Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cost model”

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 361 records · Page 20

Model for Collaboration among Carriers to Reduce Empty Container Truck Trips

In recent years, intermodal transport has become an increasingly attractive alternative to freight shippers. However, the current intermodal freight transport is not as efficient as it could be. Oftentimes an empty container needs to be transported from the empty container depot to the shipper, and conversely, an empty container needs to be transported from the receiver to the empty container depot. These empty container movements decrease the freight carrier’s profit, as well as increase traffic congestion, decrease roadway safety, and add unnecessary emissions to the environment. To this end, our study evaluates a potential collaboration strategy to be used by carriers for domestic intermodal freight transport based on an optimization approach to reduce the number of empty container trips. A binary integer-linear programming model is developed to determine each freight carrier’s optimal schedule while minimizing its operating cost. The model ensures that the cost for each carrier with collaboration is less than or equal to its cost without collaboration. It also ensures that average savings from the collaboration are shared equally among all participating carriers. Additionally, two stochastic models are provided to account for uncertainty in truck travel times. The proposed collaboration strategy is tested using empirical data and is demonstrated to be effective in meeting all of the shipment constraints.

42 ENGINEERING↗

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗

BEPAM-E Model Code and CABBI Simulation Results for "Repeal of the Clean Power Plan: Social Cost and Distributional Implications"

The dataset consists of results and various input data that are used in the GAMS model for the publication "Repeal of the Clean Power Plan: Social Cost and Distributional Implications". All the data are either excel files or in the .inc format which can be read within GAMS or Notepad. Main data sources include: agriculture, transportation and electricity data. Model details can be found in the paper and the GAMS model package.

carbon abatement↗

Optimal Design and Techno-Economic Analysis of 3D-Printed, Intensified Packings for Absorbers and Strippers in Solvent-Based CO 2 Capture

A potential technology for the CO 2 absorption process is utilizing intensified structured packing with embedded cooling/heating channels for continuous heat exchange, which can overcome limitations of discrete methods, such as discrete intercooling and centralized reboilers, to aid in reducing energy consumption and decreasing costs. This work investigates the modeling of intensified packing (IP) for the stripper tower, extending on previous work for the absorber, which distributes heat internally within the column, improving the thermodynamics for the solvent regeneration process. The model includes submodels for steam turbine extraction to produce steam at various qualities as well as a surrogate model for calculating steam enthalpy. A cost model for a plant-scale absorption capture process was developed, allowing for the design of the plant to be optimized, subject to minimizing capture cost using two different power plant flue gas sources. In this optimization, the placement of IP in both towers is optimized to balance the trade-off between enhanced heat transfer and reduced mass transfer volume. For natural gas combined cycle flue gas, the standard process configuration had a minimum cost of $\$$65.40/tonne CO 2 , and considering IP, the minimum capture cost is reduced to $\$$62.73/tonne, with utilization in the stripper column, which reduces yearly costs by up to $\$$2.67 MM/yr. Cooling the absorber through IP, or intercoolers, was only found to be beneficial at higher capture rates, with IP in both towers having a cost of capture of $\$$68.08/tonne at 99.9% capture, a reduction of $\$$12.64/tonne when using only intercoolers at the same capture rate. When capturing from pulverized-coal power plants, the minimum cost of capture when using IP in both towers is $\$$44.18/tonne (at 97% capture), while the standard configuration with and without intercoolers was $\$$45.69 and $\$$47.22 per tonne, respectively. This results in a reduction in yearly costs of $\$$16.98 MM/yr from the base-case configuration. At this higher CO 2 concentration, cooling in the absorber from the IP becomes extremely beneficial, reducing energy consumption by up to 6%.

