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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles while producing significantly less emissions of tailpipe oxides of nitrogen (NOx). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles while producing significantly less emissions of tailpipe oxides of nitrogen (NOx). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles while producing significantly less emissions of tailpipe oxides of nitrogen (NOx). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Classification of River Catchments in the Contiguous United States: Code, Dataset, Similarity Patterns, and Resulting Classes

This dataset serves as supplementary information for the paper by Ciulla F. and Varadharajan C. A Network Approach for Multiscale Catchment Classification using Traits (see reference 1). It contains environmental and physical catchment traits, such as temperatures, precipitation, land use and human interference, from 9067 sites across the contiguous United States (CONUS). The purpose of this dataset is to provide information for a better trait-based categorization of river catchments in the CONUS using networks as an analytical tool. The traits variables match the ones present in the GAGES-II dataset and the preprocessing steps are described in the Methods section (processed_dataset.csv). Additionally we include the topologies (nodes, edges and clusters, also referred as classes) of the catchment network and traits network generated by said dataset (csv and json files). A series of tables support the information carried by the network providing more detailed descriptions of cluster components (SI1.pdf). A summary of all the plots of clusters of catchments with at least 50 nodes is provided (SI2.pdf). The characteristic traits for each cluster of catchments is presented as z-score (traits_categories_zscores_per_catchment_class.csv). The link to the hydrological behavior of clusters of catchments is displayed by boxplots, each describing a particular river discharge index (SI3.pdf). Both csv and json files can be read by common text editors but the data contained into them can be better handled using programming languages like python and database oriented libraries like pandas. Pdf files can be read by any pdf reader software.[02-23-2024] Update: The code and datasets necessary to reproduce the results of the study are available as a zipped repository (code_datasets_catchments_similarity.zip).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model-ready benchmarks for NPP, ANPP, litter fluxes, and recruitment into the 1 cm dbh size class

The intended use of this dataset is to serve as an observational benchmark to evaluate model predictions of NPP, ANPP, litter fluxes, and recruitment at Barro Colorado Island, Panama. This dataset contains four CSV files and one text file. “Benchmarks-NPP-ANPP-R-L.csv” provides estimates of annual ecosystem-level reproductive litter flux (R), leaf litter flux (L), aboveground net primary productivity (ANPP), and net primary productivity (NPP) for 61 field plots throughout tropical, temperate, and boreal forest biomes. An additional 499 plots (n = 550) include estimates of just R, L, and R/L. Each row reports a distinct set of estimates for one sampling interval at one plot. “Metadata-Benchmarks-NPP-ANPP-R-L.csv” contains field descriptions for all data fields in “Benchmarks-NPP-ANPP-R-L.csv”. “References-Benchmarks-NPP-ANPP-R-L.txt” contains full references to the original studies used to produce the observations at each plot included in the data. “Benchmarks-Recruitment.csv” provides estimates of species-level recruitment rates into the 1 cm dbh size class at four CTFS-ForestGeo sites using methods that account for unobserved mortality of new recruits between census intervals (Kohyama et al., 2018). “Metadata-Benchmarks-Recruitment.csv” contains field descriptions for all data fields in “Benchmarks-Recruitment.csv”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

A toolbox for a class of discontinuous Petrov-Galerkin methods using trilinos.

The class of discontinuous Petrov-Galerkin finite element methods (DPG) proposed by L. Demkowicz and J. Gopalakrishnan guarantees the optimality of the solution in an energy norm and produces a symmetric positive definite stiffness matrix, among other desirable properties. In this paper, we describe a toolbox, implemented atop Sandia's Trilinos library, for rapid development of solvers for DPG methods. We use this toolbox to develop solvers for the Poisson and Stokes problems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Safety-Class Uninterruptable Power Supply General Contractor for the TA-55 TRP II Project: REQUEST FOR QUALIFICATION No. ADPM-002 - PREQUALIFICATION CRITERIA

LANS is seeking to establish qualified suppliers for the procurement and construction of a Safety-Class Uninterruptable Power Supply (UPS) facility in accordance with ASME NQA-1 2008 and 2009 addenda. This scope includes Work required to construct a new UPS facility at Technical Area (TA)-55 including procurement, construction, installation, and post installation testing of all structures, systems, and equipment (e.g., foundations, concrete, structural steel, building components, HVAC, fire protection, site utilities) excluding the UPS system itself. Design of the facility and associated infrastructure are provided by LANL. Final design of the UPS system will be provided by the UPS Integrator under separate contract. Procurement of all safety-related components (specified by LANL) must be in accordance with NQA-1 and must be purchased from qualified suppliers that have an NQA-1 program or through Commercial Grade Dedication (CGD) in accordance with NQA-1a-2009, Subpart 2.14. The SUBCONTRACTOR shall plan, schedule, coordinate, test, inspect and assure effective performance of all aspects of the Work as described herein.

