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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 523 records · Page 29

Jet Engine Noise Generation, Prediction and Control

Aircraft noise has been a problem near airports for many years. It is a quality of life issue that impacts millions of people around the world. Solving this problem has been the principal goal of noise reduction research that began when commercial jet travel became a reality. While progress has been made in reducing both airframe and engine noise, historically, most of the aircraft noise reduction efforts have concentrated on the engines. This was most evident during the 1950 s and 1960 s when turbojet engines were in wide use. This type of engine produces high velocity hot exhaust jets during takeoff generating a great deal of noise. While there are fewer commercial aircraft flying today with turbojet engines, supersonic aircraft including high performance military aircraft use engines with similar exhaust flow characteristics. The Pratt & Whitney F100-PW-229, pictured in Figure la, is an example of an engine that powers the F-15 and F-16 fighter jets. The turbofan engine was developed for subsonic transports, which in addition to better fuel efficiency also helped mitigate engine noise by reducing the jet exhaust velocity. These engines were introduced in the late 1960 s and power most of the commercial fleet today. Over the years, the bypass ratio (that is the ratio of the mass flow through the fan bypass duct to the mass flow through the engine core) has increased to values approaching 9 for modern turbofans such as the General Electric s GE-90 engine (Figure lb). The benefits to noise reduction for high bypass ratio (HPBR) engines are derived from lowering the core jet velocity and temperature, and lowering the tip speed and pressure ratio of the fan, both of which are the consequences of the increase in bypass ratio. The HBPR engines are typically very large in diameter and can produce over 100,000 pounds of thrust for the largest engines. A third type of engine flying today is the turbo-shaft which is mainly used to power turboprop aircraft and helicopters. An example of this type of engine is shown in Figure IC, which is a schematic of the Honeywell T55 engine that powers the CH-47 Chinook helicopter. Since the noise from the propellers or helicopter rotors is usually dominant for turbo-shaft engines, less attention has been paid to these engines in so far as community noise considerations are concerned. This chapter will concentrate mostly on turbofan engine noise and will highlight common methods for their noise prediction and reduction.

Huff, Dennis L.↗

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↗

Frequency Recovery in Power Grids Using High-Performance Computing

Maintaining electric power system stability is paramount, especially in extreme contingencies involving unexpected outages of multiple generators or transmission lines that are typical during severe weather events. Such outages often lead to large supply-demand mismatches followed by subsequent system frequency deviations from their nominal value. The extent of frequency deviations is an important metric of system resilience, and its timely mitigation is a central goal of power system operation and control. This paper develops a novel nonlinear model predictive control (NMPC) method to minimize frequency deviations when the grid is affected by an unforeseen loss of multiple components. Our method is based on a novel multi-period alternating current optimal power flow (ACOPF) formulation that accurately models both nonlinear electric power flow physics and the primary and secondary frequency response of generator control mechanisms. We develop a distributed parallel Julia package for solving the large-scale nonlinear optimization problems that result from our NMPC method and thereby address realistic test instances on existing high-performance computing architectures. Our method demonstrates superior performance in terms of frequency recovery over existing industry practices, where generator levels are set based on the solution of single-period classical ACOPF models.

nonlinear model predictive control↗

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Dynamic real‐time optimization for a CO 2 capture process

Growing awareness of climate change has led to increased focus on new energy processes that mitigate generation of CO 2 , or provide for its efficient capture. To enable the development of these processes, advanced modeling and optimization platforms have been created. New capabilities among platforms include efficient solution strategies for online optimization problems. For real time, dynamic optimization of CO 2 capture processes, we demonstrate the application of the Python‐based Pyomo platform to facilitate realization of Moving Horizon Estimation and Nonlinear Model Predictive Control, through novel nonlinear optimization and sensitivity strategies. This capability allows large scale, first principle models to be applied for online optimization. Here, we introduce the Control and Adaptation with Predictive Sensitivity Enhancements (CAPRESE) framework to demonstrate this framework for advanced energy processes. Moreover, we present two case studies: profile tracking with detailed tray‐by‐tray distillation models and Bubbling Fluidized Bed reactors for CO 2 capture. © 2018 American Institute of Chemical Engineers AIChE J , 65: e16511 2019

Thierry, David↗

Comparative Study and Accommodation of Biodiesel in Diesel‐Electric Hybrid Vehicles Coupled with Aftertreatment Systems

Abstract Biodiesel is a promising renewable alternative fuel, which can be used in diesel‐electric hybrid vehicles to potentially bring one kind of clean vehicle on the road. However, the fuel property variation can cause impacts to the powertrain and aftertreatment systems. In this paper, biodiesel's influence on post‐injection effects is studied. Experimental results are obtained to demonstrate the differences between diesel and biodiesel, and to build up fuel‐dependent maps of engine torque, temperature, and emissions. Utilizing those maps, an aftertreatment warm‐up approach compatible with both fuels is developed by strategically enabling double post injections. Finally, a supervisory controller based on model predictive control method is designed to optimize and balance the tradeoff between hybrid vehicle fuel economy and tailpipe emissions. Simulations are conducted for both fuels with or without the proposed control strategy. The validation results show that for both fuels, the controller can significantly reduce tailpipe emissions by successfully regulating the catalyst temperature to the desired range sacrificing fuel economy. And for biodiesel, additional fuel cost is required in order to realize the tradeoff.

