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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
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↗
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↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
NASA NTRS1990

Experimental investigation of a new device to control the asymmetric flowfield on forebodies at large angles of attack

An exploratory experimental investigation of a new device to control the asymmetric flowfield on forebodies at large angles of attack has been conducted. The device is a rotatable forebody tip, which varies in cross section from circular at its base to elliptic at its tip. The device itself extends over a small portion of the aircraft or missile forebody. The device provides two important improvements. First, it replaced the normally random behavior of the nose side force as a function of nose tip orientation with a predictable and generally sinusoidal distribution and, second, the device showed promise for use as part of a vehicle control system, to be deflected in a prescribed manner to provide additional directional control for the vehicle. The device was tested on a cone/cylinder model having a 10 deg semiapex angle and on a 3.0 caliber tangent ogive model, each with a base diameter of 3.5 in, for angles of attack from 30 to 60 deg. Data were taken from 3 circumferential rows of pressure taps on each model at a Reynolds number of 84,000 based on cylinder diameter and by a helium-bubble flow visualization technique at a Reynolds number of 24,000.

Moskovitz, Cary A.↗
DOE OSTI2018

Data Driven Temperature Control of Heat Exchangers

The following poster presents a couple of approaches for temperature control at heat exchanger’s exit. One of the approaches is based on using n-step ahead neural network while the other approach utilizes a combination of control + learning module for temperature control. Designing robust controllers for typical heat exchangers is very challenging primarily because of complexities associated with the synthesis of dynamics of industrial heat exchangers. Even if a dynamics model is synthesized, the accuracy of it would be called into question as there can be lot of unmodeled system dynamics and random noise in input parameters. In many situations, the degree of uncertainty in the model of system being controlled limits the utility of optimal control design. Controller can be manually tuned on the field but it is very difficult to determine manual adjustments that result in overall improvement. These practical challenges call for learning based approaches for temperature control that would result in optimal heat exchanger performance. We utilize the existing mathematical model for air-water system synthesized by Underwood and Crawford for testing our algorithms. Underwood and Crawford developed a model of heating coil by fitting a second-order non-linear equations to measurements of air and water temperature and flow rates obtained from the actual coil. The uncertainties in the model include air and water inlet temperatures and air-flow rate. These uncertainties are modeled as random variables that are sampled from a uniform distribution. The control variable is the water flow rate which is controlled for regulating the air-outlet temperature. Neural networks are frequently used in various literatures for learning time-series. We propose a modified version of neural networks known as n-step ahead neural network that take the state variables and output at time t as input to successfully predict output at time t+n. The variable n is a hyper-parameter which can be optimized based on performance constraints. The n-step ahead neural network was then trained on training data generated from experimental model to high training accuracies. This trained model performed appreciably in its objective to regulate outlet temperature of a heat exchanger. The second approach uses a combination of control and learning module for temperature regulation. The control module used is a proportional controller while the learning module used is steady state neural network. The steady state neural network is used to learn the controller output as a function of random variables and temperature set-point. The proportional controller is then used to compensate for transient behavior of linearized system around various operating points. The algorithm works effectively for temperature regulation as neural network captures the non-linear input-output behavior of closed loop system leaving proportional controller to compensate for steady state errors of a linearized plant. The proposed algorithm’s performances were evaluated against the performance of a PI controller. Further, the robustness of the algorithms were also studied in great detail under small perturbations around tuned parameters of the proposed algorithms.

Sundar, Sreenath↗
NASA NTRS2009

Control of the Diameter and Chiral Angle Distributions during Production of Single-Wall Carbon Nanotubes

