Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “controllable loads”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Transient Modelling of HTGR Thermal Load Follow in Modelica

The ability of nuclear power plants to load follow electrical power demand is particularly important as we move to higher variable renewable (VRE) energy production on low carbon energy grids. High temperature gas reactors (HTGRs) are capable of load following by adjusting thermal output, but their high temperatures and thermal inertia significantly impact the power transients they are able to follow. To aid investigation into new integrated energy systems with advanced reactors, it is necessary to have an open-source transient model of a full HTGR primary and secondary. This paper introduces two new open-source models for a HTGR and corresponding balance of plant (BOP) written for the HYBRID library developed at Idaho National Laboratory. A transient thermal load follow test case was taken by the work from Brits et al. to benchmark our open-source model against transient operation results from other proprietary codes that exist. The control scheme for the two new models in combination is discussed and a comparison of the response in transient load follow between the two codes performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2020

Adaptive shape control of wavefront-preserving X-ray mirrors with active cooling and heating

This article describes the development and testing of a novel, water cooled, active optic mirror system (called “REAL: Resistive Element Adjustable Length”) that combines cooling with applied auxiliary heating, tailored to the spatial distribution of the thermal load generated by the incident beam. This technique is theoretically capable of sub-nanometer surface figure error control even at high power density. Tests conducted in an optical metrology laboratory and at synchrotron X-ray beamlines showed the ability to maintain the mirror profile to the level needed for the next generation storage rings and FEL mirrors.

42 ENGINEERING↗
DOE OSTI2021

A two-stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A time‐varying formulation of the curled wake model within the FAST.Farm framework

Abstract In this article, we present a time‐varying formulation of the curled wake model that we implemented in FAST.Farm. The curled wake model, originally developed for steady‐state conditions, is used to produce realistic wake profiles behind a wind turbine in yawed (or skewed) conditions. We begin by introducing the key elements of the FAST.Farm framework. Then, after briefly summarizing the original wake dynamics formulation of FAST.Farm based on a polar wake profile, we present the new time‐varying formulation of the curled wake model, compare the two, and highlight the differences with the original curled wake model. After discussing some implementation details, we present different applications with increasing levels of complexity: single turbine with uniform and turbulent inflow, fixed and transient yaw, and multiple turbines. We verify our results using the original FAST.Farm implementation and large‐eddy simulations. The results with the new curled wake model are improved compared to the original implementation, as they include cross‐flow velocities and wake asymmetry. Yet, large‐eddy simulation results show a more pronounced lateral convection of the wake and a stronger concentration of vorticity at the top vortex. The new curled wake implementation in FAST.Farm should enable the calculation of not only generator power but also wind turbine structural loads for applications involving intentional or unintentional skewed flow and wind‐farm control involving wake steering.

17 WIND ENERGY↗
DOE OSTI2023

Robust multi‐loop control of a floating wind turbine

Abstract A principal challenge facing the control of floating offshore wind turbines (FOWTs) is the problem of instability, or “negative damping,” when using blade pitch feedback to control generator speed. This closed‐loop instability can be attributed to non‐minimum phase zeros in the transfer function from blade pitch to generator speed. Standard approaches to improving stability and performance include robust tuning of control gains and introducing multiple feedback loops to respond to platform motion. Combining these approaches is nontrivial because multiple control loops complicate the impact of coupling in the system dynamics. The single‐loop approach to analyzing stability robustness neglects inter‐loop coupling, while a simplistic multi‐loop approach is highly sensitive to dimensional scaling and overestimates the robustness of the single‐loop controller. This work proposes a sensitivity representation that separates some of the natural FOWT dynamic coupling into a parallel feedback loop in the sensitivity function loop to address both of these concerns. The modified robustness measure is used with a simplified linear FOWT model to optimize scheduled multi‐loop control parameters in an automated tuning procedure. This controller is implemented for the 10‐MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) and compared against conventional single‐ and multi‐loop controllers tuned using frequency‐domain analysis and high‐fidelity OpenFAST simulations. The multi‐loop robust controller shows the highest overall performance in generator speed regulation and tower load reduction, though consideration of power quality, actuator usage, and other structural loading leads to additional trade‐offs.

