Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cost Modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Grid Modeling for the SunShot Vision Study

This document describes the use of production cost modeling in the SunShot Vision study, including methods used to create the SunShot Vision scenarios, their implementation in the Gridview model, and assumptions regarding transmission system and operation of each generator type. It also describes challenges and limitations of modeling solar generation technologies in production cost models, andsuggests methods for improving their representation in current models.

14 SOLAR ENERGY↗
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↗
NASA NTRS2017

The Opportunity in Commercial Approaches for Future NASA Deep Space Exploration Elements

In 2011, NASA released a report assessing the market for commercial crew and cargo services to low Earth orbit (LEO). The report stated that NASA had spent a few hundred million dollars in the Commercial Orbital Transportation Services (COTS) program on the portion related to the development of the Falcon 9 launch vehicle. Yet a NASA cost model predicted the cost would have been significantly more with a non-commercial cost-plus contracting approach. By 2016 a NASA request for information stated it must "maximize the efficiency and sustainability of the Exploration Systems development programs", as "critical to free resources for reinvestment...such as other required deep space exploration capabilities." This work joins the previous two events, showing the potential for commercial, public private partnerships, modeled on programs like COTS, to reduce the cost to NASA significantly for "...other required deep space exploration capabilities." These other capabilities include landers, stages and more. We mature the concept of "costed baseball cards", adding cost estimates to NASA's space systems "baseball cards." We show some potential costs, including analysis, the basis of estimates, data sources and caveats to address a critical question - based on initial assessment, are significant agency resources justified for more detailed analysis and due diligence to understand and invest in public private partnerships for human deep space exploration systems? The cost analysis spans commercial to cost-plus contracting approaches, for smaller elements vs. larger, with some variation for lunar or Mars. By extension, we delve briefly into the potentially much broader significance of the individual cost estimates if taken together as a NASA investment portfolio where public private partnership are stitched together for deep space exploration. How might multiple improvements in individual systems add up to NASA human deep space exploration achievements, realistically, affordably, sustainably, in a relevant timeframe?

cost modeling estimation↗
NASA NTRS1995

O/S analysis of conceptual space vehicles

The application of recently developed computer models in determining operational capabilities and support requirements during the conceptual design of proposed space systems is discussed. The models used are the reliability and maintainability (R&M) model, the maintenance simulation model, and the operations and support (O&S) cost model. In the process of applying these models, the R&M and O&S cost models were updated. The more significant enhancements include (1) improved R&M equations for the tank subsystems, (2) the ability to allocate schedule maintenance by subsystem, (3) redefined spares calculations, (4) computing a weighted average of the working days and mission days per month, (5) the use of a position manning factor, and (6) the incorporation into the O&S model of new formulas for computing depot and organizational recurring and nonrecurring training costs and documentation costs, and depot support equipment costs. The case study used is based upon a winged, single-stage, vertical-takeoff vehicle (SSV) designed to deliver to the Space Station Freedom (SSF) a 25,000 lb payload including passengers without a crew.

Ebeling, Charles E.↗
NASA NTRS2008

Civil Service Workforce Market Supply and the Effect on the Cost Estimating Relationships (CERs) that may effect the Productivity Factors for Future NASA Missions

The upcoming retirement of the Baby Boomers on the horizon will leave a performance gap between younger generation (the future NASA decision makers) and the gray beards. This paper will reflect on the average age of workforce across NASA Centers, the Aerospace Industry and other Government Agencies, like DoD. This papers will dig into Productivity and Realization Factors and how they get applied to bimonthly (payroll data) for true FTE calculations that could be used at each of the NASA Centers and other business systems that are on the forefront in being implemented. This paper offers some comparative costs solutions, from simple - full time equivalent (FTE) cost estimating relationships CERs, to complex - CERs for monthly time-phasing activities for small research projects that start and get completed within a government fiscal year. This paper will present the results of a parametric study investigating the cost-effectiveness of different alternatives performance based cost estimating relationships (CERs) and how they get applied into the Center s forward pricing rate proposals (FPRP). True CERs based on the relationship of a younger aged workforce will have some effects on labor rates used in both commercial cost models and internal home-grown cost models which may impact the productivity factors for future NASA missions.

Sterk, Steve↗
DOE OSTI2019

The International Supply Chain and Manufacturing Costs for Photovoltaic Modules, and Project Economics of Systems Including Storage

This talk will highlight the most recent efforts from the National Renewable Energy Laboratory's (NREL) Strategic Energy Analysis Center (SEAC) to track solar PV manufacturing capacities and trade flows, bottom-up manufacturing costs, and project capital costs including storage. We will begin with an overview of the global PV supply chain and the 2019 benchmark input data used to inform NREL's crystalline silicon (c-Si) and thin film module manufacturing cost models. The accounting framework that we follow and that will be reviewed during this talk provides a methodology to prepare cost models in accordance with the Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standards (IFRS). For the polysilicon, wafer, cell conversion, and module assembly steps of the c-Si supply chain, and for thin film modules, we will review input data and methods useful for calculating the costs of goods sold (COGS); research and development (R&D) expenses; and sales, general, and business administration (S, G, &A) expenses. This 2019 benchmark analysis is compiled for state-of-the-art polysilicon-to-module manufacturing across the globe. We will also provide an overview of opportunities for solar plus storage and show our 2018 benchmark bottom-up capital cost model results for utility-scale PV systems with battery storage.

14 SOLAR ENERGY↗
NASA NTRS1992

A model for the cost of doing a cost estimate

A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K((C(sub p))(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns.

Remer, D. S.↗