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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Framework for Quantitative Evaluation of Resilience Solutions: An Approach to Determine the Value of Resilience for a Particular Site

The paper provides the approach to providing a benefit cost analysis of energy and water alternatives to provide resilience to extreme events. The approach estimates the costs and returns of providing greater resilience of water and energy infrastructure. Extreme events are defined as high impact, low-frequency events such as, but not limited to, hurricanes, floods, storm surges and earthquakes. The provides justification for hardening water and energy infrastructure. Resilience is defined as “the ability to prepare for and to withstand an extreme event with little or no damage, or to recover more quickly from an extreme event.” The approach can be summarized as follows. The approach requires the development of a baseline with which to compare alternatives. The baseline is used to evaluate the baseline’s resilience to hazards through the probability of the hazard(s), the likelihood of damage from that the hazard through a vulnerability analysis, and the consequence to calculate a cost of the damage. The approach then evaluates proposed mitigation alternatives that would improve the resilience of the system. Each alternative is evaluated based on probability of the hazard, probability of vulnerability and consequence to determine the reduced damage that each alternative presents. The approach includes any monetary and non-monetary benefits that can quantified for each of the alternatives. Non-quantifiable benefits are evaluated based on the relative importance of each alternative to the criteria used to determine how well the alternative meets the goals and objectives of the site/facility. Then, a life cycle cost analysis should be conducted for the baseline and alternatives. Finally, the results of the life cycle analysis should be presented in a decision matrix with cost, net present value, benefit/cost ratios, and any non-monetary criteria ranked to show how well the alternatives met the criteria, weighted with the decision maker’s weights and the results presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

CO 2 Storage prospeCtive Resource Estimation Excel aNalysis (CO 2 -SCREEN) User’s Manual: Python_V4.1

This user’s manual guides the use of the National Energy Technology Laboratory’s (NETL) CO 2 Storage prospeCtive Resource Estimation Excel aNalysis (CO 2 -SCREEN) tool, which was developed to aid users in screening geologic formations for prospective CO 2 storage resources. This manual is specific to the CO 2 -SCREEN 4.1 version which is based in Python. The 4.1 version of CO 2 -SCREEN adds in newly updated storage efficiency factors for saline formations. CO 2 -SCREEN applies U.S. Department of Energy (DOE) methods and equations for estimating prospective CO 2 storage resources for saline formations, shale formations, and residual oil zones (ROZ). CO 2 -SCREEN was developed to be substantive and user-friendly and provide a consistent method for calculating prospective CO 2 storage resources. CO 2 -SCREEN uses a Java- based graphical user interface (GUI) for data inputs and uses Python to calculate prospective CO 2 storage resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NETL CO2U openLCA LCI Database Version 2.1

The NETL CO2U openLCA LCI Database Version 2 is part of the NETL CO2U LCA Guidance Toolkit for Carbon Utilization funding recipients to meet their LCA requirements. The toolkit includes the following files: NETL CO2U LCA Guidance Document, NETL CO2U openLCA LCI Database, NETL CO2U openLCA Results Contribution Tool, NETL CO2U LCA Documentation Spreadsheet, and NETL CO2U LCA Report Template. The NETL CO2U openLCA LCI Database is an openLCA software files that contains data and an example system for funding recipients using openLCA to complete their LCA requirements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NETL CO2U LCA Documentation Spreadsheet

The NETL CO2U LCA Documentation Spreadsheet is part of the NETL CO2U LCA Guidance Toolkit for Carbon Utilization funding recipients to meet their LCA requirements. The toolkit includes the following files: NETL CO2U LCA Guidance Document, NETL CO2U openLCA LCI Database, NETL CO2U openLCA Results Contribution Tool, NETL CO2U LCA Documentation Spreadsheet, and NETL CO2U LCA Report Template. The NETL CO2U LCA Documentation Spreadsheet is available to funding recipients to help meet their LCA data documentation requirements, especially when using a spreadsheet model rather than LCA software to complete the LCA requirements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NETL CO2U LCA Guidance Toolkit - Version 2.1

The NETL CO2U LCA Guidance Toolkit is part of the NETL CO2U LCA Guidance Toolkit for Carbon Utilization funding recipients to meet their LCA requirements. The toolkit includes the following files: NETL CO2U LCA Guidance Document, NETL CO2U openLCA LCI Database, NETL CO2U openLCA Results Contribution Tool, NETL CO2U LCA Documentation Spreadsheet, and NETL CO2U LCA Report Template. Note: This zip file may take a few minutes to download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Incorporating new locations into a hydrodynamic model using ArcGIS Pro

