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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 OSTI2014

SU-F-BRD-15: The Impact of Dose Calculation Algorithm and Hounsfield Units Conversion Tables On Plan Dosimetry for Lung SBRT

Purpose: To assess dosimetric differences in IMRT lung stereotactic body radiotherapy (SBRT) plans calculated with Varian AAA and Acuros (AXB) and with vendor-supplied (V) versus in-house (IH) measured Hounsfield units (HU) to mass and HU to electron density conversion tables. Methods: In-house conversion tables were measured using Gammex 472 density-plug phantom. IMRT plans (6 MV, Varian TrueBeam, 6–9 coplanar fields) meeting departmental coverage and normal tissue constraints were retrospectively generated for 10 lung SBRT cases using Eclipse Vn 10.0.28 AAA with in-house tables (AAA/IH). Using these monitor units and MLC sequences, plans were recalculated with AAA and vendor tables (AAA/V) and with AXB with both tables (AXB/IH and AXB/V). Ratios to corresponding AAA/IH values were calculated for PTV D95, D01, D99, mean-dose, total and ipsilateral lung V20 and chestwall V30. Statistical significance of differences was judged by Wilcoxon Signed Rank Test (p<0.05). Results: For HU<−400 the vendor HU-mass density table was notably below the IH table. PTV D95 ratios to AAA/IH, averaged over all patients, are 0.963±0.073 (p=0.508), 0.914±0.126 (p=0.011), and 0.998±0.001 (p=0.005) for AXB/IH, AXB/V and AAA/V respectively. Total lung V20 ratios are 1.006±0.046 (p=0.386), 0.975±0.080 (p=0.514) and 0.998±0.002 (p=0.007); ipsilateral lung V20 ratios are 1.008±0.041(p=0.284), 0.977±0.076 (p=0.443), and 0.998±0.018 (p=0.005) for AXB/IH, AXB/V and AAA/V respectively. In 7 cases, ratios to AAA/IH were within ± 5% for all indices studied. For 3 cases characterized by very low lung density and small PTV (19.99±8.09 c.c.), PTV D95 ratio for AXB/V ranged from 67.4% to 85.9%, AXB/IH D95 ratio ranged from 81.6% to 93.4%; there were large differences in other studied indices. Conclusion: For AXB users, careful attention to HU conversion tables is important, as they can significantly impact AXB (but not AAA) lung SBRT plans. Algorithm selection is also important for small targets in low density lung.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-24: Feasibility of Magnetic Susceptibility to Relative Electron Density Conversion Method for Radiation Therapy

Purpose: The aim of this study is to develop radiation treatment planning using magnetic susceptibility obtained from quantitative susceptibility mapping (QSM) via MR imaging. This study demonstrates the feasibility of a method for generating a substitute for a CT image from an MRI. Methods: The head of a healthy volunteer was scanned using a CT scanner and a 3.0 T MRI scanner. The CT imaging was performed with a slice thickness of 2.5 mm at 80 and 120 kV (dual-energy scan). These CT images were converted to relative electron density (rED) using the CT-rED conversion table generated by a previous dual-energy CT scan. The CT-rED conversion table was generated using the conversion of the energy-subtracted CT number to rED via a single linear relationship. One T2 star-weighted 3D gradient echo-based sequence with four different echo times images was acquired using the MRI scanner. These T2 star-weighted images were used to estimate the phase data. To estimate the local field map, a Laplacian unwrapping of the phase and background field removal algorithm were implemented to process phase data. To generate a magnetic susceptibility map from the local field map, we used morphology enabled dipole inversion method. The rED map was resampled to the same resolution as magnetic susceptibility, and the magnetic susceptibility-rED conversion table was obtained via voxel-by-voxel mapping between the magnetic susceptibility and rED maps. Results: A correlation between magnetic susceptibility and rED is not observed through our method. Conclusion: Our results show that the correlation between magnetic susceptibility and rED is not observed. As the next step, we assume that the voxel of the magnetic susceptibility map comprises two materials, such as water (0 ppm) and bone (-2.2 ppm) or water and marrow (0.81ppm). The elements of each voxel were estimated from the ratio of the two materials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-72: Dosimetric Study of Cone-Beam CT-Based Radiation Treatment Planning Using a Patient-Specific Stepwise CT-Density Table

