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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Reference Model 1 Cost Breakdown (RM1: Tidal Current Turbine)

Contains the Reference Model 1 (RM1) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM1 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 1 (RM1) is a dual variable-speed variable-pitch (VSVP) axial-flow tidal turbine device, designed for the Tacoma Narrows tidal current energy resource site in Puget Sound, Washington. RM1 comprises a monopile foundation and a crossarm assembly to mount the two rotors. The cross-arm assembly is nearly neutrally buoyant so the attached rotors can be recovered and redeployed with a minimal amount of lifting crane capacity; therefore, the design minimizes the handling requirements during deployment and recovery, which reduces overall cost in all O&M activities including access to the power conversion chain (PCC).

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Reference Model 6 Cost Breakdown (RM6: Oscillating Water Column)

Contains the Reference Model 6 (RM6) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM6 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 6 (RM6) is a Backward Bent Duct Buoy (BBDB), which is a type of oscillating water column wave energy converter. First proposed by Masuda, the BBDB design is a floating Oscillating Water Column (OWC) device that consists of an air chamber, an L-shaped duct, bow and stern buoyancy modules, and a power take-off (PTO) composed of a Wells air turbine and a generator. This L- shaped device opens to the ocean downstream from the wave propagation direction. Power is produced by the motion of the wave, which causes the ambient pressure in the air chamber to vary thereby forcing air to flow through the Wells turbine. The reference wave energy resource for RM6 was developed from site information collected near Eureka, in Humboldt County, California.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Reference Model 3 Cost Breakdown (RM3: Wave Point Absorber)

Contains the Reference Model 3 (RM3) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM3 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 3 (RM3) is a wave point absorber, also referred to as a wave power buoy, that was designed for a reference site located off the shore of Eureka in Humboldt County, California. The design of the device consists of a surface float that translates (oscillates) with wave motion relative to a vertical column spar buoy, which connects to a subsurface reaction plate. This two-body point absorber converts wave energy into electrical power predominately from the devices heave oscillation induced by incident waves; the float is designed to oscillate up and down the vertical shaft up to 4 m. The bottom of the reaction plate is about 35 m below the water surface. The device is targeted for deployment in water depths of 40 m to 100 m. The point absorber is also connected to a mooring system to keep the floating device in position.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Reference Model 4 Cost Breakdown (RM4: Ocean Current Turbine)

Contains the Reference Model 4 (RM4) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM4 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 4 (RM4) is a flying-wing ocean current turbine concept intended for deployment in the Gulf Stream off the southeast coast of Florida. The RM4 device has four rotors, with a rotorless center nacelle housing the power electronics, attached on a straight wing 120 m long. The device is designed to be submerged ~50 m below the surface and is moored to the seabed. The RM4 uses buoyancy within the wing and the five nacelles to maintain its position in the water column. Each rotor has a diameter of 33 m and has a 1-MW power rating, yielding a total device rated power of 4 MW. The rotors on the left and right side of the wing rotate in opposite directions in order to balance the torque applied to the device. The rotorless center nacelle housing the power electronics serves to condition the power generated by the rotors before it is delivered to the grid.

16 TIDAL AND WAVE POWER↗
NASA NTRS1996

What Drives Spacecraft Cost: A Look Into How Material Characteristics Relate to the Material Complexity Multipliers

Today a variety of engineered materials are used to build the space vehicles and satellites that NASA, DOD and the aerospace community will use in future projects. These materials can be a significant part of the cost when designing and building these systems. Current cost models such as NASCOM, SEER-H and PRICE allow the cost analysis to select materials requirements during the development of the cost model. It should be noted however that some of these models do not always give the most detailed information with respect to material specifications for the given cost model. Instead the materials are defined within broad classification, giving questionable data with regard to specific material cost. It is the objective of this paper to present a summary of basic information on materials to assist the cost analyst in the development of their models. Specifically, this paper will compare materials and their complexity multipliers to some specific material properties.

