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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Integration of Data Analytics with Plant System Health Program

This report summarizes the R&D activities of the Plant Health Management (PHM) project during fiscal year 2020 (FY20). This project focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate system sources of risk. This project development lives in cooperation with the Risk Informed Asset Management (RIAM) project. The RIAM project, in fact, uses the reliability models and data generated by the PHM project to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture). This year’s activities for the PHM project focused mainly on the development of two classes of models. The first one includes a series of component reliability models which include aging, testing and maintenance. The second class includes a series of system reliability models which focus on the secondary side of exiting U.S. reactors. These models are based on fault tree logic structures such that they can be used by existing plant PRA software. We also started to focus on the management of health data and we tackled this issue in two directions. The first one focuses on the integration of monitor data with simulation models to assess component health. This approach is moving from a classical data based to a model+data based approach with the goal of improving higher component health information. The second direction focuses on linking equipment reliability data (e.g., maintenance/failure reports, component monitoring data) directly to system reliability models using a safety margin based language rather than a probability based language. The main advantage of a safety margin based language is that it can provide to system engineers more tangible information on system/component health and how it propagates to the system level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Using Planning, Scheduling and Execution for Autonomous Mars Rover Operations

With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase raises not only the opportunities for science data collection, but also amplifies the amount of environment and rover state uncertainty that must be handled in rover operations. This paper describes how planning, scheduling and execution techniques can be used onboard a rover to autonomously generate and execute rover activities and in particular to handle new science opportunities that have been identified dynamically. We also discuss some of the particular challenges we face in supporting autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.

automated rover operations↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2022

Large-scale scenarios of electric vehicle charging with a data-driven model of control

Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1989

Real-Time MENTAT programming language and architecture

Real-time MENTAT, a programming environment designed to simplify the task of programming real-time applications in distributed and parallel environments, is described. It is based on the same data-driven computation model and object-oriented programming paradigm as MENTAT. It provides an easy-to-use mechanism to exploit parallelism, language constructs for the expression and enforcement of timing constraints, and run-time support for scheduling and exciting real-time programs. The real-time MENTAT programming language is an extended C++. The extensions are added to facilitate automatic detection of data flow and generation of data flow graphs, to express the timing constraints of individual granules of computation, and to provide scheduling directives for the runtime system. A high-level view of the real-time MENTAT system architecture and programming language constructs is provided.

Grimshaw, Andrew S.↗
NASA NTRS2016

Biomolecule Sequencer: Next-Generation DNA Sequencing Technology for In-Flight Environmental Monitoring, Research, and Beyond

On the International Space Station (ISS), technologies capable of rapid microbial identification and disease diagnostics are not currently available. NASA still relies upon sample return for comprehensive, molecular-based sample characterization. Next-generation DNA sequencing is a powerful approach for identifying microorganisms in air, water, and surfaces onboard spacecraft. The Biomolecule Sequencer payload, manifested to SpaceX-9 and scheduled on the Increment 4748 research plan (June 2016), will assess the functionality of a commercially-available next-generation DNA sequencer in the microgravity environment of ISS. The MinION device from Oxford Nanopore Technologies (Oxford, UK) measures picoamp changes in electrical current dependent on nucleotide sequences of the DNA strand migrating through nanopores in the system. The hardware is exceptionally small (9.5 x 3.2 x 1.6 cm), lightweight (120 grams), and powered only by a USB connection. For the ISS technology demonstration, the Biomolecule Sequencer will be powered by a Microsoft Surface Pro3. Ground-prepared samples containing lambda bacteriophage, Escherichia coli, and mouse genomic DNA, will be launched and stored frozen on the ISS until experiment initiation. Immediately prior to sequencing, a crew member will collect and thaw frozen DNA samples, connect the sequencer to the Surface Pro3, inject thawed samples into a MinION flow cell, and initiate sequencing. At the completion of the sequencing run, data will be downlinked for ground analysis. Identical, synchronous ground controls will be used for data comparisons to determine sequencer functionality, run-time sequence, current dynamics, and overall accuracy. We will present our latest results from the ISS flight experiment the first time DNA has ever been sequenced in space and discuss the many potential applications of the Biomolecule Sequencer for environmental monitoring, medical diagnostics, higher fidelity and more adaptable Space Biology Human Research Program investigations, and even life detection experiments for astrobiology missions.

Sequencer↗
NASA NTRS2002

Automating CPM-GOMS

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

GOMS↗
NASA NTRS1993

The application of multiple instruction retry to VLIW architectures using compiler generated hazard-free code

In this paper, we describe the development of compiler assisted multiple instruction word retry for VLIW architectures. Compiler generated hazard-free code with different degrees of rollback capability is compacted by a trace scheduling algorithm. Performances are compared under three parameters: N, the rollback distance for uni-processors; P, the number of functional units; and Np, the rollback distance for VLIW architectures. In the majority of benchmarks examined, for fixed P and Np, the larger N tends to generate compacted code with better performance.

Chen, Shyh-Kwei↗
DOE OSTI2024

Generator Scorecard

Code built on Archive Walker. The Generator Scorecard analyzes power grid measurements to automate the process of evaluating the performance of generators. The tool includes three aspects: frequency response, voltage response, and voltage schedule tracking.

Follum, Jim↗
DOE OSTI2025

Flexible Resource Scheduler for FAST-DERMS (FRS-FASTDERMS) v0.9

The Flexible Resource Scheduler is a hierarchical controller that manages the distributed energy resources in a distribution substation or distribution feeder to provide a firm commitment of power flow at the substation or feeder head to be scheduled in transmission-level markets as an aggregated demand resource. It is the reference controller for the FAST-DERMS Architecture, developed in tandem with the architecture under the DOE FAST-DERMS project. It is comprised of a day-ahead stochastic optimization, which schedules substation power flow and reserves, a intra-hour MPC, which generates dispatch base points for DER, and a real-time PID controller maintaining that dispatches DER to maintain the substation power around the base points. The repository also includes a representative aggregator controller, and all of the necessary components to run a simulation using PNNL's GridAPPS-D software with the controller.