Purpose: To assess dose calculation accuracy of cone-beam CT (CBCT) based treatment plans using a patient-specific stepwise CT-density conversion table in comparison to conventional CT-based treatment plans. Methods: Unlike CT-based treatment planning which use fixed CT-density table, this study used patient-specific CT-density table to minimize the errors in reconstructed mass densities due to the effects of CBCT Hounsfield unit (HU) uncertainties. The patient-specific CT-density table was a stepwise function which maps HUs to only 6 classes of materials with different mass densities: air (0.00121g/cm3), lung (0.26g/cm3), adipose (0.95g/cm3), tissue (1.05 g/cm3), cartilage/bone (1.6g/cm3), and other (3g/cm3). HU thresholds to define different materials were adjusted for each CBCT via best match with the known tissue types in these images. Dose distributions were compared between CT-based plans and CBCT-based plans (IMRT/VMAT) for four types of treatment sites: head and neck (HN), lung, pancreas, and pelvis. For dosimetric comparison, PTV mean dose in both plans were compared. A gamma analysis was also performed to directly compare dosimetry in the two plans. Results: Compared to CT-based plans, the differences for PTV mean dose were 0.1% for pelvis, 1.1% for pancreas, 1.8% for lung, and −2.5% for HN in CBCT-based plans. The gamma passing rate was 99.8% for pelvis, 99.6% for pancreas, and 99.3% for lung with 3%/3mm criteria, and 80.5% for head and neck with 5%/3mm criteria. Different dosimetry accuracy level was observed: 1% for pelvis, 3% for lung and pancreas, and 5% for head and neck. Conclusion: By converting CBCT data to 6 classes of materials for dose calculation, 3% of dose calculation accuracy can be achieved for anatomical sites studied here, except HN which had a 5% accuracy. CBCT-based treatment planning using a patient-specific stepwise CT-density table can facilitate the evaluation of dosimetry changes resulting from variation in patient anatomy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-CAMPUS-J-06: The Impact of CT-Scan Energy On Range Uncertainty in Proton Therapy Planning

Purpose: To investigate the impact of tube potential (kVp) on the CTnumber (HU) to proton stopping power ratio (PSPR) conversion table; the range uncertainty and the dosimetric change introduced by a mismatch in kVp between the CT and the HU to PSPR table used to calculate dose are analyzed. Methods: A CIRS CT-ED phantom was scanned with a Philips Brilliance 64-slice scanner under 90kVp and 120kVp tube potentials. Two HU to PSPR curves were then created. Using Eclipse (Varian) a treatment plan was created for a single beam in a water phantom (HU=0) passing through a wedge-shaped heterogeneity (HU=1488). The dose was recalculated by changing only the HU to PSPR table used in the dose calculation. The change in range (the distal 90% isodose line) relative to a distal structure was recorded as a function of heterogeneity thickness in the beam. To show the dosimetric impact of a mismatch in kVp between the CT and the HU to PSPR table, we repeated this procedure using a clinical plan comparing DVH data. Results: The HU to PSPR tables diverge for low-density bone and higher density structures. In the phantom plan, the divergence of the tables results in a change in range of ~1mm per cm of bone in the beam path for the HU used. For the clinical plan, a mismatch in kVp showed a 28% increase in mean dose to the brainstem along with a 10% increase in maximum dose to the brainstem center. Conclusion: A mismatch in kVp between the CT and the HU to PSPR table can introduce significant uncertainty in the proton beam range. For dense bone, the measured range uncertainty is about 1mm per cm of bone in the beam. CT-scan energy verification should be employed, particularly when high-density media is in the proton beam path.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-136: Breath Hold Lung Phantom Study in Using CT Density Versus Relative Stopping Power Ratio for Proton Pencil Beam Scanning System

Purpose: To evaluate mass density effects of CT conversion table and its variation in current treatment planning system of spot scanning proton beam using an IROC proton lung phantom for this study. Methods: A proton lung phantom study was acquired to Imaging and Radiation Oncology Core Houston (IROC) Quality Assurance Center. Inside the lung phantom, GAF Chromic films and couples of thermal luminescent dosimeter (TLD) capsules embedded in specified PTV and adjacent structures to monitor delivered dosage and 3D dose distribution profiles. Various material such as cork (Lung), blue water (heart), Techron HPV (ribs) and organic material of balsa wood and cork as dosimetry inserts within phantom of solid water (soft tissue). Relative stopping power (RLSP) values were provided. Our treatment planning system (TPS) doesn’t require SP instead relative density was converted relative to water. However lung phantom was irradiated by planning with density override and the results were compared with IROC measurements. The second attempt was conducted without density override and compared with IROC’s. Results: The higher passing rate of imaging and measurement results of the lung phantom irradiation met the criteria by IROC without density override. The film at coronal plane was found to be shift due to inclined cylinder insertion. The converted CT density worked as expected to correlate relative stopping power. Conclusion: The proton lung phantom provided by IROC is a useful tool to qualify our commissioned proton pencil beam delivery with TPS within reliable confidence. The relative mass stopping power ratios of materials were converted from the relative physical density relative to water and the results were satisfied.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Thermal Degradation Investigation of Polyurethane Elastomers using Thermal Gravimetric Analysis - Gas Chromatography/Mass Spectrometry