Zhao, Junfeng↗

Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

In recent years, as engine control strategies have grown increasingly sophisticated in a continued drive for increasing efficiency and reducing emissions, engine operation has been pushed into regimes that are limited by combustion instabilities. These instabilities produce undesirable abnormal combustion events, which pose barriers to further improvement in engine efficiency. Many of the phenomena involved are difficult to model or control using traditional, purely physics-based models and reactive control approaches. Artificial intelligence (AI) techniques offer some promise for achieving more effective combustion stability control, especially when appropriately applied within a physics-informed framework. This chapter illustrates the current state-of-the-art in applying AI to combustion stability control and examines three case studies with application to the dilute stability limit in spark-ignition engines to illustrate the utility and limitations of AI in these applications.

Maldonado Puente, Bryan↗

Residual {sup 18}F-FDG-PET Uptake 12 Weeks After Stereotactic Ablative Radiotherapy for Stage I Non-Small-Cell Lung Cancer Predicts Local Control

Purpose: To investigate the prognostic value of [{sup 18}F]fluorodeoxyglucose positron emission tomography (FDG-PET) uptake at 12 weeks after stereotactic ablative radiotherapy (SABR) for stage I non-small-cell lung cancer (NSCLC). Methods and Materials: From November 2006 to February 2010, 132 medically inoperable patients with proven Stage I NSCLC or FDG-PET-positive primary lung tumors were analyzed retrospectively. SABR consisted of 60 Gy delivered in 3 to 8 fractions. Maximum standardized uptake value (SUV{sub max}) of the treated lesion was assessed 12 weeks after SABR, using FDG-PET. Patients were subsequently followed at regular intervals using computed tomography (CT) scans. Association between post-SABR SUV{sub max} and local control (LC), mediastinal failure, distant failure, overall survival (OS), and disease-specific survival (DSS) was examined. Results: Median follow-up time was 17 months (range, 3-40 months). Median lesion size was 25 mm (range, 9-70 mm). There were 6 local failures: 15 mediastinal failures, 15 distant failures, 13 disease-related deaths, and 16 deaths from intercurrent diseases. Glucose corrected post-SABR median SUV{sub max} was 3.0 (range, 0.55-14.50). Using SUV{sub max} 5.0 as a cutoff, the 2-year LC was 80% versus 97.7% for high versus low SUV{sub max}, yielding an adjusted subhazard ratio (SHR) for high post-SABR SUV{sub max} of 7.3 (95% confidence interval [CI], 1.4-38.5; p = 0.019). Two-year DSS rates were 74% versus 91%, respectively, for high and low SUV{sub max} values (SHR, 2.2; 95% CI, 0.8-6.3; p = 0.113). Two-year OS was 62% versus 81% (hazard ratio [HR], 1.6; 95% CI, 0.7-3.7; p = 0.268). Conclusions: Residual FDG uptake (SUV{sub max} {>=}5.0) 12 weeks after SABR signifies increased risk of local failure. A single FDG-PET scan at 12 weeks could be used to tailor further follow-up according to the risk of failure, especially in patients potentially eligible for salvage surgery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Dynamic Modeling and Control Studies of a Two-Stage Bubbling Fluidized Bed Adsorber-Reactor for Solid-Sorbent CO{sub 2} Capture

A one-dimensional, non-isothermal, pressure-driven dynamic model has been developed for a two-stage bubbling fluidized bed (BFB) adsorber-reactor for solid-sorbent carbon dioxide (CO{sub 2}) capture using Aspen Custom Modeler® (ACM). The BFB model for the flow of gas through a continuous phase of downward moving solids considers three regions: emulsion, bubble, and cloud-wake. Both the upper and lower reactor stages are of overflow-type configuration, i.e., the solids leave from the top of each stage. In addition, dynamic models have been developed for the downcomer that transfers solids between the stages and the exit hopper that removes solids from the bottom of the bed. The models of all auxiliary equipment such as valves and gas distributor have been integrated with the main model of the two-stage adsorber reactor. Using the developed dynamic model, the transient responses of various process variables such as CO{sub 2} capture rate and flue gas outlet temperatures have been studied by simulating typical disturbances such as change in the temperature, flowrate, and composition of the incoming flue gas from pulverized coal-fired power plants. In control studies, the performance of a proportional-integral-derivative (PID) controller, feedback-augmented feedforward controller, and linear model predictive controller (LMPC) are evaluated for maintaining the overall CO{sub 2} capture rate at a desired level in the face of typical disturbances.

