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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Decision Tree Regression to Identify Representative Road Sections for Evaluating Performance of Connected and Automated Class 8 Tractors

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified. High-level metrics and detailed information of the derived road sections are also illustrated and discussed, which demonstrate their representativeness and the effectiveness of the approach. Meanwhile, the capability of this approach can be easily extended by integrating specific constraints into the DTR algorithm. As an example, a specific representative road section with an aggressive road grade profile was also provided via this approach.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Utilizing waste heat in wastewater treatment plants for water desalination: Modeling and Multi-Objective optimization of a Multi-Effect desalination system using Decision Tree Regression and Pelican optimization algorithm

This paper examines the feasibility of using waste heat from wastewater treatment plants (WWTPs) for water desalination. A model was developed to utilize waste heat from the gensets at As Samra WWTP in Jordan, using real data and TRNSYS® software to calculate available waste heat. The desalination process was then modeled with ASPEN PLUS® software, focusing on multi-effect desalination (MED). Both series and parallel configurations for the MED system were compared. The study investigated the effects of system feeding flow rate, feeding pressure, and heat input on productivity, performance ratio, and recovery ratio. The study also introduces a novel optimization technique combining machine learning and modern optimization algorithms to maximize system productivity and performance. Initially, a decision tree regression (DTR) model is developed to establish relationships between key independent variables (flow rate, feed pressure, and heat input) and dependent variables (productivity, performance ratio, and recovery ratio). The Pelican Optimization Algorithm (POA) is then used to identify the optimal values of the independent variables for maximum productivity and performance. The results show that using a series configuration yields a system productivity of 3984.2 kg/hr, a performance ratio of 3.78, and a recovery ratio of 0.991 at a feed flow rate of 4000 kg/hr, feed pressure of 3 bars, and heat input of 719 kW. Optimal productivity (4421 kg/hr), performance ratio (3.81), and recovery ratio (0.851) are achieved at a feed flow rate of 5166 kg/hr, feed pressure of 3.2 bars, and heat input of 794 kW. In conclusion, the techno-economic assessment indicates a levelized cost of water of 1.63 USD/m 3 for parallel configurations and 1.65 USD/m 3 for series configurations, with a payback period of less than two years.

42 ENGINEERING↗
DOE OSTI2019

Machine Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions

Machine learning feature selection analyses were conducted to understand the impact of various weather attributes on power electricity demand and its components in the northwest United States. Load profiles data were obtained from the Northwest Energy Efficiency Alliance (NEEA) and processed to yield hourly load profiles with the same temporal resolution as the weather conditions data obtained from the National Oceanic and Atmospheric Administration (NOAA) for representative weather stations. The data were divided into five seasons; for each season, for each load component, the influences of weather factors were evaluated and quantified using cross-correlation, principal component analysis, and mutual information evaluation; then predictive models were developed based on the ranked/screened factors using the regression tree (RT) approach. After multi-fold cross-validation, the optimal complexity/depth of the regress tree models is found to vary for different load components, and the prediction accuracy using RT and weather data can be higher than 80% for heating and refrigeration, or lower than 30% for vent and cooking. The study provides guidance on improvement of load profile generation, or even serves as a standalone predictive tool for approximating load profiles in balance authorities or climate zones where load profile data is absent.

load composite, load profiling, NEEA, mutual infor↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
NASA NTRS2010

Exploiting the Free Landsat Archive for Operational Monitoring of Ecosystem Condition and Change Across the Chesapeake Bay Watershed

For the first time, all imagery acquired by the Landsat series of satellites is being made available by the USGS to users at no cost. This represents a key opportunity to use Landsat in a truly operational monitoring framework: large regions of the U.S. such as the Chesapeake Bay Watershed can now be analyzed using "wall-to-wall" imagery at timescales from approximately 1 month to several years. With the future launch of the Landsat Data Continuity Mission (LDCM) and Decadal Survey missions such as the hyperspectral HyspIRI, it is imperative to develop robust processing systems to perform annual ecosystem assessments over large regions such as the Chesapeake Bay. We have been working at NASA's Goddard Space Flight Center (GSFC) to develop an integrative framework for inserting 30m, annual, Landsat based data and derived products into the existing decision support system for the Bay, with a particular focus on ecosystem condition and changes over the entire watershed. The basic goal is to use a 'stack' of Landsat imagery with 40% or less cloud cover to produce multi-date (2005-2009 period), cloud/shadow/gap-free composited surface reflectance products that will support the creation of watershed scale land cover/ use products and the monitoring of ecosystem change across the Bay. Our scientific focus extends beyond the conventional definition of land cover (i.e. a classification of vegetation type) as we propose to monitor both changes in surface type (e.g. forest to urban), vegetation structure (e.g. forest disturbance due to logging or insect damage), as well as winter crop cover. These processes represent a continuum from large, interannual changes in land cover type, to subtler, intra-annual changes associated with short-term disturbance. The free Landsat data are being processed to surface reflectance and composited using the existing Landsat Ecosystem Disturbance Adaptive Processing System here at NASA/ GSFC, and land cover products (type, tree cover, impervious cover, winter cover) are being produced using well-established decision tree and regression tree algorithms. The goal of this session is to present the data products that we have been developing to the Bay science community and to discuss potential avenues for improvements and usage of the products for decision support.

BrowndeColstoun, Eric↗
DOE OSTI2013

Using Machine Learning to Create Turbine Performance Models (Presentation)

Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.

17 WIND ENERGY↗
NASA NTRS2013

Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for

ozone↗
NASA NTRS2017

Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Machine Learning Model.↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A machine learning approach to fracture mechanics problems

Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. Here this example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Modeling Fire Severity in Black Spruce Stands in the Alaskan Boreal Forest Using Spectral and Non-Spectral Geospatial Data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R2 of the organic layer depth reduction models was 0.60 and 0.55 (pb0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R2 to 0.80 and 0.79 (pb0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (N200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.

Barrett, K.↗
DOE OSTI2025

Efficient Decision Trees for Tensor Regressions

Here, we proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output regression tree models whose input variables are tensors (i.e., multi-way arrays). We devised and implemented fast randomized and deterministic algorithms for efficient fitting of scalar-on-tensor trees, making TT competitive against tensor-input GP models (Yu, Li, and Liu; Sun et al.). Based on scalar-on-tensor tree models, we extend our method to tensor-on-tensor problems using additive tree ensemble approaches. Theoretical justification and extensive experiments, including testing robustness to entrywise input tensor noise, are provided on real and synthetic datasets to illustrate the performance of TT. Our implementation is provided at https://github.com/hrluo/TensorDecisionTreeRegressor. Supplementary materials for this article are available online.

Decision tree regressions↗
DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗
DOE OSTI2019

Predicting Storm Outages Through New Representations of Weather and Vegetation

This paper presents new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results demonstrate that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to the phenology, solar-view geometry, and atmospheric condition etc. factors but not actual landcover difference. Finally, we will compare the classification results from screened and unscreened training samples to assess the improvement achieved by cleaning up the training samples. Keywords:

Tan, Bin↗
DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