Stockhouse, David↗
DOE OSTI2023

Densification and microstructure features of lithium hydride fabrication

The manufacturing of lithium hydride (LiH) utilizing uniaxial pressing, which offers fabrication with tailorable properties via microstructure control, can lead to the expansion in application of LiH while bypassing the challenges presented by historical casting manufacturing techniques. Through control of consolidation conditions such as pressure, temperature, dwell time and powder load, the presented work highlights the densification of LiH, with an emphasis on quantifying oxygen content, for applications requiring a specific density range necessary for optimized material performance. Karl Fischer Titration and X-ray Diffraction proved useful in determining oxygen and phase content while Computed Tomography and Scanning Electron Microscopy provided structural analysis. The temperature dependent densification of LiH fit with an Arrhenius term resulted in an activation energy of 21.2 kJ/mol. Images of fractured surfaces of LiH pressed at 500 °C revealed drastic grain coarsening, aided by the presence of oxygen impurities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Scaling up carboxylic acid production from cheese whey and brewery wastewater via methane-arrested anaerobic digestion

In a circular economy, organic waste streams are valuable resources for sustainably producing chemicals and fuels. This study investigates a new methane-arrested anaerobic digestion (MAAD) process that converts high-strength cheese whey and brewery wastewater into carboxylic acids. The process was developed and optimized under various bench-scale semi-continuous (fed-batch) operating conditions (e.g., retention time, organic loading rate, pH, and feed/harvest frequency). The MAAD responses to various control-failure scenarios were also systematically investigated. The highest total acid productivity was 26g/(L liq ·d) with a substrate conversion of 0.79g COD digested /g COD fed at a hydraulic retention time (HRT) of approximately 2 d in a 14-L digester. The most stable conditions for digester operation (HRT 3 d at pH 6.0 and 40°C) were selected for process scale-up to 100gal (~380 L). Semi-continuous, pilot-scale MAAD successfully produced a total acid concentration of 40.6+/-1.1 g/L with 8.1% acetic acid, 45.1% butyric acid, and 44.5% lactic acid. The links between wastewater characteristics, operation mode, digester scale, and microbial community structure were statistically analyzed. The results show genera Sporolactobacillus and Clostridium positively correlate with butyric acid production (Pearson correlation coefficient>0.5). Moreover, four kinetic models were developed and fit to batch MAAD experimental datasets (R 2 >95%) and were successfully applied to predict the total acid production in both bench-and pilot-scale semi-continuous MAADs. In conclusion, this study shows MAAD has the potential for industrial-scale applications and is a robust platform to valorize low- or negative-value waste streams into high-value bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2023

Deciphering the multiple deformation mechanisms responsible for sustained work hardening in a FeCrCoNi medium entropy alloy

Two important and desirable properties of materials for most structural applications are high tensile strength and ductility, which typically require high work hardening to delay necking. Here, in this work, we designed and tensile tested a face-centered cubic (fcc) Fe-Cr-Co-Ni medium-entropy alloy in which multiple deformation mechanisms are triggered during tensile loading at different temperatures to induce sustained work hardening. Our strategy involved control of the relative stabilities of the fcc, hcp (hexagonal close-packed), and bcc (body-centered cubic) phases in this quaternary system via high-throughput thermodynamic calculations. This alloy not only exhibits extensive deformation-induced nanotwinning at room temperature, but also displays a two-step sequential phase transformation [γ (fcc) → ε (hcp) martensite → α’ (bcc) martensite] at 77 K, which contrasts with the single-step phase transformation [γ → ε martensite] observed in many other fcc high/medium entropy alloys with a low stacking fault energy. The sequence of phase transformation at 77K was supported by first-principles density functional theory calculations. This work provides new templates for the design of alloys capable of multiple deformation mechanisms for sustained work hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A conceptual model for how to design for building envelope characteristics. Impact of thermal comfort intervals and thermal mass on commercial buildings in U.S. climates

