Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Adaptive Control”

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 289 records · Page 16

Using Demanded Power and RDE Aggressiveness Metrics to Analyze the Impact of CACC Aggressiveness on Heavy Duty Platooning Power Consumption

Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases. It was hypothesized that the CACC may have amplified transient platooning events relative to the individual truck baseline results, hampering the potential energy savings. Therefore, the impact of the controller on the observed driving style was analyzed in detail. In order to quantify the transient operational characteristics of the experimental trials, metrics from the European Real Driving Emissions (RDE) legislation were modified to serve as metrics of aggressiveness during platooning. The metrics (v · apos)95 and Relative Positive Acceleration (RPA) were calculated for platooning and non-platooning runs. These results indicate that the CACC induces small acceleration events during platooning to retain the commanded longitudinal separation between vehicles. These small acceleration events increase following vehicle aggressiveness during platooning and prevent the following vehicles from obtaining maximum energy savings. Moreover, a correlation between the RDE metric (v · apos)95 and energy savings is developed. Hence, this work establishes the ability of RDE metrics to assess CACC impacts on platoon energy savings.

European Real Driving Emissions, RDE, greenhouse g↗

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗

Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices

The Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices (Project) investigated, analyzed and modeled advanced turbine control schemes with the objective of increasing the energy harvested by hydrokinetic turbines in turbulent flow. Ocean Renewable Power Company (ORPC) implemented and validated a feedforward controller to increase power capture; and applied and tested the controls on ORPC’s RivGen® Power Systems in Igiugig, Alaska. Assessments of performance improvements were made for the RivGen® in the Igiugig environment and for ORPC’s TidGen® Power System in a reference tidal environment. Annualized Energy Production (AEP) and Levelized Cost of Energy (LCOE) improvements associated with implementation of the recommended control methodology were made for the TidGen® Power System in the DOE reference tidal environment. System Performance Advancement (SPA) goals were selected for the project. SPA targets were to improve Power to Weight Ratio (PWR) and system Availability, with the intention of reducing Levelized Cost of Electricity (LCOE). This project focused primarily reducing in PWR. Reductions in PWR of 25.5% were achieved. Reductions of 20.3% in LCOE were achieved. This project evaluated four types of controllers which were tested in simulation, emulation, a laboratory flume, and the field. The adaptive Kω2 controller performs similarly to the non-adaptive version of the same controller and may be useful in tidal channels where the mean velocity is continually evolving. Trends in simulation were largely verified through experiments, which also provided the opportunity to test assumptions about turbine responsiveness and control resilience to varying scales of turbulence. Laboratory experiments provided an essential stepping stone between simulation and implementation on a field-scale turbine. Experiments also demonstrated that using “energy loss” as a metric to differentiate between well-designed controllers operating at an optimal tip-speed ratio set-point is difficult, which anticipated the outcome from field experiments. The clear message is that the feedforward Kω2 controller out-performs the feedback controllers in almost all aspects and modes of evaluation. The controllers proved a substantial improvement over the baseline performance of the TidGen® turbine, in terms of energy capture. The effects of noise-contaminated angular velocity signals were investigated and validated by simulation as an explanation for the performance limitations observed for TidGen® turbine operations in Eastport, Maine. Measurements of loads performed as part of the laboratory testing indicate that there are limited differences in average axial thrust force between control architectures. This suggests that none of the control strategies are likely to substantially affect loads on the turbine support structure. Velocity measurements during the ORPC RivGen® turbine deployment at Igiugig, Alaska, in 2014 were used to assess the variability of the river flow. Results suggest that the river flow is approximately steady, in the mean sense, at any particular location in the river, with random turbulent fluctuations that are around 10% of the mean flow. The mean flow in the center channel of the river is 2.5 m/s, with reductions near the riverbanks and in the shallows. Spectral analysis and lagged correlation results indicate that temporal fluctuations at a given point are dominated by large scale fluctuations, such that measurements at the turbine location are just as useful for inflow control implementation as upstream measurements. At this site, and likely at many other river sites, flow is generally steady at a given location, but flow varies dramatically between locations, particularly laterally across the river. The primary result is that a lateral change in position of a few meters results in changes to flow speed that far exceed the turbulence fluctuations at any given location. The turbulence is dominated by long time scales. Following final system tests, the RivGen® device was submerged and each evaluated controller was tested across a range of gain/set point values and filter configurations for a minimum of 5 minutes, with longer runs attempted for well-performing cases. In addition to testing controllers during the 2015 deployment season, LGL Alaska Research Associates Inc. (LGL) performed a fish monitoring study in compliance with Alaska Department of Fish and Game fisheries habitat permit for the project. During the season, LGL reviewed 10% of 179 one-hour blocks of footage (6 minutes on the hour) and documented a total of over 1200 fish in the vicinity of the RivGen® device, including over 800 salmon smolt and over 350 adult salmon. No evidence of adverse effects including passage delay by upstream migrating salmon was noted. This is an important result for future deployments and has a direct impact on commercial system designs.

