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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Techno-Economic Analysis of High-Pressure Metal Hydride Compression Systems

Traditional high-pressure mechanical compressors account for over half of the car station’s cost, have insufficient reliability, and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100–875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on minichannel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti0.97Zr0.03)1.1Cr1.6Mn0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 °C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper.

42 ENGINEERING↗
DOE OSTI2015

Large area strain analysis using scanning transmission electron microscopy across multiple images

Here, we apply revolving scanning transmission electron microscopy to measure lattice strain across a sample using a single reference area. To do so, we remove image distortion introduced by sample drift, which usually restricts strain analysis to a single image. Overcoming this challenge, we show that it is possible to use strain reference areas elsewhere in the sample, thereby enabling reliable strain mapping across large areas. As a prototypical example, we determine the strain present within the microstructure of a Ni-based superalloy directly from atom column positions as well as geometric phase analysis. While maintaining atomic resolution, we quantify strain within nanoscale regions and demonstrate that large, unit-cell level strain fluctuations are present within the intermetallic phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

A gLite FTS based solution for managing user output in CMS

The CMS distributed data analysis workflow assumes that jobs run in a different location from where their results are finally stored. Typically the user output must be transferred across the network from one site to another, possibly on a different continent or over links not necessarily validated for high bandwidth/high reliability transfer. This step is named stage-out and in CMS was originally implemented as a synchronous step of the analysis job execution. However, our experience showed the weakness of this approach both in terms of low total job execution efficiency and failure rates, wasting precious CPU resources. The nature of analysis data makes it inappropriate to use PhEDEx, the core data placement system for CMS. As part of the new generation of CMS Workload Management tools, the Asynchronous Stage-Out system (AsyncStageOut) has been developed to enable third party copy of the user output. The AsyncStageOut component manages glite FTS transfers of data from the temporary store at the site where the job ran to the final location of the data on behalf of that data owner. The tool uses python daemons, built using the WMCore framework, and CouchDB, to manage the queue of work and FTS transfers. CouchDB also provides the platform for a dedicated operations monitoring system. In this paper, we present the motivations of the asynchronous stage-out system. We give an insight into the design and the implementation of key features, describing how it is coupled with the CMS workload management system. Finally, we show the results and the commissioning experience.

Cinquilli, M.↗
DOE OSTI2018

Energy Storage Data Submission Guidelines

In order to consider and understand emerging energy storage technologies, data analysis can be executed to ascertain proper operation and performance. The technical benefits of rigorous testing and data analysis are important for the customer, the planner, developer, and system operator: the end-user has a safe, reliable system that performs predictably on a macro level. The test-and-analyze approach to verifying performance of energy storage devices, equipment, and systems integration into the grid improves the understanding of the value of energy storage over time from the economic vantage point. Demonstrating the lifecycle value of energy storage begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some ESS data is inconsistent and incomplete - thus leading to a question of the inefficacy of the data when it comes time to analyze it. This paper will review and propose general guidelines such as sampling rates and data points that providers must supply in order for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol to (a) reduce time it takes data to reach those who are providing analyses; (b) allow them to better understand the energy storage installations; and (c) provide high quality analysis of the installation. This paper intends to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this paper will be updated to remain current.

25 ENERGY STORAGE↗
DOE OSTI2023

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Market analysis for the integration of new power technologies: A case study of the deployment of hybrid fossil-based generator plus energy storage (ES-FE)

This study examines the national landscape of hybridized fossil energy (FE) power plants with energy storage (ES) technologies (“ES-FE”) and presents the compilation of an ES-FE dataset, which includes over 65 ES-FE projects and concepts in the United States, comprising approximately 500 MWh of co-located ES capacity with FE power plants. This study also estimates the economic feasibility of adding ES to existing FE power plants by characterizing the potential revenues that can be generated by the ES component through flexibility and capacity value. The analysis focuses on ES technologies with 2- to 10-h. durations located in four U.S. independent system operators (ISOs): Midcontinent ISO (MISO), Electric Reliability Council of Texas (ERCOT), PJM Interconnection (PJM), and California ISO (CAISO), which have +70,000 MW of combined FE power capacity that could add ES. Annual revenues are estimated for the ES component using a what-if-analysis approach, for capacity value, price arbitrage, or ancillary services provision. The results show that annual revenues depend on the end-use storage service, wholesale electricity and capacity market prices, and ES technology operation parameters such as discharging duration and cycling frequency. When performing a sensitivity analysis, ES accrues $7–178/kW-yr. via price arbitrage and ancillary services provision in the four ISOs, and $13–92/kW-yr. when providing capacity value only in MISO and PJM. A cash flow analysis is performed to estimate the net present value (NPV) of the ES addition using a range of ES costs. The study finds that for most ES technologies considered, these revenues alone are insufficient to achieve economic feasibility. In conclusion, of the 1645 total runs analyzed, 115 had positive NPVs (7%). Therefore, other revenue streams or monetizable benefits are necessary to achieve the break-even point.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2014