The thermal degradation of two polyurethane elastomers was investigated via thermal gravimetric analysis coupled with gas chromatography/ma ss spectrometry. Decomposition occur s in a multi - step fashion with similar onset temperatures for both materials. Apparent activation energy plots were calculated inside Model - Free Kinetics software and utilized to construct conversion and isothermal conversion tables . These tables predicted material degradation as a function of temperature and time. Isothermal experiments were performed and found to be in good agreement with the predictions made from the Model - Free Kinetic s software package. Volatile products evolved during the multistep decomposition were captured at various times and analyzed using the coupled gas chromatography/mass spectrometry system . This analysis demonstrated strong correlation between the degradation products and known decomposition mechanisms for polyurethanes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lawrence Livermore National Laboratory Environmental Report 2021

The purposes of the Lawrence Livermore National Laboratory Environmental Report 2021 are to record Lawrence Livermore National Laboratory’s (LLNL’s) compliance with environmental standards and requirements, describe LLNL’s environmental protection and remediation programs, and present the results of environmental monitoring at the two LLNL sites—the Livermore Site and Site 300. The report is prepared for the U.S. Department of Energy (DOE) by LLNL’s Environmental Functional Area. Submittal of the report satisfies requirements under DOE Order 231.1B, “Environment, Safety and Health Reporting,” and DOE Order 458.1, “Radiation Protection of the Public and Environment.” The report is distributed electronically and is available at https://saer.llnl.gov/, the website for the LLNL annual environmental report. Previous LLNL annual environmental reports beginning with 1994 are also on the website. Some references in the electronic report text are underlined, which indicates that they are clickable links. Clicking on one of these links will open the related document, data workbook, or website. Sampling location maps throughout this report were created using ArcGIS® software by Esri. The report begins with an executive summary, which provides the purpose of the report and an overview of LLNL’s compliance and monitoring results. The first three chapters provide background information: Chapter 1 is an overview of the location, meteorology, and hydrogeology of the two LLNL sites; Chapter 2 is a summary of LLNL’s compliance with environmental regulations; and Chapter 3 is a description of LLNL’s environmental programs with an emphasis on the Environmental Management System including pollution prevention. The majority of the report covers LLNL’s environmental monitoring programs and monitoring data for 2021: effluent and ambient air monitoring and dose assessment (Chapter 4); waters, including wastewater, storm water runoff, surface water, rain, and groundwater (Chapter 5); and terrestrial, including soil, sediment, vegetation, foodstuff, ambient radiation, and special status wildlife and plants (Chapter 6). The remaining two chapters discuss LLNL’s groundwater remediation program (Chapter 7), and quality assurance for the environmental monitoring programs (Chapter 8). Complete monitoring data, which are summarized in the body of the report, are provided in Appendix A. The report uses Système International units, consistent with the federal Metric Conversion Act of 1975 and Executive Order 12770, “Metric Usage in Federal Government Programs” (1991). For ease of comparison to environmental reports issued prior to 1991, dose values and many radiological measurements are given in both metric and U.S. customary units. A conversion table is provided in the glossary. The report is the responsibility of LLNL’s Environmental Functional Area. Monitoring data were obtained through the combined efforts of the Environmental Functional Area; Environmental Restoration Department; Physical and Life Sciences Environmental Monitoring Radiological Laboratory; and the Radiation Protection Functional Area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Solvent-Producing Clostridia Revisited

The review provides an overview of the current status of the solvent-producing clostridia. The origin and development of industrial clostridial species, as well as the history of the industrial Acetone Butanol Ethanol fermentation process, is reexamined, and the recent resurgence of interest in the production of biobutanol is reviewed. Over 300 fully sequenced genomes for solvent-producing and closely related clostridial species are currently available in public databases. These include 270 genomes sourced from the David Jones culture collection. These genomes were allocated arbitrary DJ codes, and a conversion table to identify the species and strains has now been provided. The expanded genomic database facilitated new comparative genomic and phylogenetic analysis. A synopsis of the common features, molecular taxonomy, and phylogeny of solvent-producing clostridia and the application of comparative phylogenomics are evaluated. A survey and analysis of resident prophages in solvent-producing clostridia are discussed, and the discovery, occurrence, and role of novel R-type tailocins are reported. Prophage genomes with R-type tailocin-like features were detected in all 12 species investigated. The widespread occurrence of tailocins in Gram-negative species is well documented; this survey has indicated that they may also be widespread in clostridia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Obtaining correct compile results by absorbing mismatches between data types representations

