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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Investigations of the Impact of Biodiesel Metal Contaminants on Emissions Control Devices

Biodiesel is a renewable fuel with the potential to displace a portion of petroleum use. However, as with any alternative fuel, in order to be a viable choice it must be compatible with the emissions control devices. The finished biodiesel product can contain up to 5 ppm Na+K and 5 ppm Ca+Mg, and these metal impurities can lead to durability issues with the devices used to control emissions in diesel vehicles. Significant work has been performed to understand how the presence of these metals impacts each individual component of diesel emissions control systems, and this chapter summarizes the findings of these research efforts.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

Trajectory controllability of semilinear systems with multiple variable delays in control

In this paper, finite-dimensional dynamical control system described by semilinear differential state equation with multiple variable delays in control are considered. The concept of controllability we extend on trajectory controllability for systems with multiple point delays in control. Moreover, remarks and comments on the relationships between different concepts of controllability are presented. Finally, simple numerical example, which illustrates theoretical considerations is also given. The possible extensions are also proposed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Fractional order PID controller for improvement of PMSM speed control in aerospace applications

Because of the benefits reduced size, cost and maintenance, noise, CO2 emissions and increased control flexibility and precision, to meet these expectations, electrical equipment increasingly utilize in modern aircraft systems and aerospace industry rather than conventional mechanic, hydraulic, and pneumatic power systems. Electric motor drives are capable of converting electrical power to drive actuators, pumps, compressors, and other subsystems at variable speeds. In the past decades, permanent magnet synchronous motor (PMSM) and brushless dc (BLDC) motor were investigated for aerospace applications such as aircraft actuators. In this paper, the fractional-order PID controller is used in the design of speed loop of PMSM speed control system. Having more parameters for tuning fractional order PID controller lead to good performance ratio to integer order. This good performance is shown by comparison fractional order PID controller with the conventional PI and tuned PID controller by Genetic algorithm in MATLAB soft wear.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Stoichiometry control of complex oxides by sequential pulsed-laser deposition from binary-oxide targets

To have precise atomic layer control over interfaces, we examine the growth of complex oxides through the sequential deposition from binary targets by pulsed laser deposition. In situ reflection high-energy electron diffraction (RHEED) is used to control the growth and achieve films with excellent structural quality. The growth from binary oxide targets is fundamentally different from single target growth modes and shows more similarities to shuttered growth by molecular beam epitaxy. The RHEED intensity oscillations of non-stoichiometric growth are consistent with a model of island growth and accumulation of excess material on the surface that can be utilized to determine the correct stoichiometry for growth. Correct monolayer doses can be determined through an envelope frequency in the RHEED intensity oscillations. In order to demonstrate the ability of this growth technique to create complex heterostructures, the artificial n = 2 and 3 Sr n+1 Ti n O 3 n+1 Ruddlesden-Popper phases are grown with good long-range order. Furthermore, this method enables the precise unit-cell level control over the structure of perovskite-type oxides, and thus the growth of complex materials with improved structural quality and electronic functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V{sub max} amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V{sub max} of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k{sub //} computed by means of the coupling code ANTITER II remains small for the considered cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System

Frequency control from photovoltaic (PV) power plants has great potential to address the frequency response challenge of the power system with high penetrations of renewable generation. Using model-based approaches to determine the optimal PV headroom reserve, however, requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic 1-day operation profile of the WECC system and demonstrates a savings of more than 40% PV headroom compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

frequency control↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

deployment↗
DOE OSTI2018

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR) for learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS- SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can be properly distinguished, thus a robust modeling result can be achieved. Finally, a novel multi- objective evaluation index on modeling performance is developed by comprehensively considering the root mean square error of modeling and the correlation coefficient on trend fitting, based on which the NSGA-II algorithm is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently, which indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

blast cavities, Control↗
DOE OSTI2020

Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control

We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.

Chen, B↗
DOE OSTI2022

Potential Heating Energy and Cost Savings of Dual Fuel Heat Pump Controls as a Residential Building Equipment Retrofit in the U.S.

