rrlfe : software for generating and applying metallicity calibrations for RR Lyrae variable stars across a wide range of phases and temperatures
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Eckhart Spalding↗
SEARCH · Engineering Papers
Results for “variable generation”
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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Stability and control of power systems with high penetrations of inverter-based resources: An accessible review of current knowledge and open questions
As power system renewable energy penetrations increase, the ways in which key renewable technologies such as wind and solar photovoltaics (PV) differ from thermal generators become more apparent. Many studies have examined the variability and uncertainty of such generators and described how generation and load can be balanced for a wide variety of annual energy penetrations, at timescales from seconds to years. Another important characteristic of these resources is asynchronicity, the result of using inverters to interface the prime energy source with the power system as opposed to synchronous generators. Unlike synchronous generators, whose frequency of alternating current (AC) injection is physically coupled to the rotation of the machine itself, inverter based asynchronous generators do not share the same physical coupling with the generated frequency. These subtle differences impact the operations of power systems developed around the characteristics of synchronous generators. In this paper we review current knowledge and open research questions concerning the interplay between asynchronous inverter-based resources (IBRs) and cycle- to second-scale power system dynamics, with a focus on how stability and control may be impacted or need to be achieved differently when there are high instantaneous penetrations of IBRs across an interconnection. This work does not seek to provide a comprehensive review of the latest developments, but is instead intended to be accessible to any reader with an engineering background and an interest in power systems and renewable energy. As such, the paper includes basic material on power electronics, control schemes for IBRs, and power system stability; and uses this background material to describe potential impacts of IBRs on power system stability, operational challenges associated with large amounts of distributed IBR generation, and modern power system simulation trends driven by IBR characteristics.
14 SOLAR ENERGY↗
Extended Low Load Boiler Operation to Improve Performance and Economics of an Existing Coal Fired Power Plant (Final Report)
The overall goal is to improve the performance and economics of existing coal fired power plants by extending low load boiler operation to lower loads than is currently achievable. The objective of this program is to develop and validate sensor hardware and analytical algorithms to lower plant operating expenses (OPEX) for the currently operating pulverized coal utility boiler fleet. Coal fired utility boilers are increasingly under grid dispatch pressure. In some cases, the coal fired cost of generation is noncompetitive with respect to natural gas generation and subsidized renewable sources. To remain profitable and remain fully compliant with existing environmental regulations, the installed coal fired fleet must find technologies which allow it to move into a more flexible cyclic load dispatch model. Today the installed coal fired utility fleet must be cost of generation competitive, fully emissions compliant, and responsive to the variability inherent in renewable energy generation sources. In the Phase I of the project, GE Steam Power, Inc. (GE) performed modeling of different operating scenarios for low load operation using an existing full plant dynamic model developed for a 660MW steam power plant. Sensors and analytic algorithms to enable a stable and steady coal supply for low load pulverizer operation were identified and tested at the Pulverizer Development Facility (PDF) at GE’s Clean Energy Center in Bloomfield, Connecticut. Sensors and analytic algorithms to enable stable combustion for low load operation were identified and tested at the 15 MWth Industrial Scale Burner facility (ISBF) at GE’s Clean Energy Center. A concept was developed to test the sensors and control algorithms, down selected after testing, at a full-scale coal fired power plant. A budget estimate was then developed, and the concept was implemented at an existing utility power plant. The specific objectives of the experimental work were to: • Identify and select sensors and analytic algorithms for monitoring coal pulverizer operation at lower loads to provide stable operation and appropriate coal fineness at lower coal throughput; Identify and select sensors and analytic algorithms for a Boiler Flame Stability Monitor to better balance air and fuel at each burner. This enables a reduction in a coal boiler’s safe low load power level while maintaining stable flame characteristics; Develop a concept in Phase I for low load operation of a full-scale power plant and develop a budget estimate for testing and execute the test plan at an existing plant in Phase II; Validate the capability of the extended low load boiler system to extend the minimum load operating point in a safe and reliable manner on an existing full-scale utility boiler. At the completion of this experimental study, GE has developed a set of sensors and analytic algorithms, down selected after testing, that have the potential to enable safe low load operation of a utility boiler. GE has also identified a host site for testing these identified sensors and analytic algorithms. GE has generated a full set of deliverables that provide sufficient information to proceed with the next step of testing at a host site. This includes a potential host site and budget estimate for concept testing at host site. In the Phase II of the project, a series of field tests were completed to validate the extended low load boiler operation, which consisted of detailed engineering, installation, commissioning, and testing the additional sensors and analytics for the coal-fired combustion system on an existing full-scale utility boiler. The optimization work has been supported by the host plant and endorsed by their engineering and operation staff.