SU-E-T-253: Open-Source Automatic Software for Quantifying Biological Assays of Radiation Effects

Purpose: Clonogenic cell survival is a common assay for quantifying the effect of drugs and radiation. Manual counting of surviving colonies can take 30–90seconds per plate, a major limitation for large studies. Currently available automatic counting tools are not easily modified for radiation oncology research. Our goal is to provide an open-source toolkit for precise, accurate and fast analysis of biological assays in radiation oncology. Methods: As an example analysis, we used HeLa cells incubated with gadolinium nanoparticles prior to irradiation. After treatment, the cells are grown for 14days to allow for colony formation. To analyze the colony growth, we capture images of each dish for archiving and automatic computer-based analysis. A FujifilmX20 camera is placed at the top of a box setup, 20cm above the sample, which is backlit by a LED lamp placed at the bottom of the box. We use a Gaussian filter (width=1.3mm) and color threshold (19–255). The minimum size for a colony to be counted is 1mm. For this example, 20 dishes with a large range of colonies were analyzed. Each dish was counted 3 times manually by 3 different users and then compared to our counter. Results: Automatic counting of cell colonies takes an average of 7seconds, enabling the analysis process to be accelerated 4–12 times. The average precision of the automatic counter was 1.7%. The Student t-test demonstrated the non-significant differences between the two counting methods (p=0.64). The ICC demonstrated the reliability of each method with ICC>0.999 (automatic) and ICC=0.95 (manual). Conclusion: We developed an open-source automatic toolkit for the analysis of biological assays in radiation oncology and demonstrated the accuracy, precision and effort savings for clonogenic cell survival quantification. This toolkit is currently being used in two laboratories for routine experimental analysis and will be made freely available on our departmental website.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Note: Artificial neural networks for the automated analysis of force map data in atomic force microscopy

Force curves recorded with the atomic force microscope on structured samples often show an irregular force versus indentation behavior. An analysis of such curves using standard contact models (e.g., the Sneddon model) would generate inaccurate Young's moduli. A critical inspection of the force curve shape is therefore necessary for estimating the reliability of the generated Young's modulus. We used a trained artificial neural network to automatically recognize curves of “good” and of “bad” quality. This is especially useful for improving the analysis of force maps that consist of a large number of force curves.

42 ENGINEERING↗
DOE OSTI2014

Karhunen-Loeve expansion analysis of uncertainties in cloud microphysical property retrievals

Here, this study proposes a methodology of quantifying uncertainties for cloud retrievals on model resolution to facilitate the comparison with model outputs. Primary component analysis is applied to reduce the dimension of random variables (up to a factor of 50) and reveal the cross correlations in the input data, making large sampling computationally feasible and uncertainty quantification accurate and reliable. Our approach has the capability of parameterizing input uncertainties and attributing the uncertainties in the retrieval output to each individual source, which allows sensitivity analysis of cloud retrieval algorithms and provides directions for improving observation instruments as well as strategies. We applied the method to characterize uncertainties in cloud ice water content (IWC) retrieved from the DOE Atmospheric Radiation Measurement (ARM) programs baseline cloud microphysical retrieval algorithm (MICROBASE). We test it with a selected ice cloud case observed on 9 March 2000 at the ARM Southern Great Plains site during its 2000 cloud intensive observing period. The test results indicate that (1) uncertainties in the output retrieved by MICROBASE are comparable amongst different retrievals; (2) The mean values obtained by our UQ method are closer to the aircraft data with less errors compared to the direct ensemble average; (3) Ice water path (IWP) generally incurred larger uncertainty in optically thin ice clouds and there was more variability in vertical in the retrieved IWC; and (4) Uncertainties in the output are mainly due to the interactions among different modes of ARM radar pro files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). Here, we also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 10 4 km 2 ) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 ×10 4 km 2 ). However the uncertainty (1 to 128 × 10 4 km 2 ) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The dark energy survey 5-yr photometrically identified type Ia supernovae