Rosmait, Russell L.↗
NASA NTRS1983

The Shuttle Cost and Price model

The Shuttle Cost and Price (SCP) model was developed as a tool to assist in evaluating major aspects of Shuttle operations that have direct and indirect economic consequences. It incorporates the major aspects of NASA Pricing Policy and corresponds to the NASA definition of STS operating costs. An overview of the SCP model is presented and the cost model portion of SCP is described in detail. Selected recent applications of the SCP model to NASA Pricing Policy issues are presented.

Leary, Katherine↗
DOE OSTI2026

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

08 HYDROGEN↗
NASA NTRS1988

System capacity and economic modeling computer tool for satellite mobile communications systems

A unique computer modeling tool that combines an engineering tool with a financial analysis program is described. The resulting combination yields a flexible economic model that can predict the cost effectiveness of various mobile systems. Cost modeling is necessary in order to ascertain if a given system with a finite satellite resource is capable of supporting itself financially and to determine what services can be supported. Personal computer techniques using Lotus 123 are used for the model in order to provide as universal an application as possible such that the model can be used and modified to fit many situations and conditions. The output of the engineering portion of the model consists of a channel capacity analysis and link calculations for several qualities of service using up to 16 types of earth terminal configurations. The outputs of the financial model are a revenue analysis, an income statement, and a cost model validation section.

Wiedeman, Robert A.↗
DOE OSTI2021

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operationson Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

Jeong, Geonhwa↗
DOE OSTI2016

Multi-spatial analysis of forest residue utilization for bioenergy

The alternative energy sector is expanding quickly in the USA since passage of the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007. Increased interest in wood-based bioenergy has led to the need for robust modeling methods to analyze woody biomass operations at landscape scales. However, analyzing woody biomass operations in regions like the US Inland Northwest is difficult due to highly variable terrain and wood characteristics. We developed the Forest Residue Economic Assessment Model (FREAM) to better integrate with Geographical Information Systems and overcome analytical modeling limitations. FREAM analyzes wood-based bioenergy logistics systems and provides a modeling platform that can be readily modified to analyze additional study locations. We evaluated three scenarios to test the FREAM's utility: a local-scale scenario in which a catalytic pyrolysis process produces gasoline from 181 437 Mg yr-1 of forest residues, a regional-scale scenario that assumes a biochemical process to create aviation fuel from 725 748 Mg yr-1 of forest residues, and an international scenario that assumes a pellet mill producing pellets for international markets from 272 155 Mg yr-1 of forest residues. The local scenario produced gasoline for a modeled cost of $22.33 GJ-1*, the regional scenario produced aviation fuel for a modeled cost of $35.83 GJ-1 and the international scenario produced pellets for a modeled cost of $10.51 GJ-1. Results show that incorporating input from knowledgeable stakeholders in the designing of a model yields positive results.

09 BIOMASS FUELS↗
NASA NTRS1992

Cost - The challenge for advanced materials and structures

Information is presented on the cost of various aircraft structures, together with methods for predicting and reducing cost. The need for the development of cost models, and of a comparative cost algorithm which could function as an engineering design tool to evaluate different design concepts, is emphasized. Efforts are underway to develop cost models that establish building-block unit cell elements that represent different material forms, geometric shapes, fabrication processes, and methods of assembly, with the purpose of expressing cost per pound or labor per pound data, with physical design and manufacture variables that a designer can visualize.