01 COAL, LIGNITE, AND PEAT↗
Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response
A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.
36 MATERIALS SCIENCE↗
Fractionation of Iron and Titanium Isotopes By Ilmenite and the Isotopic Compositions of Lunar Magma Ocean Cumulates
Basaltic volcanism on the Moon produced low- and high-Ti mare basalt suites that are also distinct with respect to their iron, titanium, and magnesium iso- topic compositions. Here, the equilibrium fractionation of Fe and Ti isotopes between ilmenite and melt was experimentally investigated in order to evaluate the role of ilmenite in generating the isotopic compositional variability among the lunar mare basalts. Ilmenite crystallization experiments were conducted using two bulk compositions: an ilmenite-saturated basaltic andesite and an ilmenite-saturated Apollo 14 black glass, and the Fe and Ti isotopic compo- sitions of the experimental ilmenites and glass (quenched melt) were analyzed using solution MC-ICPMS after hand-picking. Additionally, Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements on synthetic ilmenite were conducted and compared to previous NRIXS measurements on synthetic lunar glasses in order to derive temperature-dependent equilibrium ilmenite-melt Fe isotopic fractionations. Experimentally determined ilmenite-melt fractionations were then incorporated into a lunar magma ocean crystallization model that tracks the major element and isotopic compositional evolution of lunar magma ocean cumulates and residual liquid. There is good agreement between the Fe equilibrium isotopic fractionation measured by NRIXS and the laboratory equili- bration experiments, and we find that the isotopic fractionation is sensitive to il- menite compositional differences (0 vs. 10% Fe3+). Further, the light Ti isotopic composition of ilmenite relative to the melt (∆49Ti ilmenite-melt = −0.09 ± 0.03h at 1100°C) is consistent with the higher coordination of Ti in ilmenite relative to melts and results of previous studies. The modeled Ti isotopic compositions for lunar magma ocean cumulates display Ti isotopic variability sufficient to explain the low- and high-Ti mare basalt sources. However, the difference in Fe isotopic composition between the low- and high-Ti mare basalts cannot be attributed solely to ilmenite fractionation. Instead, Fe isotopic fractionation by additional products of lunar magma ocean crystallization, such as clinopy- roxene, is required to generate the inferred Fe and Mg isotopic variability in the lunar mantle. Alternatively, the Fe and Mg isotopic compositions of the lunar mare basalts may indicate Fe-Mg interdiffusion has occurred in the Ti- rich component of the mare basalt source regions via reaction between ilmenite cumulates and the olivine- and pyroxene-rich lunar mantle.
