SEARCH · Search NASA

Results for “distributed predictive 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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Model Predictive Control Strategies for Turbine Electrified Energy Management

The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM’s application beyond traditional proportional-integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Model Predictive Control↗

NASA NTRSDate not supplied

Model Predictive Control Strategies for Turbine Electrified Energy Management

The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM’s application beyond traditional proportional integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Model Predictive Control↗

NASA NTRS2023

Model Predictive Control Strategies for Turbine Electrified Energy Management

Model Predictive Control↗

NASA NTRS1975

Technology of forced flow and once-through boiling: A survey

Representative boiling heat transfer and pressure drop information obtained primarily from past NASA and AEC programs is presented which is applicable to forced flow and once-through boiler systems. The forced convection boiler has a number of advantages: little possibility of flow mal-distribution; heat transfer characteristics are usually consistent; and conductances are predictable, so that higher heat fluxes may be employed with safety (which leads to more compact, lighter weight equipment). It was found that in gas-fired systems particularly, the controlling heat transfer resistance may be on the hot side, so that increased fluxes would require extended surfaces. If in a power generation system the working fluid is very expensive, a forced flow boiler can be designed especially for small holdup volume. If the fluid is temperature sensitive, the boiling side wall temperatures can be tailored to maintain maximum heat transfer rates without overheating the fluid. The forced flow and once-through configurations may be the only type which can satisfy a specific need (such as the automotive Rankine cycle power plant design having a very short time-response boiler).

Poppendieck, H. F.↗

NASA NTRS1986

Design and experimental evaluation of a swept supercritical Laminar Flow Control (LFC) airfoil

A large chord swept supercritical laminar flow control (LFC) airfoil was designed, constructed, and tested in the NASA Langley 8-ft Transonic Pressure Tunnel (TPT). The LFC airfoil experiment was established to provide basic information concerning the design and compatibility of high-performance supercritical airfoils with suction boundary layer control achieved through discrete fine slots or porous surface concepts. It was aimed at validating prediction techniques and establishing a technology base for future transport designs and drag reduction. Good agreement was obtained between measured and theoretically designed shockless pressure distributions. Suction laminarization was maintained over an extensive supercritical zone up to high Reynolds numbers before transition gradually moved forward. Full-chord laminar flow was maintained on the upper and lower surfaces at M sub infinity = 0.82 up to R sub c is less than or equal to 12 x 10 to the 6th power. When accounting for both the suction and wake drag, the total drag could be reducted by at least one-half of that for an equivalent turbulent airfoil. Specific objectives for the LFC experiment are given.

Harvey, W. D.↗

NASA NTRS2010

Figure of Merit Characteristics Compared to Engineering Parameters

A workshop held in 2005 defined a large number of parameters of interest for users of lunar simulants. The need for formal requirements and standards in the manufacture and use of simulants necessitates certain features of measurements. They must be definable, measureable, useful, and primary rather than derived. There are also certain features that must be avoided. Analysis of the total parameter list led to the realization that almost all of the parameters could be tightly constrained, though not predicted, if only four properties were measured: Particle composition, particle size distribution, particle shape distribution, and bulk density. These four are collectively referred to as figures of merit (FoMs). An evaluation of how each of the parameters identified in 2005 is controlled by the four FoMs is given.

Rickman, D.L.↗

NASA NTRSDate not supplied

Uncertainty Estimates for Sonic-Boom Pressure Signatures and Loudness Carpets

A non-intrusive uncertainty quantification method is applied to computational analysis of supersonic, low-boom aircraft. The mean and standard deviation statistics of the pressure waveforms and loudness metrics are evaluated through use of numerical quadrature. The probability density function (p.d.f.) of these outputs is evaluated via kernel density estimation. The simulations use an inviscid, embedded-boundary Cartesian-mesh flow solver in the nearfield combined with an augmented Burgers’ equation solver for propagation in the farfield. The results show that the p.d.f. of the waveform is bimodal at shocks, which makes the mean and standard deviation statistics inappropriate. Despite this limitation, we show that the moment statistics can provide effective assessment of discrepancies when comparing with experimental data. This is demonstrated by presenting uncertainty analysis of a wind-tunnel test and showing that we significantly improve the predictions when we include the test uncertainties in the simulation. Normal distributions are obtained for the ground signature and loudness metrics, which is primarily due to the careful shaping of the low-boom waveform. Separation of variables and error control are used to reduce computational cost. We demonstrate that this is an efficient approach in the sense of balancing numerical errors in the statistics quadrature with discretization errors in the solvers.