20 FOSSIL-FUELED POWER PLANTS↗

Generation Plant Cost of Operations and Cycling Optimization Model (Final Technical Report)

Modern coal plants are a masterpiece of engineering, having been refined and improved over more than a century. As they evolved, they have grown more efficient and cleaner. At the same time, they have grown much larger and increasingly designed to operate on very specific fuels at or near the maximum capacity, providing baseload power. In recent decades, however, they have been called on to operate at reduced capacity (cycled) at a loss of efficiency and possibly accelerated wear and tear. The purpose of this project was to develop a model to accurately estimate the cost of cycling large coal plants so that they can be operated efficiently as part of a comprehensive strategy for generation planning and dispatch. The final goal is a model which is commercial-ready that can be “tuned” to different plants for widespread use.

20 FOSSIL-FUELED POWER PLANTS↗

Front-end engineering design (FEED) studies: a quantitative analysis

NETL has devised a methodology for normalizing FEED study metrics of interest and allowing cautious quantitative comparison across FEED studies. This presentation introduces the novel quantitative comparison methodology and presents results from utilizing this methodology to examine data presented in recent FEED study reports. The quantitative methodology developed for the examination of FEED study performance and cost also allows comparison of real-world performance and costs against NETL TEA model predicted performance and cost. Learnings from examining NETL model predicted performance and cost versus real world reported values are highlighted. These learnings provide insight into NETL TEA model uncertainty and highlight opportunities for further model development.

FEED Studies↗

Levelized Cost of Electricity (LCOE) Content Model for Design of High Deflection Foils for MHK Applications

The LCOE Content Model provides data submitters with an easy and consistent means of uploading data that can be used to calculate the levelized cost of energy for MHK devices. These data are important to DOE and will be used to develop data products that provide quantitative information to guide and support programmatic decisions. Data will also be used to evaluate MHK products and determine costs and can help data users compare the combination of capital costs and operations and maintenance over time. The ultimate goal is to use these data to perform research and tailor programs to best benefit the industry.

16 TIDAL AND WAVE POWER↗

Shallow Geothermal Resources for Cooling Applications at the University of Hawai'i

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy's Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company's GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

building cooling↗

2025 Geothermal Drilling Cost Curves Update: Preprint

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy’s Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company’s GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

15 GEOTHERMAL ENERGY↗

Model-free stabilization via Extremum Seeking using a cost neural estimator

In this paper, a fully model-free architecture for vertical stabilization of thermonuclear plasmas in tokamak experimental reactors is presented. For the first time, an Extremum Seeking control algorithm is combined with neural networks to estimate the Lyapunov function to be minimized, resulting in a fully data-driven control architecture. The performance of different neural networks are compared. Specifically, Multilayer Perceptrons and Extreme Learning Machines are considered. The proposed architecture is tested in simulation to show that it can counteract relevant plasma disturbances, resulting in a significant improvement in terms of the achievable operative space compared to the Extremum Seeking algorithm, which still relies on model-based cost estimator.

42 ENGINEERING↗

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

Benchmark Model for Wastewater Treatment Using an Activated Sludge Process

This is benchmark model for wastewater treatment using an activated sludge process. The activated sludge process is a means of treating both municipal and industrial wastewater. The activated sludge process is a multi-chamber reactor unit that uses highly concentrated microorganisms to degrade organics and remove nutrients from wastewater, producing quality effluent. This model provides pollutant concentrations, mass balance, electricity requirements, and treatment costs. This model will be continuously updated based on the latest data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Techno-Economic Wind Blade Manufacturing Model to Identify Opportunities for Cost Improvements Phase II IACMI Project 4.6/4.8