Kelsey Prissel↗
Integrated Optimization and Control of a Hybrid Gas Turbine/sCO 2 Power System
During phase-I, the project team led by Echogen Power Systems (EPS) had two primary objectives based on investigating the application of gas turbines with supercritical carbon dioxide (sCO 2 ) power cycles. The first objective was to improve the overall efficiency and performance of a hybrid gas turbine/sCO 2 power system through a joint optimization of the two subsystems (gas turbine and sCO 2 power cycle) using non-linear optimization techniques that simultaneously evaluate thermal performance of the combined cycle. The hybrid power system included several points of interaction, including (but not limited to) gas turbine exhaust, fuel heating, inlet chilling and turbine cooling. The second objective was to establish a baseline transient response model of the hybrid power system and a notional microgrid and begin steps to integrate the control systems of the three major elements (gas turbine, sCO 2 cycle and grid controller). The project team established a baseline performance for a combined cycle power plant using a production gas turbine and scaled sCO 2 power cycle only utilizing exhaust heat recovery. Echogen’s non-linear techno-economic optimization code was extended by adding gas turbine component models derived from a in-house developed gas turbine design code. With the two cycles coupled by the gas turbine exhaust, design parameters of both cycles were allowed to vary simultaneously to determine performance opportunity versus isolated designs. Returning to the baseline gas turbine/sCO 2 power cycle transient models: Echogen had in-house developed sCO 2 cycle transient model in GT-Suite system simulation software, and had partnered with Siemens Finspång for gas turbine transient model, and Siemens PTI group to provide micro-grid load profile as well as hybrid power cycle generated load (power and frequency) analysis. The transient model for the SGT-750 Siemens gas turbine was a “black-box” functional mock-up interface (FMI) model developed by Siemens Industrial Turbomachinery in Finspång, Sweden. The SGT-750 is a twin-shaft gas turbine that produces 40 MW electricity with an efficiency of about 40% at ISO conditions. At 100% gas turbine throttle (load), the SGT-750 has average exhaust conditions of 114.6 kg/s and 469.8°C. The transient model for sCO 2 power cycle was developed by Echogen in GT-SUITE 1D system simulation software platform. The basic CO 2 flow circuit has single-shaft turbomachinery with net 11.5 MW electrical power output at design conditions. The power turbine has a double-ended shaft with one end connected to synchronous generator through a fixed-ratio gearbox. The other end of power turbine is connected to the compressor through a continuously variable transmission. The major components of the sCO 2 power cycle modeled include air cooled condenser/cooler, CO 2 compressor, recuperator, two waste heat exchanger coils, power turbine, continuous variable transmission, gearbox and generator. Integration of SGT-750 transient model and sCO 2 power cycle transient model was done in Matlab Simulink. In the integrated model, the gas turbine and sCO 2 power cycle interacted at two points, first one being the gas turbine exhaust gas flow rate and temperature, which were inputs to sCO 2 power cycle model. The second point was the distribution of micro-grid load demand signal between the SGT-750 generator and sCO 2 cycle generator. For a given combined-cycle load demand, the gas turbine load demand was equal to the total demand minus the sCO 2 cycle power generated. In the present study the integrated model was simulated for two cases of grid load demand: (i) for a step change, both positive-step and negative-step, in grid load demand (ii) for a micro-grid load demand curve provided by Siemens PTI group. Finally, the time series plots representing load demand versus integrated system response were presented including the sCO 2 power cycle control system performance plots. The actual generated power and frequency of both the generators, gas turbine and sCO 2 power cycle, was supplied to Siemens PTI group for dynamic grid assessment, results of which are provided in appendices.
03 NATURAL GAS↗
New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA
Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.
Rui, Hualan↗
From inclusive to semi-inclusive one-nucleon knockout in neutrino event generators
In neutrino event generators, for models for neutrino and electron scattering only inclusive cross sections are implemented. When these models are used to describe a semi-inclusive cross section, the event generator attaches the hadron variables based on some assumptions. In this work we compare the nucleon kinematics given by the method used in the GENIE event generator, e.g. in the implementation of the SuSAv2 model, to a fully unfactorized calculation using the relativistic distorted wave impulse approximation (RDWIA). We focus on kinematics relevant to the $e4\nu$ analysis and show that observables obtained with RDWIA differ significantly from those of the approximate method used in GENIE, the latter should be considered unrealistic.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The Evolving Role of Extreme Weather Events in the U.S. Power System with High Levels of Variable Renewable Energy
As weather-dependent renewable generation grows, it is important for power system planning to understand the broad trends and correlations between weather, renewable resources, and load. The traditional planning, performed by utilities and system operators, includes the study of system resource adequacy during peak load periods in the summer and winter to ensure the generation and transmission system is appropriate to meet load. But in a power grid with a high penetration of variable renewable energy (i.e., wind and solar), periods of high risk to system resource adequacy may no longer correspond only to hours of peak load. In particular, high shares of variable renewable energy, even when well-forecasted to inform system operations, can further complicate the stress extreme weather events already place on the grid. They also may lead to changes to the types of weather conditions that are most problematic to system operations and resource adequacy due to widespread and extended deficits of wind and solar generation. Accordingly, the focus of reliability assessments in long-term planning studies may need to evolve in the coming years to more fully incorporate weather events that lead to these deficits. This report seeks to identify these new weather events and understand the characteristics of the events that lead to system risk of future systems with higher penetrations variable renewable energy.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield
This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.