ABSTRACT As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

Abstract Background A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. Results We present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. Conclusions Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Failure Analysis of Solder Joints

Soldering is a critical filler material joining process found in nearly all modern electronics. Ensuring solder joint reliability is crucial for the safety, performance, and cost-effectiveness of microelectronics.

42 ENGINEERING↗
DOE OSTI2015

Integrated Energy-Water Planning in the Western and Texas Interconnections

While long-term regional electricity transmission planning has traditionally focused on cost, infrastructure utilization, environmental impact, and reliability, the availability of water is an emerging issue. Toward this growing need, thermoelectric expansion should consider competing demands from other water use sectors balanced with fresh and non-traditional water supplies subject to climate variability. To address this need the Department of Energy's Office of Electricity Delivery and Energy Reliability supported an integrated planning project with funding through the American Reinvestment and Recovery Act (2009). Specifically, an integrated energy-water analysis was performed to support transmission system planners in the Western and Texas Interconnections to explore the potential implications of water availability and cost for long-term transmission planning. The project brought together electric transmission planners (e.g., Western Electricity Coordinating Council (WECC) and the Electric Reliability Council of Texas (ERCOT)) with western water planners (e.g., Western Governors' Association and the Western States Water Council). Efforts were organized into ten specific tasks: (1) project coordination and outreach; (2) thermoelectric water use; (3) non-thermoelectric water use; (4) water availability; (5) water cost; (6) environmental risk; (7) climate variability; (8) energy for water; (9) decision support system interface; and, (10) transmission planning support. Major accomplishments associated with this effort include: For the first time water availability was used to inform generation expansion planning by WECC and ERCOT. For the first time, projections of intensifying drought and its effect on reservoir levels, and thermal effluent discharge permitting were used to inform operational and expansion planning by ERCOT. Water withdrawal and consumption were characterized for each power plant in the WECC and ERCOT service areas/regions. Water use factors were also developed for a range of unit processes that allowed projection of future water demands related to electric generation expansion planning. Working with state water managers current and future water use (withdrawal and consumption) were projected throughout the Western United States at an 8-digit Hydraulic Unit Code (HUC-8) level (over 1200 watersheds). In a similar fashion water availability and cost were mapped across the Western United States. Considered were five different sources of water: unappropriated surface water, unappropriated groundwater, appropriated water, municipal wastewater and brackish groundwater. Water basins (at the HUC-8 level) were mapped across the Western United States with regard to their potential for conflicts between aquatic and riparian species and habitats listed under the Endangered Species Act and water availability for future energy development. Water planners were engaged through the Western States Water Council and thus reflects their membership of the 17 contiguous western states (i.e., Texas up through the Dakotas and West). Power plants at greatest risk to the impacts of drought were identified. The analysis considered the hazards of low flows, insufficient reservoir storage, and elevated water temperatures under intensifying drought conditions projected for the future. The electricity used to provide water-related services was mapped at a county level throughout the Western U.S. Considered was the electricity required for interbasin conveyance, agricultural pumping, drinking water and wastewater services. To communicate our results the project has produced 6 journal articles, 1 book chapter, 11 reports, and 47 presentations at related conferences.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Transmission and Alternatives Analysis and Strategic Stakeholder Engagement

Over the life of this 3-year agreement NREL has developed data, tools, and analyses for utilities to integrate high levels of solar, and demonstrated reliability results to produce a reduction in cost and lower overall risk for stakeholders. There were five projects that made up the Agreement 25805 portfolio, including: 1. Phase 3 of the Western Wind and Solar Integration Study which studied WECC large-scale stability and frequency response. 2. Transient Solar Model Development which developed a generic solar model for cycle-by-cycle analysis. 3. Utility Scale PV Plant Demo which demonstrated control capability on a demo plant. 4. Targeted Transmission Support which contributed to transmission planning with reinforcements 5. Strategic Stakeholder Engagement which communicated and disseminated latest research with utility and industry stakeholders so that discussions and decision-making can be informed by the most current and reliable information available.