Many applications of single wall carbon nanotubes (SWCNT), especially in microelectronics, will benefit from use of certain (n,m) nanotube types (metallic, small gap semiconductor, etc.) Especially fascinating is the possibility of quantum conductors that require metallic armchair nanotubes. However, as produced SWCNT samples are polydisperse, with many (n,m) types present and typical approx.1:2 metal/semiconductor ratio. Nanotube nucleation models predict that armchair nuclei are energetically preferential due to formation of partial triple bonds along the armchair edge. However, nuclei can not reach any meaningful thermal equilibrium in a rapidly expanding and cooling plume of carbon clusters, leading to polydispersity. In the present work, SWCNTs were produced by a pulsed laser vaporization (PLV) technique. The carbon vapor plume cooling rate was either increased by change in the oven temperature (expansion into colder gas), or decreased via "warm-up" with a laser pulse at the moment of nucleation. The effect of oven temperature and "warm-up" on nanotube type population was studied via photoluminescence, UV-Vis-NIR absorption and Raman spectroscopy. It was found that reduced temperatures leads to smaller average diameters, progressively narrower diameter distributions, and some preference toward armchair structures. "Warm-up" shifts nanotube population towards arm-chair structures as well, but the effect is small. Possible improvement of the "warm-up" approach to produce armchair SWCNTs will be discussed. These results demonstrate that PLV production technique can provide at least partial control over the nanotube (n,m) population. In addition, these results have implications for the understanding the nanotube nucleation mechanism in the laser oven.

Nikolaev, Pavel↗
NASA NTRS1980

Determination of efficiencies, loss mechanisms, and performance degradation factors in chopper controlled dc vehical motors. Section 2: The time dependent finite element modeling of the electromagnetic field in electrical machines: Methods and applications

The time dependent solution of the magnetic field is introduced as a method for accounting for the variation, in time, of the machine parameters in predicting and analyzing the performance of the electrical machines. The method of time dependent finite element was used in combination with an also time dependent construction of a grid for the air gap region. The Maxwell stress tensor was used to calculate the airgap torque from the magnetic vector potential distribution. Incremental inductances were defined and calculated as functions of time, depending on eddy currents and saturation. The currents in all the machine circuits were calculated in the time domain based on these inductances, which were continuously updated. The method was applied to a chopper controlled DC series motor used for electric vehicle drive, and to a salient pole sychronous motor with damper bars. Simulation results were compared to experimentally obtained ones.

Hamilton, H. B.↗
DOE OSTI2022

Predictive Direct DC-Link Control for 7.2 kV Three-Port Low-Inertia Solid-State Transformer With Active Power Decoupling

Promising for applications including renewable energy and electric vehicle fast charging, a medium-voltage (MV) solid-state transformer (SST) typically has multiple modules series stacked, which requires the modules to be single phase. One critical issue of the single-phase SST is the double-line-frequency power ripple. Traditionally, large passives are used to buffer the ripple, resulting in significantly increased volume and cost. This article for the first time proposes active power decoupling (APD) for the SST and a predictive direct DC-link control method, using a current-source single-stage SST as an example. An electrolytic capacitor-less buffer port is used to absorb the ripple, which tolerates up to a 30% voltage ripple for small capacitance. The APD SST control is challenging. First, the current-source SST realizes isolation, DC link, and multiport in a single stage. Second, different from conventional SSTs or APD converters, the low-inertia DC link in this SST has a 40% switching ripple, which is difficult to stabilize. To address the control challenge, a direct DC-link control architecture and a predictive control method are proposed. Simulation and experimental results verify the proposed control method on an MV SiC modular soft-switching solid-state transformer (M-S4T) prototype. Here, the proposed APD reduces the SST volume by 53.8%. Importantly, the proposed concept is not limited to the M-S4T but is generic to other SSTs or APD converters.

14 SOLAR ENERGY↗
NASA NTRS1993

A lag model for turbulent boundary layers developing over rough bleed surfaces

Boundary layer mass removal (bleed) through spanwise bands of holes on a surface is used to prevent or control separation and to stabilize the normal shock in supersonic inlets. The addition of a transport equation lag relationship for eddy viscosity to the rough wall algebraic turbulence model of Cebeci and Chang was found to improve agreement between predicted and measured mean velocity distributions downstream of a bleed band. The model was demonstrated for a range of bleed configurations, bleed rates, and local free stream Mach numbers. In addition, the model was applied to the boundary layer development over acoustic lining materials for the inlets and nozzles of commercial aircraft. The model was found to yield accurate results for integral boundary layer properties unless there was a strong adverse pressure gradient.

Lee, J.↗
NASA NTRS2001

NASA IVHM Technology Experiment for X-vehicles (NITEX)

The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.