42 ENGINEERING↗

Diagnostics: Chapter 8 of the special issue: on the path to tokamak burning plasma operation

This chapter presents the activity conducted by the ITPA topical group (TG) on Diagnostics over about the last 15 years. Following a general introduction of the ITER Diagnostics led by their measurement roles, the document is organized in several subchapters detailing the design support, research and development activity conducted by each of the specialist working groups (WGs) of the TG. Please note that the magnetic diagnostics were supported at the TG without a specific WG. Their status is included in the general introduction. In the following some highlights of the subchapter’s contents are provided. Recent advances in ITER first wall (FW) diagnostics for the measurements of plasma-metallic wall interaction in support of the ITER research plan are reported. An InfraRed imaging Video Bolometer for ITER has been developed and tested on several tokamaks to measure the radiated power loss. A laser-induced breakdown spectroscopy (LIBS) technique which utilizes a pulsed laser beam to ablate locally by forming a crater, will measure local tritium inventory in the FW material. Real-time Residual Gas Analyzers will measure the neutral gas composition in a divertor port and an equatorial port during plasma operation. Due to the full metallic FW environment, the plasma-wall interaction in ITER will face several challenges such as the compromised radiated power and divertor heat flux measurements by reflection. Ray tracing and analysis codes have been developed to eliminate and correct the effects of reflection in the measurements. The characteristics of the reflecting surfaces depending on the roughness and angle of the incidence have been measured by dedicated experiments, and the results were applied to the reflection elimination. For the measurement of the metallic impurity radiation induced by eroded metallic atoms, a vacuum ultraviolet spectrometer has been developed and tested. An extensive thermonuclear diagnostic suite will be required to support the operation of ITER and the planned experimental program for future burning plasma experiments. Due to the harsh environmental conditions, the implementation of diagnostic systems in ITER is a major challenge. These conditions include high levels of neutron and gamma fluxes, neutron heating, particle bombardment. Therefore, the selection and design of diagnostic systems must take into account a number of phenomena previously unseen in diagnostic design. For this reason, the measurement of neutrons and confined or lost fast ions, with particular emphasis on alpha particles, is critical to ITER. The diagnostics associated with these measurements will be important for future plasma-burning experiments at ITER. The high neutron emission and very large plasma size in ITER make neutron diagnostics the main diagnostic method used to measure plasma parameters such as fusion power, fusion power density, ion temperature, energy of fast ions and their spatial distributions in the plasma core. Active spectroscopy techniques are methods where a neutral particle beam is injected into the plasma and information on plasma parameters is extracted from the measurement of line emission resulting from the beam-plasma interaction, either by plasma ions or by beam atoms. Spatial localization is achieved by crossing the beamline and multiple observation lines. The ITER plasma will be a high temperature, moderately dense, fully ionized collisional plasma. The plasma facing surfaces are principally metallic being fashioned from beryllium or tungsten but many other elements, arising from either structural or from operational needs, may enter this plasma. The energy range of the emitted photons range from meV (infra-red) to multi keV (x-rays) and originate from all areas of the plasma volume. The primary role of passive emission diagnostics is to identify what is in the plasma from spectral signatures. Extracting quantitative information from these measurements such as impurity content, ion temperature, rotation, degree of detachment and radiated power depends on calibrated instruments, a physics model of the atomic and molecular processes and plasma transport and an analysis workflow that takes into account environmental effects such as reflections. The particular needs for ITER have prompted a multi-machine, many-year effort to address all these aspects and this chapter reviews the work on diagnostic design, experiments and new analysis techniques. An overview of the laser diagnostics to be implemented on ITER is also provided in this paper. This includes descriptions of the Thomson scattering in the core, edge and divertor regions, polarimetry and interferometry diagnostics used for measuring plasma density and also measurements of helium density in the divertor using Laser Induced Flourescence. Techniques which can allow improvements on current measurements are also addressed in particular expanding poloidal polarimetry measurements to measure field fluctuations and proposed use of dispersion interferometery which has a number of advantages over existing methods. This paper identifies particular areas where further research and testing on existing tokamaks is useful even at this advanced stage to inform the design of diagnostics for ITER. Outstanding areas of concern for the implementation of laser diagnostics, in particular with a view to reliable operation are identified. An overview of the latest developments of microwave diagnostic systems and techniques is given. The primary focus is the contributions for ITER—the next step burning plasma experiment—which is supplemented by describing recent progress of techniques applicable for fusion experiments beyond ITER. The contributions are intentionally kept concise, and are being supplemented by a rich list of references for further studies. Radiation induced effects are receiving continuous and well-deserved attention of the ITER diagnostic community and they are in many cases one of the primary design drivers of the ITER diagnostic systems. The paper summarizes recent progress in this area focusing primarily on the