54 ENVIRONMENTAL SCIENCES↗
Hydropower Flexibility and Environmental Tradeoffs Analysis
The importance of hydropower increases as the power grid evolves with the higher variable renewable contribution. As conventional thermal power plants are retired, the importance of hydropower contribution increases to balance the variability of solar and wind generation. However, reservoir water resources are constrained by multiple constraints, and variability of water inflow to the reservoirs creates limitations to dam water releases for power grid needs. Coordinating multiple tools, including water resources, ecological, and technical and economic power grid modeling, informs dam water releases. The case study, the Columbia River Basin multipurpose reservoir project, is operated for hydropower production and many other purposes considering the aquatic habitat of the river basin. Specifically, the river basin fish population is a vital element for the tribal community of the river basin. We integrated a production cost model, a water resource model, and decades of tribal knowledge to analyze the fish-friendly way of operating Columbia hydropower scheduling and grid impacts. We measure power grid impacts for various water resources planning scenarios in terms of total system operating cost, system reliability indicators, changes in wind and solar generation and curtailments, local marginal prices, and revenue for hydropower producers. The study results inform reservoir operating rules decisions from hydropower power producers, system operators, other water users, tribes, environmentalists, and other stakeholders.
Columbia River↗
An Optimization-Based Approach to Injector Element Design
An injector optimization methodology, method i, is used to investigate optimal design points for gaseous oxygen/gaseous hydrogen (GO2/GH2) injector elements. A swirl coaxial element and an unlike impinging element (a fuel-oxidizer-fuel triplet) are used to facilitate the study. The elements are optimized in terms of design variables such as fuel pressure drop, APf, oxidizer pressure drop, deltaP(sub f), combustor length, L(sub comb), and full cone swirl angle, theta, (for the swirl element) or impingement half-angle, alpha, (for the impinging element) at a given mixture ratio and chamber pressure. Dependent variables such as energy release efficiency, ERE, wall heat flux, Q(sub w), injector heat flux, Q(sub inj), relative combustor weight, W(sub rel), and relative injector cost, C(sub rel), are calculated and then correlated with the design variables. An empirical design methodology is used to generate these responses for both element types. Method i is then used to generate response surfaces for each dependent variable for both types of elements. Desirability functions based on dependent variable constraints are created and used to facilitate development of composite response surfaces representing the five dependent variables in terms of the input variables. Three examples illustrating the utility and flexibility of method i are discussed in detail for each element type. First, joint response surfaces are constructed by sequentially adding dependent variables. Optimum designs are identified after addition of each variable and the effect each variable has on the element design is illustrated. This stepwise demonstration also highlights the importance of including variables such as weight and cost early in the design process. Secondly, using the composite response surface that includes all five dependent variables, unequal weights are assigned to emphasize certain variables relative to others. Here, method i is used to enable objective trade studies on design issues such as component life and thrust to weight ratio. Finally, combining results from both elements to simulate a trade study, thrust-to-weight trends are illustrated and examined in detail.
Tucker, P. Kevin↗
A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools
A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.