16 TIDAL AND WAVE POWER↗

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. Here, in this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A new adaptive modeling strategy based on optimal control for atomic-to-continuum coupling simulations

A new adaptive method based on an optimal control approach is proposed for adaptive modeling of an atomic-to-continuum coupling method constructed from the Arlequin framework. The coupling method provides for an approximation of the solution to a fully atomic model in which errors may arise due to the misplacement of the overlap region. The objective is thus to determine, a posteriori, the best position of this overlap region. More precisely, the problem is to find the optimal size of the atomic region that one needs to consider in the coupled formulation in order to accurately estimate predefined quantities of interest. In this new adaptive process, the position of the overlapping domain between the two models is conveniently parameterized and iteratively determined by searching for the optimal parameters. The performance of the method is demonstrated on one-dimensional and two-dimensional test problems. In particular, it is observed that the approach yields better convergence with respect to quantities of interest when compared to a classical adaptive approach based on a posteriori estimates of the modeling error.

97 MATHEMATICS AND COMPUTING↗

Nonlinear model predictive control for chemical looping process

A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.

97 MATHEMATICS AND COMPUTING↗

Adaptive CT scanning system

Apparatus, systems, and methods that provide an X-ray interrogation system having a plurality of stationary X-ray point sources arranged to substantially encircle an area or space to be interrogated. A plurality of stationary detectors are arranged to substantially encircle the area or space to be interrogated, A controller is adapted to control the stationary X-ray point sources to emit X-rays one at a time, and to control the stationary detectors to detect the X-rays emitted by the stationary X-ray point sources.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

A Novel Design for Switchable Grid-Following and Grid-Forming Control

This paper presents the design of a novel grid- forming (GFM) control structure adapted from a typical grid- following (GFL) control structure with minimal edits, thereby enabling a switchable control structure for voltage sourced converters (VSCs) to operate in either GFL or GFM mode by simply switching a flag manually. The VSC is shown to be able to operate in the GFL control mode synchronizing to the main grid through a phase-locked-loop (PLL) and operate as a GFM controller with power-based synchronization for both grid-connected and islanded conditions. To guarantee smooth operation, the control schemes and the mode switching logic have been carefully designed and examined via a series of experiments. Here, the experiment results show that the switchable control structure can fulfill the desired control and operation functions and enable smooth transition between control modes.

14 SOLAR ENERGY↗

Advancing Platooning with ADAS Control Integration and Assessment Test Results

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.

ADAS↗

Deployment of Dynamic Neural Network Optimization to Minimize Heat Rate During Ramping for Coal Power Plants (Final Technical Report)

Much success was achieved throughout the course of this project. A successful implementation of Dynamic Neural Network Optimization (D-NNO) was coupled with Adaptive Predictive Controls (APC) and a novel hardware installation comprised of an advanced sensor network (ASN) measuring mass-weighted averages of flue gas constituents above the horizontal superheater of a coal-fired utility boiler. From 2019 through 2023 (including an extension due to COVID delays), the team was able to prototype, evaluate, deploy, iterate, and ultimately finalize an advanced closed-loop control D-NNO system which demonstrated the ability to: •improve unit efficiency ~2.0% relative to unoptimized operation (represented as total fuel fired per MWh generated) •improve unit NOx emission rates 10%+ beyond static optimization baselines •improve unit temperature stability as much as 58% and on average 12% •improve operating load stability as much as 35% The culmination of this project has generated an advanced methodology of deploying specially designed recurrent neural networks (long short-term memory, gated recurrent unit, encoder-decoder networks, transformers, etc.), customized trajectory planning and closed-loop optimization modules capable of adapting to live electric grid responses and demands, self-tuning and adaptive expert controls constantly adjusting prediction parameters to real-time unit behavior, and a hardware/software package able to reliably calculate net unit heat rate (NUHR) in real-time using flue gas constituents, machine learning, and known combustion relationships. Through this real-time NUHR value, immediate feedback on system adjustments relative to operating efficiency was available, allowing for rapid improvements to system performance. In addition to development and deployment of the advanced D-NNO system, the approach methodology has been readily commercialized through the project platform Griffin Open Systems, LLC, the D-NNO software platform host. Similar methodologies to those developed by this project have already been deployed at 5 other units across the United States, with another 6 implementations scheduled, and more expected. Over the course of the project, multiple academic papers were submitted and accepted for publication within esteemed academic journals, and PhD students were trained and graduated, as well as undergraduate students becoming involved and participating to project objectives.

01 COAL, LIGNITE, AND PEAT↗

Online multi-objective particle accelerator optimization of the AWAKE electron beam line for simultaneous emittance and orbit control

Multi-objective optimization is important for particle accelerators where various competing objectives must be satisfied routinely such as, for example, transverse emittance vs bunch length. We develop and demonstrate an online multi-time scale multi-objective optimization algorithm that performs real time feedback on particle accelerators. We demonstrate the ability to simultaneously minimize the emittance and maintain a reference trajectory of a beam in the electron beamline in CERN’s Advanced Proton Driven Plasma Wakefield Acceleration Experiment.

43 PARTICLE ACCELERATORS↗

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗

Next generation sequencing (NGS) technologies and applications [Slides]

NGS technology overview: (1) NGS library preparation - Nucleic acids extraction, Sample quality control, RNA conversion to cDNA, Addition of sequencing adapters, Quality control of library; (2) Sequencing - Clonal amplification of library fragments, (except PacBio), Sequencing by synthesis, Data output (reads and quality); and (3) Data analysis - Read mapping, Genome assembly, Gene expression, Operon structure, sRNA discovery, and Epigenetic analyses.

59 BASIC BIOLOGICAL SCIENCES↗

Supervisory Algorithm for Autonomous Hemodynamic Management Systems

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.

42 ENGINEERING↗

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