ITER diagnostics but in some cases provides also outlook for the possible solutions for even more demanding radiation environment of fusion reactors beyond ITER. Despite advancements in the area of modeling and simulation of various radiation induced effects, experimental testing in a nuclear environment as close as possible to the target one is still seen as unavoidable for proper qualification of particular diagnostic functional elements. Recent advancement within three diagnostic areas: optical diagnostics, magnetics and bolometers is covered. Encouraging results on qualification of silica glass vacuum window assemblies are presented. In the area of magnetic sensors, progress of irradiation tests performed on ITER in-vessel LTCC inductive sensors is presented with outlook for novel technological approaches to inductive sensors utilizing thick printing and photolithography technologies being highlighted. Summary of advancements in the area of steady state magnetic field sensors based on Hall effect is given. New results of neutron irradiation test of the ITER borosilicate glass inserts for vacuum electrical feedthroughs are summarized finding negligible swelling at target level of neutron fluence. Off-line irradiation tests of fiber optic current sensors for plasma current measurement demonstrated that both for gamma doses up to 5 MGy and a total neutron fluence up to 10 15 cm −2 , radiation induced changes are still compatible with required measurement accuracy on ITER. The ITER bolometers are given as an example how considering radiation effects may influence the diagnostic design. Finally, outlook for future main R&D directions is outlined. All optical and laser-based diagnostics in ITER will be using mirrors to guide plasma radiation toward detectors, cameras and sensors. In the hostile plasma, radiation and particle environment the optical characteristics of diagnostic mirrors will degrade directly affecting the entire performance of involved diagnostic systems. An assessment of factors affecting mirror performance is provided. Among the prime adverse factors are deposition of plasma impurities, sputtering of mirror surface and steam ingress in the vicinity of mirrors. Within the International Tokamak Physics Activity with active support by ITER central team and domestic agencies, the structured research and development (R&D) program on mitigation of risks for diagnostic mirrors is underway. Within this program the mirror material development, the passive mitigation of mirror degradation by using diagnostic ducts and shutters along with an active mirror recovery program comprising the in-situ mirror cleaning and calibration is underway. Recent developments in diagnostic mirror R&D are described in this Chapter along with an example of their implementation of R&D solutions in ITER Infrared Thermography diagnostic. An assessment of still open engineering and physics questions, considerations on mirror risks during an early phase of ITER operation are given along with an overview of diagnostic mirror evolution in the late ITER operation stage toward the demonstration fusion power plant. Several crucial areas of diagnostic R&D outlined in ITER Research Plan are addressed. The basic control groups in a fusion reactor can be broken-down in five categories: (1) plasma position, magnetic configuration, and plasma current control, (2) profile control and confinement optimization, (3) MHD control and suppression, (4) edge dissipation control, radiation and plasma exhaust control and (5) break-down optimization. These categories are coupled via the physics (a control action in one domain will affect the other domains) and via shared actuators (e.g. ECRH for impurity accumulation avoidance, current density distribution control and MHD suppression). Consequently, a supervisory control system should determine the priority of the various control tasks, their couplings, and the interfaces with the safety and interlock system. For the systematic development of the various controllers taking the complexity of the plasma and the control system into account, a model-based approach is required. A short historical overview is given of the developments in systems and control theory and control engineering with special emphasis on those developments that are most relevant for Nuclear Fusion research and operation. An overview is given of the state of the field of fusion plasma control for the control categories. It will be shown how synthetic diagnostics are being developed in ITER and how they are used in diagnostic design and design validation and how they can be in model-based controller synthesis using relatively simple models. In modern control methods, multiple diagnostics are used to constrain relatively simple models. The constrained models provide an estimate for the state. This opens the route to state controllers, such as model predictive control. A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modeling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g. to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modeling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of integrated data analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

ITER↗

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Framework for Multi Layer Control in Python (FMLC) v1.0

This package is developed as a backend for multi-layer and multi-time domain controller. One example are controller based on [Model Predictive Control] (MPC) where different modules with different time constants have to be coordinated to allow optimal control. In a simple example, the Weather Forecast must be acquired and processed, the MPC is computed, and finally the control is applied. All these modules typically run on the same time step (Time step: 5 minutes), but are different instances within the framework. The framework allows parallelization using the `multiprocessing` module. However, another real-time controller (Time step: 1 second) must be applied to react on instant system disturbances beyond the MPC time step. Last, a supervisory MPC controller (Time step: 1 hour) might be added to ensure global optimality.

Gehbauer, Christoph↗