42 ENGINEERING↗
DOE OSTI2014

An Optimizing Compiler for Petascale I/O on Leadership-Class Architectures

In high-performance computing (HPC), parallel I/O architectures usually have very complex hierarchies with multiple layers that collectively constitute an I/O stack, including high-level I/O libraries such as PnetCDF and HDF5, I/O middleware such as MPI-IO, and parallel file systems such as PVFS and Lustre. Our DOE project explored automated instrumentation and compiler support for I/O intensive applications. Our project made significant progress towards understanding the complex I/O hierarchies of high-performance storage systems (including storage caches, HDDs, and SSDs), and designing and implementing state-of-the-art compiler/runtime system technology that targets I/O intensive HPC applications that target leadership class machine. This final report summarizes the major achievements of the project and also points out promising future directions Two new sections in this report compared to the previous report are IOGenie and SSD/NVM-specific optimizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An Optimizing Compiler for Petascale I/O on Leadership Class Architectures

In high-performance computing systems, parallel I/O architectures usually have very complex hierarchies with multiple layers that collectively constitute an I/O stack, including high-level I/O libraries such as PnetCDF and HDF5, I/O middleware such as MPI-IO, and parallel file systems such as PVFS and Lustre. Our project explored automated instrumentation and compiler support for I/O intensive applications. Our project made significant progress towards understanding the complex I/O hierarchies of high-performance storage systems (including storage caches, HDDs, and SSDs), and designing and implementing state-of-the-art compiler/runtime system technology that targets I/O intensive HPC applications that target leadership class machine. This final report summarizes the major achievements of the project and also points out promising future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Notification of General Class 1 Permit Modification for the Los Alamos National Laboratory (LANL) Hazardous Waste Facility Permit, EPA ID # NM0890010515

This document contains a notification for a Class 1 Permit Modification to modify the Los Alamos National Laboratory (LANL) Hazardous Waste Facility Permit (Permit). The text modifications are provided herein and have been made to the Permit by the Department of Energy (DOE) and Los Alamos National Security, LLC (LANS), collectively the Permittees. The modifications to the text of the Permit have been identified using red text and red lines through the texts for deletions. Description: This modification includes three changes. One change includes an address change within the Attachment D, Contingency Plan, and the remaining changes correct two typographical errors in Permit Section 2.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Notification of Class 1 Permit Modification to Attachment B, Part A Application for the Los Alamos National Laboratory Hazardous Waste Facility Permit, EPA ID # NM0890010515

This document contains a notification for a Class 1 Permit Modification to modify the Los Alamos National Laboratory (LANL) Hazardous Waste Facility Permit (Permit) issued to the Department of Energy and the Los Alamos National Security, LLC, collectively known as the Permittees, in November 2010. All changes have been made to Attachment B, Part A Application, and a full, updated form, Revision 10.0, is enclosed. The modification updates the facility contact and address information, and the process codes and design capacity section for Technical Area (TA) 14; TA-16; TA-39; TA-54 West; TA-54, Area G; and TA-55 within the Attachment B, Part A Application. The process codes and design capacity section for TA-14, TA-16, and TA-39 was updated to incorporate a statement that the Permittees have notified of the intent to close units at these locations. The process design capacity for TA-54, Area G has been updated from 3,664,150 gallons to 4,346,590 gallons to account for the TA-54, Area G Pad 11 container storage unit that was added when the Permit was issued in 2010 but at the time did not previously store hazardous waste. The number of units at TA-54, Area G was also updated to 9 units to include this new unit. The process design capacity for TA-55 has also been updated from 178,500 gallons to 207,600 gallons to incorporate the addition of the TA-55-185 container storage unit that was also added when the Permit was issued. The number of storage units at TA-55 was updated to 7 to include the unit. The process design capacity at TA-54 West was changed to 11,660 gallons to correct a typographical error in previous versions of the form.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Recovery Act--Class 8 Truck Freight Efficiency Improvement Project

Daimler Trucks North America completed a five year, $79.6M project to develop and demonstrate a concept vehicle with at least 50% freight efficiency improvement over a weighted average of several drive cycles relative to a 2009 best-in-class baseline vehicle. DTNA chose a very fuel efficient baseline vehicle, the 2009 Freightliner Cascadia with a DD15 engine, yet successfully demonstrated a 115% freight efficiency improvement. DTNA learned a great deal about the various technologies that were incorporated into Super Truck and those that, through down-selection, were discarded. Some of the technologies competed with each other for efficiency, and notably some of the technologies complemented each other. For example, we found that Super Truck’s improved aerodynamic drag resulted in improved fuel savings from eCoast, relative to a similar vehicle with worse aerodynamic drag. However, some technologies were in direct competition with each other, namely the predictive technologies which use GPS and 3D digital maps to efficiently manage the vehicles kinetic energy through controls and software, versus hybrid which is a much costlier technology that essentially targets the same inefficiency. Furthermore, the benefits of a comprehensive, integrated powertrain/vehicle approach was proven, in which vast improvements in vehicle efficiency (e.g. lower aero drag and driveline losses) enabled engine strategies such as downrating and downspeeding. The joint engine and vehicle developments proved to be a multiplier-effect which resulted in large freight efficiency improvements. Although a large number of technologies made the selection process and were used on the Super Truck demonstrator vehicle, some of the technologies proved not feasible for series production.

33 ADVANCED PROPULSION SYSTEMS↗