This study investigated the energy and cost savings potential by modeling a dual fuel heat pump system with its control for a residential building in (5A) cold climate in U.S. The simulation analysis showed that the application of the DFHP system and its control to the target building can save the heating energy by 42% over a gas furnace system (baseline system). Although the HP-only system can save more heating energy (i.e., 53%) over the baseline system than DFHP system does, the DFHP with its control demonstrated the higher cost savings when a time of use electricity rate was considered.

Yoon, Yeobeom↗
DOE OSTI2023

Fault isolation and fault-tolerant control for nonlinear stochastic distribution control systems with multiplicative faults

Here, in this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.

42 ENGINEERING↗
DOE OSTI2020

Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Distributed Control System Prototyping Environment to Support Control Room Modernization

Operators of critical processes, such as nuclear power production, must contend with highly complex systems, procedures, and regulations. Developing human-machine interfaces (HMIs) that better support operators is a high priority for ensuring the safe and reliable operation of critical processes. Human factors engineering (HFE) provides a rich and mature set of tools for evaluating the performance of HMIs, however the set of tools for developing and designing HMIs is still in its infancy. Here we propose a rapid prototyping approach for integrating proposed HMIs into their native environments before a design is finalized. This approach allows researchers and developers to test design ideas and eliminate design flaws prior to fully developing the new system. We illustrate this approach with four prototype designs developed using Microsoft’s Windows Presentation Foundation (WPF). One example is integrated into a microworld environment to test the functionality of the design and identify the optimal level of automation for a new system in a nuclear power plant. The other three examples are integrated into a full-scale, glasstop digital simulator of a nuclear power plant. One example demonstrates the capabilities of next generation control concepts; another aims to expand the current state of the art; lastly, an HMI prototype was developed as a test platform for a new control system currently in development at U.S. nuclear power plants. WPF possesses several characteristics that make it well suited to HMI design. It provides a tremendous amount of flexibility, agility, robustness, and extensibility. Distributed control system (DCS) specific environments tend to focus on the safety and reliability requirements for real-world interfaces and consequently have less emphasis on providing functionality to support novel interaction paradigms. Because of WPF’s large user-base, Microsoft can provide an extremely mature tool. Within process control applications,WPF is platform independent and can communicate with popular full-scope process control simulator vendor plant models and DCS platforms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Micronutrient metal speciation is controlled by competitive organic chelation in grassland soils

Many elements are scarcely soluble in aqueous conditions found in high pH environments, such as calcareous grassland soils, unless complexed to strong binding organic ligands. To overcome this limitation, some plants and microbes produce chelators that solubilize micronutrient metals such as Fe, Ni, Cu, and Zn from mineral phases. These complexes are taken up by organisms via specific membrane receptors, thereby differentially impacting the bioavailability of these metals to the plant and microbial community. Although the importance of these chelation strategies for individual organisms has been well established, little is known about which pathways coexist within rhizosphere microbiomes or how they interact and compete for metal binding. Identifying these metallo-organic species within natural ecosystems has remained a formidable analytical challenge due to the vast diversity of compounds and poorly defined metabolic processes in complex soil matrix. Herein, we employed recently developed liquid chromatography (LC) mass spectrometry (MS) methods to characterize the speciation of water-soluble dissolved trace elements (Fe, Ni, Cu, and Zn) from Kansas Prairie soil. Both plant and fungal chelators were identified, revealing compound-specific patterns of chelation to biologically essential metals. Numerous metabolites typically implicated in plant iron acquisition and homeostasis, including mugineic acids, deoxymugineic acid, nicotianamine, and hydroxynicotianamine, dominated the speciation of divalent metals such as Ni, Cu, and Zn (2-57 pmol / g soil). In contrast, the fungal siderophore ferricrocine bound comparatively more trivalent Fe (9pmol / g soil). These results define biochemical pathways that underpin the regulation of metals in the grassland rhizosphere. They also raise new questions about the competition of these compounds for metal binding and their bioavailability to different members of the rhizosphere population. Even small structural differences result in significant differences in their environmental metal speciation, and likely impact metal uptake within the rhizosphere of calcareous soils.

Boiteau, Rene M.↗