastructure and command and control (C2) systems. As the name implies, OT-SDN networks are programmable, which allows system owners to utilize the characteristics of their physical process to inform the security of their network. There are best practices for deploying OT-SDN into an environment, whether it is all at once or over time (hybrid) that the network is converted to SDN technologies. Through the development of data mining tools and standardized process control, OT-SDN can be deployed reliably. These tools will minimize or eliminate any communication failures during the transition and provide the network owner with complete documentation of their environment. This documentation could enable or facilitate the network owner to pass any audits or policy checks (Authority to Operate) before being allowed to utilize the OT-SDN infrastructure.

Software Defined Networking, Operational Technolog↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling Riverine Dissolved and Particulate Organic Carbon Fluxes from Two Small Watersheds in the Northeastern United States

The coupled carbon (C) cycle across terrestrial and aquatic environments at the watershed scale has been identified as an important, but poorly constrained component of the global carbon budget. Here, we extended Soil and Water Assessment Tool (SWAT) with coupled riverine particulate organic carbon (POC) and dissolved organic carbon (DOC) modules (referred to as SWAT-C hereafter). Results show that SWAT-C reproduced daily POC and DOC fluxes well in two watersheds in the Northeastern United States. We found that SWAT-C tended to underestimate high flows and peak DOC and POC fluxes. Uncertainty analysis indicated flux uncertainties associated with POC and DOC simulation were larger than those for flow simulation. Sensitive parameters controlling POC and DOC biogeochemical processes were identified along with how these parameters influence mechanisms underlying C cycling. We anticipate that the tool developed and applied here will inform C related ecosystem services in watershed assessment and planning.

Dissolved organic carbon, Particulate organic carb↗
DOE OSTI2020

Uncovering the Functional Role of Coherent Phonons during the Photoinduced Phase Transition in a Molecular Crystal

The atomic motions that make up phonons and molecular vibrations in molecular crystals influence their photophysical and electronic properties, including polaron formation, carrier mobility, and phase transitions. Discriminating between spectator and driving motions is a significant challenge hindering optimization. Unlocking this information and developing fine-tuned controls over actively participating phonon modes would not only lead to a stronger understanding of photochemistry but also provide a significant new tool in controlling solid state chemistry. We present a strategy using rationally designed double pulses to unveil the unique function of specific excited state phonon modes. Using ultrafast spectroscopy, we identified 50 and 90 cm –1 phonons involved in modulating the photoinduced spin-Peierls melting of potassium tetracyanoquinodimethane crystals. Here, we show that the 50 cm –1 phonon specifically corresponds to the coherent nuclear wavepacket involved in the charge transfer component of the overall spin-Peierls phase melting process, while the 90 cm –1 phonon facilitates the phase transition component.

74 ATOMIC AND MOLECULAR PHYSICS↗