ARMD↗

DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗

NASA NTRS1993

Spray Combustion Experiments and Numerical Predictions

The next generation of commercial aircraft will include turbofan engines with performances significantly better than those in the current fleet. Control of particulate and gaseous emissions will also be an integral part of the engine design criteria. These performance and emission requirements present a technical challenge for the combustor: control of the fuel and air mixing and control of the local stoichiometry will have to be maintained much more rigorously than combustors in current production. A better understanding of the flow physics of liquid fuel spray combustion is necessary. This presentation describes recent experiments on spray combustion where detailed measurements of the spray characteristics were made, including local drop-size distributions and velocities. In addition, an advanced combustor CFD code has been under development and predictions from this code are presented and compared with measurements. Studies such as these will provide information to the advanced combustor designer on fuel spray quality and mixing effectiveness. Validation of new fast, robust, and efficient CFD codes will also enable the combustor designer to use them as valuable design tools for optimization of combustor concepts for the next generation of aircraft engines.

Mularz, Edward J.↗

NASA NTRS1993

Spray combustion experiments and numerical predictions

The next generation of commercial aircraft will include turbofan engines with performance significantly better than those in the current fleet. Control of particulate and gaseous emissions will also be an integral part of the engine design criteria. These performance and emission requirements present a technical challenge for the combustor: control of the fuel and air mixing and control of the local stoichiometry will have to be maintained much more rigorously than with combustors in current production. A better understanding of the flow physics of liquid fuel spray combustion is necessary. This paper describes recent experiments on spray combustion where detailed measurements of the spray characteristics were made, including local drop-size distributions and velocities. Also, an advanced combustor CFD code has been under development and predictions from this code are compared with experimental results. Studies such as these will provide information to the advanced combustor designer on fuel spray quality and mixing effectiveness. Validation of new fast, robust, and efficient CFD codes will also enable the combustor designer to use them as additional design tools for optimization of combustor concepts for the next generation of aircraft engines.

Mularz, Edward J.↗

DOE OSTI2021

Neoclassical toroidal viscosity torque prediction via deep learning

GPECnet is a densely connected neural network that has been trained on GPEC data, to predict the plasma stability, neoclassical toroidal viscosity (NTV) torque, and optimized 3D coil current distributions for desired NTV torque profiles. Using NTV torque, driven by non-axisymmetric field perturbations in a tokamak, can be vital in optimizing pedestal performance by controlling the rotation profile in both the core, to ensure tearing stability, and the edge, to avoid edge localized modes (ELMs). The generalized perturbed equilibrium code (GPEC) software package can be used to calculate the plasma stability to 3D perturbations and the NTV torque profile generated by applied 3D magnetic fields. These calculations, however, involve complex integrations over space and energy distributions, which takes time to compute. Initially, GPECnet has been trained solely on data representative of the quiescent H-mode (QH) scenario, in which neutral beams are often balanced and toroidal rotation is low across the plasma profile. Lastly, this work provides the foundation for active control of the rotation shear using a combination of beams and 3D fields for robust and high performance QH mode operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra: Supporting Data

High-resolution classification maps derived from occupied aerial systems (UASs). The UAS data were collected in August 2021 using a Skydio 2+ drone equipped with a 4K resolution red-green-blue (RGB) camera (2024 Skydio Inc) and a 3DR SOLO Quadcopter carried a Parrot Sequoia+ Multispectral Sensor (2023 Parrot Drone SAS). This package includes vegetation classification maps at four locations around Next Generation Ecosystem Experiment Arctic (NGEE Arctic) Council watershed study site on the Seward Peninsula, Alaska. The classification maps were generated using a combination of RGB and canopy height information. The map data and metadata are provided as ENVI image (.dat) and text (.txt, *hdr) formats. Additional map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In review), “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1971

A generalized theory on the noise generation from supersonic shear layers.

A generalization is presented of Phillips' (1960) theory of noise generation by supersonic turbulent shear layers. Both Mach wave radiation and non-Mach wave noise radiation mechanisms are considered. The range of validity of Phillips' theory has been expanded to include the low supersonic and transonic ranges. These generalizations are important not only for their analytical rigor, but also for their prospective applications to practical problems in jet noise prediction and control. The noise generation mechanisms in a supersonic jet are found to differ from those in a subsonic jet. The theory is considered to offer some prospects of answering important questions in supersonic jet noise, such as noise source distribution, mean flow refraction effects, directivity, spectrum, and efficiency of noise radiation.