Davis, John G., Jr.↗
NASA NTRS2016

Nasa Space Flight Instruments: Cost Time Trends

Are NASA’s space flight instruments becoming cheaper or more expensive as time marches forward? After analyzing the costs of hundreds of instruments launched over the last 30 years, the short answer to this question is no… and yes. This paper gives a visual analysis of the cost time trends for various NASA space flight instrument types, such as optical, particles detectors, fields detectors and microwave instruments. In addition to the statistical approaches utilized, such as significance tests, cluster analysis and principle components analysis (PCA), we will also discuss the intangibles which are likely at play, including technological progress, NASA policy and the luck of the draw associated with mission manifests. This analysis was performed as the main driver for the NASA Instrument Cost Model (NICM) recent cost estimating model redesign. Started in 2004, the first version of NICM was based off of instruments launched from 1985-2005, or 20 years’ worth of data. As NICM hit its 10-year anniversary, we wanted to know: should NICM continue to only use the most recent 20 years’ worth of data (1995-2015)? Are instruments becoming cheaper or more expensive as time marches forward? There is evidence in favor of a drop in the median dollar-per-kg value across some instrument types, but little in others. Whereas further research is needed to substantiate, Particles and Optical-Planetary instrument types show moderate to strong evidence of a downward trend in dollar-per-kg. Further research is required to study the nature of this trend (shift, taper, cyclic, etc.). Little evidence for a similar downward trend was detected for Fields or Microwave instruments, or Optical instruments on Earth Orbiting spacecraft. We presented evidence in favor of a drop in the median dollar-per-kg value for Particles and Optical-Planetary instrument types. While similar evidence was weak at best for Fields and Microwave instruments. We can speculate as to the causes for this effect, but we are also equipped to begin to rule out, or at least prioritize, some of the suspected drivers. We observed, for Particles and Optical-Planetary instruments, that perhaps a launch manifest effect was playing part of the role in the observed decrease in dollar-per-kg over the years, noting that the more flagship class missions, which have more money to spend on their instruments, were seen in the earlier years in our data, versus the later years which were dominated by less expensive class missions. However, if this were a dominating driver, would we not have seen the downward trend in the Fields and Microwave instruments as well, which were drawn from that same launch manifest? The fact that we did not observe this helps us rule out the launch manifest effect, and other drivers, such as advances in technology, that seem to be more likely suspects. In that case, however, why would technology advances be helping the Particles and Optical-Planetary instruments only? Why would it not be impacting Optical-Earth Orbiting instruments? Further suspects were looked at as well and ruled out, such as the “Faster, Better, Cheaper”era of NASA development which did not seem to actually impact trends by instrument type on a dollar-perkg scale. VI. Future Work A. Time Series Detailed Statistical Assessment The analysis discussed above sets the foundation for a more rigorous time series analysis of the data. Time series analysis will further explore evidence to-date of time trends for the instrument types which showed the strongest indicators for a decrease in dollar-per-kg: Optical (Planetary) and Particles instruments. More than providing evidence and top-level significance tests, time series analysis would help elucidate what kind of trend that exists in the data, their significance and allow statistically based forecasting (see Figure 10)

Mrozinksi, Joseph↗
NASA NTRS2017

The Dangers of Parametrics

Building a parametric cost model is hard work. The data is noisy and often does not behave like we want it to. We need statistics to give us an indication of the goodness of our models, but; statistics can be manipulated and mislead. On top of all of that, our own very human biases can lead us astray; causing us to see patterns in the noise and draw false conclusions from the data. Yet, it is the data itself that is the foundation for making better cost estimates and cost models. I believe the mistake we often make is we believe that our models are representative of the data; that our models summarize the experiences, the knowledge, and the stories contained in the data. However, it is the opposite that is true. Our models are but imitations of reality. They give us trends, but not truth. The experiences, the knowledge, and the stories that we need in order to make good cost estimates is bound up in the data. You cannot separate good cost estimating from a knowledge of the historical data. One final thought. It is our attempts to make sense out of the randomness that leads us astray. In order to make progress as cost modelers and cost estimators, we must accept that there are real limitations on our ability to model the past and predict the future. I do not believe we should throw up our hands and say this is the best we can do. Rather, to see real improvement we must first recognize these limitations, avoid the easy but misleading solutions, and seek to find ways to better model the world we live in. I don't have any simple solutions. Perhaps the answers lie in better data or in a totally different approach to simulating how the world works. All I know is that we must do our best to speak truth to ourselves and our customers. Misleading ourselves and our customers will, in the end, result in an inability to have a positive impact on those we serve.

