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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

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗
DOE OSTI2025

A Cost-Effective Wave Energy Harvesting System with Maximum Power Point Tracking Capability

In this paper, we propose a low-cost bidirectional four-switch inverter that transfers three-phase energy between a permanent magnet synchronous generator (PMSG) driven by a wave energy collector and the power grid or a local ac load. To achieve high control performance, the machine-side inverter uses a high-gain observer for rotor position estimation and a Lyapunov-based approach for torque control. For the gridside inverter control, we estimate the filter capacitor voltage with a model reference adaptive system and control the dc-link voltage with a Lyapunov-based energy function. We also track the maximum power point from the sea waves with an integral control law that actively matches the generator impedance with that of the wave collection device. The proposed system supports both grid-following/forming modes. We validate our control design with simulation results on a 3-kW hardware setup.

wave energy, power systems integration, control de↗
DOE OSTI2015

Reliability of unstable periodic orbit based control strategies in biological systems

Presence of recurrent and statistically significant unstable periodic orbits (UPOs) in time series obtained from biological systems is now routinely used as evidence for low dimensional chaos. Extracting accurate dynamical information from the detected UPO trajectories is vital for successful control strategies that either aim to stabilize the system near the fixed point or steer the system away from the periodic orbits. A hybrid UPO detection method from return maps that combines topological recurrence criterion, matrix fit algorithm, and stringent criterion for fixed point location gives accurate and statistically significant UPOs even in the presence of significant noise. Geometry of the return map, frequency of UPOs visiting the same trajectory, length of the data set, strength of the noise, and degree of nonstationarity affect the efficacy of the proposed method. Results suggest that establishing determinism from unambiguous UPO detection is often possible in short data sets with significant noise, but derived dynamical properties are rarely accurate and adequate for controlling the dynamics around these UPOs. A repeat chaos control experiment on epileptic hippocampal slices through more stringent control strategy and adaptive UPO tracking is reinterpreted in this context through simulation of similar control experiments on an analogous but stochastic computer model of epileptic brain slices. Reproduction of equivalent results suggests that far more stringent criteria are needed for linking apparent success of control in such experiments with possible determinism in the underlying dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

FastProNGS: fast preprocessing of next-generation sequencing reads

Background Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. Yet, until now, software that can efficiently make all the quality assessments and filtration of raw data is still lacking. Results We developed FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest. Read information before and after filtration can be output in plain-text, JSON, or HTML formats with user-friendly visualization. Conclusions FastProNGS is a rapid, standardized, and user-friendly tool for preprocessing next-generation sequencing data within minutes. It is an all-in-one software that is convenient for bulk data analysis. It is additionally very flexible and can implement different functions using different user-set parameter combinations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Adaptive individual-cylinder thermal state control using piston cooling for a GDCI engine

A system for a multi-cylinder compression ignition engine includes a plurality of nozzles, at least one nozzle per cylinder, with each nozzle configured to spray oil onto the bottom side of a piston of the engine to cool that piston. Independent control of the oil spray from the nozzles is provided on a cylinder-by-cylinder basis. A combustion parameter is determined for combustion in each cylinder of the engine, and control of the oil spray onto the piston in that cylinder is based on the value of the combustion parameter for combustion in that cylinder. A method for influencing combustion in a multi-cylinder engine, including determining a combustion parameter for combustion taking place in in a cylinder of the engine and controlling an oil spray targeted onto the bottom of a piston disposed in that cylinder is also presented.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Adaptive individual-cylinder thermal state control using intake air heating for a GDCI engine

A system for a multi-cylinder compression ignition engine includes a plurality of heaters, at least one heater per cylinder, with each heater configured to heat air introduced into a cylinder. Independent control of the heaters is provided on a cylinder-by-cylinder basis. A combustion parameter is determined for combustion in each cylinder of the engine, and control of the heater for that cylinder is based on the value of the combustion parameter for combustion in that cylinder. A method for influencing combustion in a multi-cylinder compression ignition engine, including determining a combustion parameter for combustion taking place in a cylinder of the engine and controlling a heater configured to heat air introduced into that cylinder, is also provided.

42 ENGINEERING↗
DOE OSTI2019

Heuristic Dynamic Programming for Adaptive Virtual Synchronous Generators

In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines: Preprint

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.

17 WIND ENERGY↗
DOE OSTI2024

Securing the Clean Energy Transition with Cyber-Informed Engineering

The Bipartisan Infrastructure Law kick-started a surge in clean energy investments in the United States. Securing these systems and their supporting infrastructure from the outset is imperative, starting from the conceptual design and continuing through the entire engineering lifecycle. Cyber-Informed Engineering is an ideal approach to seamlessly integrate the necessary advanced control capabilities and adaptive protection systems, especially considering the trend towards cloud-based infrastructure and process controls. Learn more about CIE at https://www.energy.gov/ceser/articles/cyber-informed-engineering-bridge-between-cyber-and-critical-infrastructure-securing.

14 SOLAR ENERGY↗
DOE OSTI2024

Adaptive Computing and Multi-Fidelity Strategies for Control, Design and Scale-Up of Renewable Energy Applications

We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

adaptive computing↗
DOE OSTI2015

Chaos control for the output-constrained system by using adaptive dynamic surface technology and application to the brushless DC motor

In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposed approach is demonstrated on the brushless DC motor example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Evaluation of Distributed Power Apportioning with Net Load Management Engine in Microgrids Using Power Hardware-in-the-Loop Simulation

This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.

hardware-in-the-loop simulation↗
DOE OSTI2022

Adaptive Neurocontrol for Grid-Following Inverters

The new generation of power systems involve a large number of autonomous operating units with the flow of data being restricted by privacy concerns or infrastructure hurdles. The systems are also time-varying so the control should adjust in a timely manner to avoid failures that usually have very negative economical implications. Adaptive neurocontrol which takes elements from adaptive control (great for time-varying problems) and model identification (could be in the form of neural network) by using local available data is a compelling tool. In this work, we summarize analytical results for adaptive neurocontrol, followed with numerical verification of how the method could work for future grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

System and methods for deploying payloads

Embodiments of the present invention include systems for launching primary or secondary payloads or actuating other launch vehicle or payload or instrumentation devices. The system includes an adapter assembly and at least one sequencer mounted to the adapter assembly. The sequencer includes: controller boards, each of the controller boards having a controller for controlling deployment of the payloads and data files; output ports coupled to the controller boards and configured to transmit signals from the controller boards to dispensers therethrough, deployment mechanisms containing the payloads, the adapter assembly having channels for accommodating the dispensers; and a detector coupled to the controller boards and adapted to detect an external signal and, in response to the external signal, to send an initiation signal to the controller boards. The system also includes at least one power supply coupled to the sequencer and adapted to provide an electrical power to the sequencer.

42 ENGINEERING↗
DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2019

Neural Network Predictive Controller for Grid-Connected Virtual Synchronous Generator

In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual synchronous generator (VSG) is discussed, and it is shown that when the inverter is connected to non-inductive grids, the conventional PI-based VSGs are unable to perform acceptable tracking. The concept of the neural network predictive controller is also discussed to replace the traditional VSGs. This replacement enables inverters to perform in both inductive and non-inductive grids. The simulation results confirm that a well-trained neural network predictive controller illustrates can adapt to any grid impedance angle, compared to the traditional PI-based virtual inertia controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