Precipitation↗
Energy, economic, and environmental tradeoffs at run-of-river hydropower facilities
Hydropower’s ability to quickly adapt to variability from wind and solar generation by fluctuation flow rates can allow the electricity grid to integrate more renewable capacity. However, these rapid flow fluctuations, required to meet variability needs, can negatively impact aquatic ecosystems. In this study, we quantified energy-economic-environment tradeoffs at five conventional hydropower facilities (i.e. hydropower produced ad a dam on a river channel) across the United States to identify a mix of operational regimes that can provide flexibility to support variable renewable energy integration and environmental protections. Model results show a range of ability to meet demand from 4.7% to 97.8% depending on which case study is considered. Additionally, when modeling the case study facilities on a range of RoR conditions, allowing a % of inflow as discharge, we found the range of 140–200% of inflow allowed as discharge lead to lowest environmental impact while meeting the highest amount of demand. Our sensitivity analysis results demonstrated the Richard-Baker Flashiness Index, used to measure flowrate changes, and Revenue, were negatively correlated with the percent of hydropower generation within the defined Regional Energy Deployment System balancing area (i.e. region in which energy demand and energy supply is balanced based on the Regional Energy Deployment System model) yet positively correlated to the variable renewable energy generation percentage in the defined balancing area. In conclusion, our results suggest hydropower operations can aid in increasing renewable energy generation while limiting environmental impacts when considering a holistic analysis of energy-economic-environment tradeoffs.
Economic impact↗
Hydropower Role in a Power Grid with a High Proportion of Variable Renewables
The importance of hydropower increases as the power grid evolves with the higher variable renewable contribution. As conventional thermal power plants are retired, the importance of hydropower contribution increases to balance the variability of solar and wind generation. However, reservoir water resources are constrained by multiple constraints, and variability of water inflow to the reservoirs creates limitations to dam water releases for power grid needs. Coordination of multiple tools, including water resources modeling, ecological modeling, and technical and economic power grid modeling, informs dam water releases. Better representation of ecological constraints and other water use details in the suit of power grid models are required to identify the hydropower flexibility to provide multiple power grid services. Impacts of water planning and hydropower operation changes to a power grid are better understood by production cost model simulations with technical and economic data, including unserved energy, reserve failures, transmission congestion to serve load, and local marginal price increases. Similarly, PCM simulation of current and future projected power grid information, including LMP, and carbon emission rates, informs short-term water release plans and long-term investments for power plant upgrades for technical and ecological needs. Hydropower operations of several case studies are simulated to understand operation patterns, revenue, water release decisions in multiple time scales (month, day, minutes), hydropower contracts scheduling, and water variability impacts.
environmental↗
Chapter 9: Impact of Variable Renewable Energy Sources on Bulk Power System Planning and Operations
Wind and solar photovoltaics (PV) have experienced remarkable growth in recent years, with many consequent benefits within and outside of power systems. At the same time, wind and solar PV have unique characteristics relative to the historically dominant dispatchable technologies like coal, gas, and nuclear power plants that have required and will continue to require changes in power system planning and operations. This chapter discusses planning and operational challenges of integrating wind and solar PV into bulk power systems. We first present the key characteristics of wind and solar PV that differentiate it from conventional technologies, such as variable and uncertain electricity generation, asynchronous interconnection to the power system, and near-zero marginal costs. We then link these characteristics to power system planning and operational challenges at low through high wind and solar penetrations. Finally, we discuss near- and long-term solutions to those challenges, such as diversifying the generation mix and wind and solar fleets, improving system flexibility, diversifying ancillary service products, and integrating generation and transmission planning.
bulk power system↗
An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures
We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method’s precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE’s encoder and decoder networks capture the localization process and the source’s influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbor approach based on the closest latent representation of a source. Validation with pencil-lead-break tests on a metallic pipe confirmed our method’s effectiveness, achieving an averaged 3D localization accuracy of 0.84.
Lee, Guan-Wei↗
Visualization of computer-generated flow fields
Flow field variables are visualized using color representations described on surfaces that are interpolated from computational grids and transformed to digital images. The color at a point on a surface represents the magnitude of a variable, and several surfaces can be included in a single digital image. Typically the surfaces are a boundary surface, a windward surface, and a crossflow surface. Sequences of images, in pictorial form, are presented to describe an entire flow field or a time history of a flow field. Several examples are presented.
Smith, R.↗