Prince, Frank A.↗
NASA NTRS2017

The Dangers of Parametrics

Building a parametric cost model is hard work. The data is noisy and often does not behave like we want it to. We need statistics to give us an indication of the goodness of our models, but; statistics can be manipulated and mislead. On top of all of that, our own very human biases can lead us astray; causing us to see patterns in the noise and draw false conclusions from the data. Yet, it is the data itself that is the foundation for making better cost estimates and cost models. I believe the mistake we often make is we believe that our models are representative of the data; that our models summarize the experiences, the knowledge, and the stories contained in the data. However, it is the opposite that is true. Our models are but imitations of reality. They give us trends, but not truth. The experiences, the knowledge, and the stories that we need in order to make good cost estimates is bound up in the data. You cannot separate good cost estimating from a knowledge of the historical data. One final thought. It is our attempts to make sense out of the randomness that leads us astray. In order to make progress as cost modelers and cost estimators, we must accept that there are real limitations on our ability to model the past and predict the future. I do not believe we should throw up our hands and say this is the best we can do. Rather, to see real improvement we must first recognize these limitations, avoid the easy but misleading solutions, and seek to find ways to better model the world we live in. I don't have any simple solutions. Perhaps the answers lie in better data or in a totally different approach to simulating how the world works. All I know is that we must do our best to speak truth to ourselves and our customers. Misleading ourselves and our customers will, in the end, result in an inability to have a positive impact on those we serve.

Prince, Frank A.↗
DOE OSTI2014

Demand Response Availability Profiles for California in the Year 2020

Demand response (DR) is being considered as a valuable resource for keeping the electrical grid stable and efficient, and deferring upgrades to generation, transmission, and distribution systems. However, simulations to determine how much infrastructure upgrades can be deferred are necessary in order to plan optimally. Production cost modeling is a technique, which simulates the dispatch of generators to meet demand and reserves in each hour of the year, at minimal cost. By integrating demand response resources into a production cost model (PCM), their value to the grid can be estimated and used to inform operations and infrastructure planning. DR availability profiles and constraints for 13 end-uses in California for the year 2020 were developed by Lawrence Berkeley National Laboratory (LBNL), and integrated into a production cost model by Lawrence Livermore National Laboratory (LLNL), for the California Energy Commission’s Value of Energy Storage and Demand Response for Renewable Integration in California Study. This report summarizes the process for developing the DR availability profiles for California, and their aggregate capabilities. While LBNL provided potential DR hourly profiles for regulation product in the ancillary services market and five-minute load following product in the energy market for LLNL’s study, additional results in contingency reserves and an assumed flexible product are also defined. These additional products are included in the analysis for managing high ramps associated with renewable generation and capacity products and they are also presented in this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Intermountain West Energy Sustainability & Transitions Initiative: CO2 Transport and Geologic Storage Modeling Results

These resources provide the full set of cost modeling results and methods as part of the I-WEST Roadmap Initiative that were used to compile figures as part of the "Pathways to CO2 Utilization and Storage for the Intermountain West Region" chapter. The data were generated from a series of National Energy Technology Laboratory (NETL) cost models and relate to carbon dioxide (CO2) transport costs, CO2 enhanced oil recovery (CO2-EOR) economics, and saline storage economics. These models were used to analyze various business cases given changes in technical and financial assumptions for the I-WEST region as a means to explore how these assumptions influence CO2 transport and storage costs, as well as to evaluate the effect of changing oil prices on the viability of CO2-EOR and the mass of CO2 stored via CO2-EOR. The accompanying report titled "Intermountain West Energy Sustainability & Transitions Initiative: CO2 Transport and Geologic Storage Modeling Results" provides a detailed overview on the models, assumptions, and parameters used in the modeling, as well as example results..

CO2 EOR costs,CO2 Storage Costs,CO2 transportation↗
NASA NTRS2006

Software Development Cost Estimation Executive Summary

Identify simple fully validated cost models that provide estimation uncertainty with cost estimate. Based on COCOMO variable set. Use machine learning techniques to determine: a) Minimum number of cost drivers required for NASA domain based cost models; b) Minimum number of data records required and c) Estimation Uncertainty. Build a repository of software cost estimation information. Coordinating tool development and data collection with: a) Tasks funded by PA&E Cost Analysis; b) IV&V Effort Estimation Task and c) NASA SEPG activities.

data mining↗