In IACMI Project 4.6 and IACMI Project 4.8, an Excel-based Techno-Economic Model (TEM) of the manufacturing process for composite wind turbine blades and a DELMIA Factory Flow Simulation of a generic wind blade manufacturing facility was developed. Together, these two tools provide a combined economic modeling capability that accounts for the material, labor, overhead and full-lifecycle operating costs associated with wind blade manufacturing as well as the impact of process flow and factory layout on overall manufacturing efficiency. The tools provide a novel means of detailed comparative analysis of the economic feasibility of proposed technologies and process changes for blade manufacturing. The modeling tools were developed with close support from members of industry and visits to multiple blade manufacturing facilities. With industry oversight, a detailed generalized manufacturing process plan and facility layout were developed with manufacturing parameters, material costs and economic factors based on historical data. Dassault Systèmes and the University of Texas at Dallas (UTD) contributed to the development of the Techno-Economic Model by providing macros to enable the generation of Bill of Material (BOM) data from a 3D blade design in either CATIA or NuMAD format, respectively. The TEM was built with the capability to directly import a Bill of Materials for economic analysis, and with the addition of the macros provided by Dassault and UTD, the TEM can directly import blade designs from both CATIA and NuMAD file formats. The modeling tools developed in Project 4.6 were used to investigate four wind blade manufacturing concepts in detail and select one to explore with laboratory-scale experimentation in Project 4.8. The four manufacturing concepts that were investigated were down-selected by the full project team from a larger list of concepts. The selections were made based on a number of criteria ranking viability and level of interest for each concept. The ‘One-Step Close’ manufacturing concept was ultimately selected for investigation in Project 4.8 and the demonstration was performed at the NREL CoMET facility. The TPI advanced manufacturing facility in Warren, RI contributed the production of several prototype components, the designs for which were developed by Janicki Industries. The demonstration project provided clear indication of the viability of the One-Step Close manufacturing concept for blade manufacturing and good validation of the Techno-Economic Model’s prediction of its economic impact.

17 WIND ENERGY↗

Scalable and Highly-Efficient Microbial Electrochemical Reactor for Hydrogen Generation from Wastes

The overall goal of this project was to develop a scalable and highly efficient hybrid microbial electrochemical reactor for hydrogen recovery from waste streams at a cost of less than $\$$2/kg H₂. The specific objectives were: (1) to design and fabricate a scalable and highly efficient microbial electrochemical cell (MEC) reactor, and (2) to determine the techno-economic feasibility of the system for H₂ generation from organic-rich waste streams. We achieved the first objective by (a) developing low-cost electrode materials, (b) synthesizing a highly efficient cathode catalyst in a scalable manner, (c) evaluating and validating the developed electrode material and catalyst in MEC reactors, and (d) designing and fabricating a larger reactor that incorporates (a) to (c). We met the second objective by (a) identifying the impacts of wastewater composition and operational conditions on H₂ production, and (b) developing a cost-performance model that identified critical parameters affecting the system's performance and cost, providing a pathway for further improvement.

08 HYDROGEN↗

Thermal Analysis Evaluations Using the Dry Cask Simulator

Different thermal analysis models were developed to simulate the dry cask simulator (DCS). The DCS is an experiment designed to simulate dry storage of a single boiling water reactor fuel assembly under a variety of heat loads and internal pressures. The DCS was set up and tested in both a vertical and horizontal configuration to determine cladding temperatures in vertical and horizontal dry cask storage systems. The models included a detailed STAR-CCM+ model with the fuel assembly geometry explicitly modeled, a porous STAR-CCM+ model with the fuel assembly geometry modeled as porous media region with calculated effective properties, and a COBRA-SFS model. COBRA-SFS is a thermal-hydraulic code developed for steady-state and transient analysis of multi assembly spent-fuel storage and transportation systems. STAR-CCM+ is a commercial computation fluid dynamics (CFD) code. Both a detailed and porous STARCCM+ model were developed to look at the effective thermal conductivity (keff) approach to modeling a fuel assembly. A keff fuel model is typically model in CFD thermal analyses due to its significantly lower computational costs. The models were run for a combination of low and high canister pressures (100 kPa and 800 kPa) and low and high internal heat loads (0.5 kW and 5 kW). Results from all three models were compared against experimental data taken from the DCS for the peak cladding temperature (PCT) and inlet air mass flow.

Suffield, Sarah R.↗