Pao, S. P.↗

NASA NTRS1999

Unstructured Grid Euler Method Assessment for Aerodynamics Performance Prediction of the Complete TCA Configuration at Supersonic Cruise Speed

Unstructured grid Euler computations, performed at supersonic cruise speed, are presented for a proposed high speed civil transport configuration, designated as the Technology Concept Airplane (TCA) within the High Speed Research (HSR) Program. The numerical results are obtained for the complete TCA cruise configuration which includes the wing, fuselage, empennage, diverters, and flow through nacelles at Mach 2.4 for a range of angles-of-attack and sideslip. The computed surface and off-surface flow characteristics are analyzed and the pressure coefficient contours on the wing lower surface are shown to correlate reasonably well with the available pressure sensitive paint results, particularly, for the complex shock wave structures around the nacelles. The predicted longitudinal and lateral/directional performance characteristics are shown to correlate very well with the measured data across the examined range of angles-of-attack and sideslip. The results from the present effort have been documented into a NASA Controlled-Distribution report which is being presently reviewed for publication.

Farhad Ghaffari↗

NASA NTRS1993

Spray combustion experiments and numerical predictions

The next generation of commercial aircraft will include turbofan engines with performance levels significantly better than those in the current fleet. Control of particulate and gaseous emissions will also be an integral part of the engine design criteria. These performance and emission requirements present a technical challenge for the combustor: control of the fuel and air mixing and control of the local stoichiometry will have to be maintained much more rigorously than with combustors in current production. A better understanding of the flow physics of liquid fuel spray combustion is necessary. This paper describes recent experiments on spray combustion where detailed measurements of the spray characteristics were made, including local drop-size distributions and velocities. Also, an advanced combustor CFD code has been under development and predictions from this code are compared with experimental results. Studies such as these will provide information to the advanced combustor designer on fuel spray quality and mixing effectiveness. Validation of new fast, robust, and efficient CFD codes will also enable the combustor designer to use than as valuable additional design tools for optimization of combustor concepts for the next generation of aircraft engines.

Mularz, Edward J.↗

DOE OSTI2024

Four-dimensional phase-space reconstruction of flat and magnetized beams using neural networks and differentiable simulations

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling in hadron beams. Preparing these beams requires precise control and characterization of the four-dimensional transverse phase space. In this study, we employ generative phase-space reconstruction techniques to rapidly characterize magnetized and flat-beam phase-space distributions using a conventional quadrupole-scan method. The reconstruction technique is experimentally demonstrated on an electron beam produced at the Argonne Wakefield Accelerator and successfully benchmarked against conventional diagnostics techniques. Specifically, we show that predicted beam parameters from the reconstructed phase-space distributions (e.g., as magnetization and flat-beam emittances) are in excellent agreement with those measured from the conventional diagnostic methods. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Measurement of free chlorine levels in water using potentiometric responses of biofilms and applications for monitoring and managing the quality of potable water

We report residual free chlorine is not monitored continuously at scale in drinking water distribution systems because existing real-time sensor technologies require frequent maintenance, cleaning, and calibration, which makes these products too costly to be used throughout a distribution system. As a result, current measurement approaches require manual sampling, which is not feasible for the consistent monitoring of free chlorine because chlorine concentrations vary significantly throughout pipeline distribution and over time and space. This research presents an alternative and cost-effective method of predicting free chlorine levels in drinking water using graphite electrodes coated with naturally grown microbial biofilms. This Microbial Potentiometric Sensor (MPS) array was installed in a Continuously Mixed Batch Reactor (CMBR), and drinking water containing variable free chlorine concentrations. The chlorine concentrations were introduced in a controlled manner, and the MPS signals were monitored over time. MPS signals were measured from the change in Open Circuit Potential (OCP) across the MPS array in real-time. An empirically derived relationship between the normalized change in OCP and free chlorine was established by fitting individual and average MPS data to a decaying exponential growth function in order to predict free chlorine levels. The results show that free chlorine can be predicted with reasonable accuracy, with model validation showing an average absolute error of ±0.09 ppm below 1.1 ppm and ±0.30 ppm between 1.1 and 2.7 ppm. However, the accuracy of predictions was reduced at higher free chlorine levels. The researchers conclude that MPS systems may benefit drinking water distribution systems by measuring free chlorine. These advantages of the MPS are especially pronounced in the developing world because this system is inexpensive and does not require routine maintenance or cleaning. The system relies on a naturally forming and regenerating biofilm and an inexpensive potentiometric meter to produce stable measurements.

54 ENVIRONMENTAL SCIENCES↗