Methods and a system are provided. A method includes implementing a function, which a compiler for a first language does not have, using a compiler for a second language. The implementing step includes generating, by the compiler for the first language, a first abstract syntax tree. The implementing step further includes converting, by a converter, the first abstract syntax tree to a second abstract syntax tree of the compiler for the second language using a conversion table from data representation types in the first language to data representation types in the second language. When a compilation error occurs, the implementing step also includes generating a special node for error processing in the second abstract syntax tree and storing an error token in the special node. When unparsing, the implementing step additionally includes outputting the error token, in the form of source code written in the first language.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Obtaining correct compile results by absorbing mismatches between data types representations

Methods and a system are provided. A method includes implementing a function, which a compiler for a first language does not have, using a compiler for a second language. The implementing step includes generating, by the compiler for the first language, a first abstract syntax tree. The implementing step further includes converting, by a converter, the first abstract syntax tree to a second abstract syntax tree of the compiler for the second language using a conversion table from data representation types in the first language to data representation types in the second language. When a compilation error occurs, the implementing step also includes generating a special node for error processing in the second abstract syntax tree and storing an error token in the special node. When unparsing, the implementing step additionally includes outputting the error token, in the form of source code written in the first language.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Obtaining correct compile results by absorbing mismatches between data types representations

Methods and a system are provided. A method includes implementing a function, which a compiler for a first language does not have, using a compiler for a second language. The implementing step includes generating, by the compiler for the first language, a first abstract syntax tree. The implementing step further includes converting, by a converter, the first abstract syntax tree to a second abstract syntax tree of the compiler for the second language using a conversion table from data representation types in the first language to data representation types in the second language. When a compilation error occurs, the implementing step also includes generating a special node for error processing in the second abstract syntax tree and storing an error token in the special node. When unparsing, the implementing step additionally includes outputting the error token, in the form of source code written in the first language.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Transportation Energy Data Book: Edition 38

The Transportation Energy Data Book: Edition 38 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available via the Internet. This edition of the Data Book has 13 chapters which focus on various aspects of the transportation industry. Chapter 1 - focuses on petroleum; Chapter 2 - energy; Chapter 3 - highway vehicles; Chapter 4 - light vehicles; Chapter 5 - heavy vehicles; Chapter 6 - alternative fuel vehicles; Chapter 7 - transit and shared mobility; Chapter 8 - fleet vehicles; Chapter 9 - household vehicles; Chapter 10 - nonhighway modes; Chapter 11 - transportation and the economy; Chapter 12 - greenhouse gas emissions; and Chapter 13 - criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and energy tables and figures with electricity generation and distribution taken into account. A glossary of terms is also included for the reader’s convenience.

02 PETROLEUM↗
DOE OSTI2021

Transportation Energy Data Book: Edition 39

The Transportation Energy Data Book: Edition 39 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available via the Internet (tedb.ornl.gov).This edition of the Data Book has 13 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 – energy; Chapter 3 – highway vehicles; Chapter 4 – light vehicles; Chapter 5 – heavy vehicles; Chapter 6 – alternative fuel vehicles; Chapter 7 – transit and other shared mobility; Chapter 8 – fleet vehicles; Chapter 9 – household vehicles; Chapter 10 – nonhighway modes; Chapter 11 – transportation and the economy; Chapter 12 – greenhouse gas emissions; and Chapter 13 – criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and energy tables with electricity generation and distribution. A glossary of terms is also included for the reader’s convenience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Initial implementation of the conversion from the energy-subtracted CT number to electron density in tissue inhomogeneity corrections: An anthropomorphic phantom study of radiotherapy treatment planning