The paper presents a simplified conceptual model for energy demand calculations based on building envelope characteristics, thermal mass and local climate. It is based on a network model and lumped analysis of the dynamic process. Characteristic parameters for the buildings are suggested; Driving temperature (DT), Driving temperature difference, (DTD), External Load Temperature (ELT), and Thermal Load Resistance (TLR). The Building Envelope Performance ( BEP 0 ), based on a controlled constant indoor temperature is introduced. Solution techniques using stable explicit forward differences based on analytical solutions are derived. The conceptual model has been used for mapping the Driving temperature difference and introduced two performance factors and . The first factor represents the effect of thermal comfort interval and thermal mass on the energy demand. The latter represents the ratio between cooling and heating energy demand. These three parameters and factors have been visualized on U.S. maps and enable a possibility to communicate the demand of energy, and cooling and the coupling to building characteristics, in a concise way.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Carboxylic acid ligand substituent impacts hydrosilylation activity of platinum single atom catalysts on ceria

Current industrially employed hydrosilylation catalysts rely on homogeneous platinum catalysts which are not recovered after the reaction. To eliminate this issue, our group has been working to optimize recyclability of heterogeneous platinum single atom catalysts (SACs) on ceria via 1,10-phenanthroline-5,6-dione ligands (PDO), which incorporates mono (PDO-C) and dicarboxylic acid (PDO-C2) groups in the 2- and 9-position of PDO ligand to increase metal–surface interaction. DRIFTS results confirm carboxylic acid coordination to the terminal hydroxy groups of the ceria surface. New catalyst synthesis conditions wherein PDO was combined with the metal prior to exposure to the surface allow control of Pt oxidation state on the surface. Here, the highest metal loading was observed for PDO and PDO-C, correlating with improved catalytic recyclability compared to the PDO-C2 ligand. It is proposed that the location of the carboxylic acid groups and the steric effects can explain the lower activity and metal loading for PDO-C2 ligands. Post-reaction XPS and DRIFTS spectra show the appearance of new Si and O species on the catalyst during the hydrosilylation reaction, indicating the silane reagent is depositing on the surface. The silane coverage and leaching of catalyst from the surface is the cause for the reduced catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Statistical Analysis of Inter-Area Oscillations in the U.S. Eastern Interconnection: A 2017-2023 Perspective

Recent advancements and the accumulation of high-resolution, long-term phasor measurement unit (PMU) data have provided detailed insights into inter-area oscillations in power grids. This study conducts a comprehensive statistical analysis of inter-area oscillations within the United States Eastern Interconnection from 2017 to 2023. Utilizing data captured by the advanced wide-area Frequency Monitoring Network (FNET/GridEye), this investigation examines the occurrence patterns, dominant frequencies, damping ratios, and excitation mechanisms of these oscillations. Our analysis sheds light on the evolving statistical behaviors of inter-area oscillations, offering updated and critical information for grid operators and planners. The insights gained from this study can be instrumental in enhancing the operational resilience of the power network and guiding strategic developments in grid infrastructure to accommodate future challenges. Additionally, the study discusses emerging challenges associated with the modernization of the power grid, including increased renewable penetration, dynamic load variability, and cyber-physical vulnerabilities that complicate oscillation monitoring and control.

Inter-area oscillations↗
DOE OSTI2022

Network Reconfiguration for Enhanced Operational Resilience Using Reinforcement Learning

This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.

actor critic↗
DOE OSTI2020

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

electricity distribution market↗
DOE OSTI2025

Electric Grid Simulator For Human Factor Research

The developed code simulates real-time monitor and control for west area of IEEE 118-bus system. The 24-hour load profile for each bus is derived by scaling the system’s rated load in the PSSE sav file according to the California Independent System Operator’s Day-ahead load forecast for May 1, 2024. This simulator performs several critical functions: (1) Calculating time-series power flow every 4 seconds; (2) Updating and dispatching AGC signals every minute; (3) Conducting N-1 contingency analysis every 5 minutes. Additionally, the simulator can trip lines and subsequently update and dispatch AGC signals, running power flow analysis after each tripping event.

Huang, Jianqiao [Idaho National Laboratory (INL), ↗