MacDonald, Jason [Lawrence Berkeley National Labor↗
DOE OSTI2022

Failure Mode and Effects Analysis for a Photovoltaic Inverter

While PV panel reliability continues to increase, PV inverters become the limiting factor for PV system reliability. Consequently, it is critical to have a generic tool from a third party for PV inverter reliability assessment to help 1) utilities/PV farm operators schedule maintenance in advance, and 2) inverter developers improve the next-generation design. However, these two things cannot be accomplished without first understanding the reasons behind inverter failure. Following this idea, as the first step, it is essential to identify and investigate the most failure-prone components within a PV inverter system. After all, any system is only as reliable as the components that are contained within it. This motivates the failure mode and effects analysis (FMEA) work presented for this workshop. The FMEA is conducted as follows: first, the overview of the methodology on the development of the FMEA is presented; then, based on a top-down approach starting from the PV inverter system, critical inverter components with high failure rates are identified and summarized; afterward, a thorough FMEA study at a component-level is performed and its results, including failure modes, failure mechanisms, and critical stressors, are tabulated; finally, according to three rankings (chance of occurrence, severity of occurrence, and ease of detection prior to failure) for each failure mechanism provided by the FMEA, risk priority numbers are calculated and the failure mechanisms along with the critical stressors are ranked in terms of their potentially detrimental effect on the PV inverter.

Brown, Buck↗
NASA NTRS1974

A 2.5 kW advanced technology ion thruster

A program has been conducted in order to improve the performance characteristics of 30 cm thrusters. This program was divided into three distinct, but related tasks: (1) the discharge chamber and component design modifications proposed for inclusion in the engineering model thruster were evaluated and engineering specifications were verified; (2) thrust losses which result from the contributions of double charged ions and nonaxial ion trajectories to the ion beam current were measured and (3) the specification and verification of power processor and control requirements of the engineering model thruster design were demonstrated. Proven design modifications which provide improved efficiencies are incorporated into the engineering model thruster during a structural re-design without introducing additional delay in schedule or new risks. In addition, a considerable amount of data is generated on the relation of double ion production and beam divergence to thruster parameters. Overall thruster efficiency is increased from 68% to 71% at full power, including corrections for double ion and beam divergence thrust losses.

Poeschel, R. L.↗
NASA NTRS1976

Space-based solar power conversion and delivery systems study. Volume 2: Engineering analysis of orbital systems

Program plans, schedules, and costs are determined for a synchronous orbit-based power generation and relay system. Requirements for the satellite solar power station (SSPS) and the power relay satellite (PRS) are explored. Engineering analysis of large solar arrays, flight mechanics and control, transportation, assembly and maintenance, and microwave transmission are included.

Source record↗
NASA NTRS1979

Flight test evaluation of a digital controller used in a VTOL automatic approach and landing system

As part of the NASA Langley Research Center's effort to develop technology for VTOL operation in the air transportation system in the late 1980's and beyond, research has been conducted aimed at developing digital controller design procedures. This paper describes the verification of one design procedure by the flight evaluation of an advanced digital control algorithm. The control algorithm, operating at 10 iterations per second, follows step guidance commands with zero steady state error and thus provides an autotrim capability for the nonlinear vehicle. Changes in vehicle dynamics are accounted for using a gain scheduling technique. This control algorithm is combined with sensor filters, a trajectory generator, and a closed loop guidance algorithm to form a VTOL autoland system. A CH-47 tandem rotor helicopter which contains a set of sensors, onboard digital flight computers and electro-hydraulic actuators is used in the evaluation. All software, except input-output routines, is coded in FORTRAN using floating point arithmetic and executed in the flight computer. This autoland system is exercised by automatically flying straight-in descending decelerating trajectories typical of VFR manual approaches to a predetermined landing pad.

Downing, D. R.↗
NASA NTRS1989

Nonlinear control of a supermaneuverable aircraft

This paper describes a technique which may be used to design the flight control system for a highly maneuverable aircraft. The control system was provided to stabilize the dynamics of the aircraft model and allow it to fly simulated, poststall 'supermaneuvers'. Although the aircraft dynamics are highly nonlinear under these conditions, the gain-scheduled, flight control system was designed using basically linear techniques. A manuever generator was implemented to pilot the mathematical model through prescribed optimal trajectories. The control system design performed well while executing maneuvers involving small angular rates where the governing dynamics could be considered linear. However, the performance deteriorated once the model was subjected to high angular rates at high angle of attack.

Snell, S. Antony↗
NASA NTRS1990

Simulator evaluation of the Final Approach Spacing Tool

The design and simulator evaluation of an automation tool for assisting terminal radar approach controllers in sequencing and spacing traffic onto the final approach course is described. The automation tool, referred to as the Final Approach Spacing Tool (FAST), displays speed and heading advisories for arrivals and sequencing information on the controller's radar display. The main functional elements of FAST are a scheduler that schedules and sequences the traffic, a four-dimensional trajectory synthesizer that generates the advisories, and a graphical interface that displays the information to the controller. FAST has been implemented on a high-performance workstation. It can be operated stand-alone in the terminal radar approach control (TRACON) facility or as an element of a system integrated with automation tools in the Air Route Traffic Control Center). Simulation results show that FAST significantly reduced controller workload and demonstrated a potential for an increase in landing rate.

Davis, Thomas J.↗