Purpose: To achieve accurate tissue inhomogeneity corrections in radiotherapy treatment planning, the authors had previously proposed a novel conversion of the energy-subtracted computed tomography (CT) number to an electron density (ΔHU–ρ{sub e} conversion), which provides a single linear relationship between ΔHU and ρ{sub e} over a wide range of ρ{sub e}. The purpose of this study is to present an initial implementation of the ΔHU–ρ{sub e} conversion method for a treatment planning system (TPS). In this paper, two example radiotherapy plans are used to evaluate the reliability of dose calculations in the ΔHU–ρ{sub e} conversion method. Methods: CT images were acquired using a clinical dual-source CT (DSCT) scanner operated in the dual-energy mode with two tube potential pairs and an additional tin (Sn) filter for the high-kV tube (80–140 kV/Sn and 100–140 kV/Sn). Single-energy CT using the same DSCT scanner was also performed at 120 kV to compare the ΔHU–ρ{sub e} conversion method with a conventional conversion from a CT number to ρ{sub e} (Hounsfield units, HU–ρ{sub e} conversion). Lookup tables for ρ{sub e} calibration were obtained from the CT image acquisitions for tissue substitutes in an electron density phantom (EDP). To investigate the beam-hardening effect on dosimetric uncertainties, two EDPs with different sizes (a body EDP and a head EDP) were used for the ρ{sub e} calibration. Each acquired lookup table was applied to two radiotherapy plans designed using the XiO TPS with the superposition algorithm for an anthropomorphic phantom. The first radiotherapy plan was for an oral cavity tumor and the second was for a lung tumor. Results: In both treatment plans, the performance of the ΔHU–ρ{sub e} conversion was superior to that of the conventional HU–ρ{sub e} conversion in terms of the reliability of dose calculations. Especially, for the oral tumor plan, which dealt with dentition and bony structures, treatment planning with the HU–ρ{sub e} conversion exhibited apparent discrepancies between the dose distributions and dose–volume histograms (DVHs) of the body-EDP and head-EDP calibrations. In contrast, the dose distributions and DVHs of the body-EDP and head-EDP calibrations coincided with each other almost perfectly in the ΔHU–ρ{sub e} conversion for 100–140 kV/Sn. The difference between the V{sub 100}’s (the mean planning target volume receiving 100% of the prescribed dose; a DVH parameter) of the body-EDP and head-EDP calibrations could be reduced to less than 1% using the ΔHU–ρ{sub e} conversion, but exceeded 11% for the HU–ρ{sub e} conversion. Conclusions: The ΔHU–ρ{sub e} conversion can be implemented for currently available TPS’s without any modifications or extensions. The ΔHU–ρ{sub e} conversion appears to be a promising method for providing an accurate and reliable inhomogeneity correction in treatment planning for any ill-conditioned scans that include (i) the use of a calibration EDP that is nonequivalent to the patient’s body tissues, (ii) a mismatch between the size of the patient and the calibration EDP, or (iii) a large quantity of high-density and high-atomic-number tissue structures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Multi-Angle Snowflake Camera Value-Added Product

The Multi-Angle Snowflake Camera (MASC) addresses a need for high-resolution multi-angle imaging of hydrometeors in freefall with simultaneous measurement of fallspeed. As illustrated in Figure 1, the MASC consists of three cameras, separated by 36°, each pointing at an identical focal point approximately 10 cm away. Located immediately above each camera, a light aims directly at the center of depth of field for its corresponding camera. The focal point at which the cameras are aimed lies within a ring through which hydrometeors fall. The ring houses a system of near-infrared emitter-detector pairs, arranged in two arrays separated vertically by 32 mm. When hydrometeors pass through the lower array, they simultaneously trigger all cameras and lights. Fallspeed is calculated from the time it takes to traverse the distance between the upper and lower triggering arrays. The trigger electronics filter out ambient light fluctuations associated with varying sunlight and shadows. The microprocessor onboard the MASC controls the camera system and communicates with the personal computer (PC). The image data is sent via FireWire 800 line, and fallspeed (and camera control) is sent via a Universal Serial Bus (USB) line that relies on RS232-over-USB serial conversion. See Table 1 for specific details on the MASC located at the Oliktok Point Mobile Facility on the North Slope of Alaska. The value-added product (VAP) detailed in this documentation analyzes the raw data (Section 2.0) using Python: images rely on OpenCV image processing library and derived aggregated statistics rely on some clever averaging. See Sections 4.1 and 4.2 for more details on what variables are computed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Transportation Energy Data Book: Edition 34

The Transportation Energy Data Book: Edition 34 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.

Air↗
DOE OSTI2014

Transportation Energy Data Book. Edition 33

The Transportation Energy Data Book: Edition 33 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Transportation Energy Data Book: Edition 35

The Transportation Energy Data Book: Edition 35 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