14 SOLAR ENERGY↗
DOE OSTI2014

Establishing a process of irradiating small animal brain using a CyberKnife and a microCT scanner

Purpose: Establish and validate a process of accurately irradiating small animals using the CyberKnife G4 System (version 8.5) with treatment plans designed to irradiate a hemisphere of a mouse brain based on microCT scanner images. Methods: These experiments consisted of four parts: (1) building a mouse phantom for intensity modulated radiotherapy (IMRT) quality assurance (QA), (2) proving usability of a microCT for treatment planning, (3) fabricating a small animal positioning system for use with the CyberKnife's image guided radiotherapy (IGRT) system, and (4)in vivo verification of targeting accuracy. A set of solid water mouse phantoms was designed and fabricated, with radiochromic films (RCF) positioned in selected planes to measure delivered doses. After down-sampling for treatment planning compatibility, a CT image set of a phantom was imported into the CyberKnife treatment planning system—MultiPlan (ver. 3.5.2). A 0.5 cm diameter sphere was contoured within the phantom to represent a hemispherical section of a mouse brain. A nude mouse was scanned in an alpha cradle using a microCT scanner (cone-beam, 157 × 149 pixels slices, 0.2 mm longitudinal slice thickness). Based on the results of our positional accuracy study, a planning treatment volume (PTV) was created. A stereotactic body mold of the mouse was “printed” using a 3D printer laying UV curable acrylic plastic. Printer instructions were based on exported contours of the mouse's skin. Positional reproducibility in the mold was checked by measuring ten CT scans. To verify accurate dose delivery in vivo, six mice were irradiated in the mold with a 4 mm target contour and a 2 mm PTV margin to 3 Gy and sacrificed within 20 min to avoid DNA repair. The brain was sliced and stained for analysis. Results: For the IMRT QA using a set of phantoms, the planned dose (6 Gy to the calculation point) was compared to the delivered dose measured via film and analyzed using Gamma analysis (3% and 3 mm). A passing rate of 99% was measured in areas of above 40% of the prescription dose. The final inverse treatment plan was comprised of 43 beams ranging from 5 to 12.5 mm in diameter (2.5 mm size increments are available up to 15 mm in diameter collimation). Using the Xsight Spine Tracking module, the CyberKnife system could not reliably identify and track the tiny mouse spine; however, the CyberKnife system could identify and track the fiducial markers on the 3D mold.In vivo positional accuracy analysis using the 3D mold generated a mean error of 1.41 mm ± 0.73 mm when fiducial markers were used for position tracking. Analysis of the dissected brain confirmed the ability to target the correct brain volume. Conclusions: With the use of a stereotactic body mold with fiducial markers, microCT imaging, and resolution down-sampling, the CyberKnife system can successfully perform small-animal radiotherapy studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Measuring the Kerr spin parameter of a non-Kerr compact object with the continuum-fitting and the iron line methods

Under the assumption that astrophysical black hole candidates are the Kerr black holes of general relativity, the continuum-fitting method and the analysis of the Kα iron line are today the only available techniques capable of providing a relatively reliable estimate of the spin parameter of these objects. If we relax the Kerr black hole hypothesis and we try to test the nature of black hole candidates, we find that there is a strong correlation between the measurement of the spin and possible deviations from the Kerr solution. The properties of the radiation emitted in a Kerr spacetime with spin parameter a{sub *} are indeed very similar, and practically indistinguishable, from the ones of the radiation emitted around a non-Kerr object with different spin. In this paper, I address the question whether measuring the Kerr spin with both the continuum-fitting method and the Kα iron line analysis of the same object can be used to claim the Kerr nature of the black hole candidate in the case of consistent results. In this work, I consider two non-Kerr metrics and it seems that the answer does depend on the specific background. The two techniques may either provide a very similar result (the case of the Bardeen metric) or show a discrepancy (Johannsen-Psaltis background)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