Sandra, Hayden↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
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↗
NASA NTRS2015

Effect of Interfacial Turbulence and Accommodation Coefficient on CFD Predictions of Pressurization and Pressure Control in Cryogenic Storage Tank

Laminar models agree closely with the pressure evolution and vapor phase temperature stratification but under-predict liquid temperatures. Turbulent SST k-w and k-e models under-predict the pressurization rate and extent of stratification in the vapor but represent liquid temperature distributions fairly well. These conclusions seem to equally apply to large cryogenic tank simulations as well as small scale simulant fluid pressurization cases. Appropriate turbulent models that represent both interfacial and bulk vapor phase turbulence with greater fidelity are needed. Application of LES models to the tank pressurization problem can serve as a starting point.

Condensation Evaporation↗
NASA NTRS1993

Effect of geometry, static stability, and mass distribution on the tumbling characteristics of generic flying-wing models

Results from an investigation to determine the low-speed tumbling characteristics of twelve generic flying-wing models are summarized. There is a concern that airplanes with flying-wing planforms could inadvertently enter an out-of-control tumbling motion under certain conditions. The objectives of this investigation were to: 1) identify the geometric and mass-related parameters that cause flying wings to be capable of sustained tumbling, 2) analyze some of the driving mechanisms that cause tumbling, and 3) determine the feasibility of using computer simulations to predict the tumbling characteristics of flying wings. Free-tumble and free-to-pitch tests were conducted with dynamically-scaled, generic flying wing models. The use of computer simulations as a predictive tool for tumbling was explored. Results indicated that center-of-gravity location, mass distribution, and geometric aspect ratio strongly affected the tumbling characteristics of the models tested and that positive static stability did not necessarily preclude tumbling. The magnitude of dynamic effects were found to be of the same order as static effects for the models undergoing autorotation-in-pitch. The simulations indicated that the dynamic terms in the equations of motion used to predict tumbling must be obtained using experimental methods that account for the large amplitude/high pitch-rate environment that characterizes tumbling.

Fremaux, C. M.↗
DOE OSTI2021

Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments

Thiospinels, such as CoNi 2 S 4 , are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi 2 S 4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi 2 S 4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 5–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/$\bar{d}$) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Furthermore, our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

GGOT total pressure loss control concept evaluation

Total pressure loss is one of the most important parameters in the design of a turbine. This parameter effects not only the turbine performance, but consequently the engine power balance and engine performance. Computational Fluid Dynamics (CFD) can be an effective tool in predicting turbine total pressure loss, and also for performing sensitivity studies to achieve an optimal design with respect to pressure loss. In the present study, the AEROVISC code was used to predict the total pressure loss in the Turbine Technology Team Gas Generator Oxidizer Turbine (GGOT). The objectives in this study are two-fold. It is first necessary to determine an optimal methodology in predicting total pressure loss. The type of grid, grid density and distribution are parameters which may affect the loss prediction. Also, the effect of using a standard K-epsilon turbulence model with wall functions versus a two-layer turbulence model needs to be investigated. The use of grid embedding to resolve areas with high flow gradients needs to be explored. The second objective of the study is to apply the optimal methodology toward evaluating different tip leakage control concepts.

Blumenthal, R. F.↗
DOE OSTI2021

A novel construct for scaling groundwater–river interactions based on machine-guided hydromorphic classification

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. Predictive numerical models are needed to understand responses of river corridors to environmental change and to support sustainable watershed management. We posit that systematic hydromorphic classification provides a scaling construct that facilitates extrapolation of outputs from local-scale mechanistic models to reduced-order models applicable at reach and watershed scales. This in turn offers the potential to improve large-scale predictions of river corridor hydrobiogeochemical processes. Here we present a new machine-guided hydromorphic classification methodology that addresses the key requirements of this objective, and we demonstrate its application to a segment of the Columbia River in the northwestern United States. The resulting hydromorphic classes form spatially coherent and physically interpretable hydromorphic units that exhibit distinct behaviors in terms of distributions of subsurface residence times (a primary control on critical biogeochemical reactions). This approach forms the basis of ongoing research that is evaluating the formulation of reduced-order models and transferability of results to other river reaches and larger scales.

54 ENVIRONMENTAL SCIENCES↗