ALGE3D is a 3-D hydrodynamic model developed at the Savannah River National Laboratory to predict pollutant dispersion. The model has several predefined locations incorporated, however adding new locations into ALGE3D is manually intensive and time-consuming. In the event of an emergency, ALGE3D can only be used if an event occurs at one of the predefined locations incorporated into the GUI. This project focuses on streamlining the integration of new locations into ALGE3D by utilizing tools in ArcGIS Pro. The Stream Order tool was used to delineate streams and assign them an order, using the Strahler method, versus manually defining these streams in Microsoft Excel. This work advances development to turn ALGE3D into a product for emergency response use by allowing the user to incorporate a new location into ALGE3D quickly, should an event occur at a location that is not already predefined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-Informed AI for Climate and Weather Risk Prediction (Final Report)

As part of the work Terrafuse developed 1) a model for wildfire risk in California, and 2) a model for downscaled wind fields from Numerical Weather Prediction (NWP) numerical models. The wildfire model is based on 20 years of historical data and captures the dependence of wildfire incidence and spread on climatic, weather and land-use variables by training a machine learning model. Nonlinear relationships between input features are learned and expressed by the model and model transparency allows features to be ranked and interpreted. The fire model is of use for accurately predicting real-time daily and long-term wildfire risk for use cases in energy and insurance. The downscaled wind model is a spatio-temporal deep learning model that emulates the influence of high-resolution variables on wind speed, allowing coarse-resolution operational NWP models to be accurately expressed on fine grids at high resolution, with application to wind energy and weather forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Review of Software Packages for Local Intense Precipitation Flood Modeling

This report documents the review of the hydrologic and hydraulic modeling approaches used to estimate local intense precipitation (LIP) flooding and their implementation in readily available simulation software packages. The review focused on a few representative simulation software packages because of the similarity of their underlying mathematical bases and the rainfall-runoff and hydraulic processes included. Their availability for use in estimating LIP flood hazards and for U.S. Nuclear Regulatory Commission staff review of these estimates at the nuclear power plant (NPP) site scale were also considered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Porting a fast-running wildfire plume model for use in national security and climate applications

In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Summary of Expansions and Updates in GREET ® 2022

The GREET ® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. GREET is a life cycle analysis (LCA) tool, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy systems. Argonne has expanded and updated the model in various sectors in GREET 2022, and this report provides a summary of the release.

25 ENERGY STORAGE↗
DOE OSTI2022

Model Coupling Through Learned Representations

Reliable climate predictions are important for making robust decisions in response to the changing climate. This project aims to reduce mis-modeling uncertainties arising from the representation of the land-atmosphere coupling in the Energy Exascale Earth System Model (E3SM) by using a machine learning approach. This approach will use an encoder-decoder architecture to represent the information that is developed in the land model and given to the atmosphere model. The simulated data will be taken from the E3SM simulation. However, the incorporation of observed data into the simulated dataset reduces mis-modeling uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

NETL UPGrants Addendum to the CO2U LCA Guidance Toolkit

The NETL UPGrants Addendum provides additional guidance and changes to the Carbon Dioxide Utilization Life Cycle Analysis Guidance for the U.S. DOE Office of Fossil Energy and Carbon Management, Version 2.0 to make it more applicable to vendors preparing life cycle analyses for the Utilization Procurement Grants program.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Science & Technology Review: Awards for Livermore Innovation

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review (S&TR) is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. In 2021, Lawrence Livermore technologies received three R&D 100 awards, which honor the year’s top 100 innovations from around the world. The feature article beginning on p. 4 describes the Laboratory’s winners: the Multiplicity Counter for Thermal and Fast Neutrons, Flux, and the Optical Transconductance Varistor. The 2021 awards raised the Laboratory’s total R&D 100 awards to 173 since 1978. The R&D 100 logo is reprinted in this issue with permission from R&D World magazine.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Progress Report on Model Development for the Transport of Aerosol through Microchannels

This report summarizes the current progress in the development of a phenomenological model of aerosol transport, deposition, and plugging through microchannels. The purpose is to introduce a generic, reliable numerical model for the prediction of aerosol transport, deposition, and plugging in leak paths while accounting for potential plugging formation, to a user community involving researchers, regulators, and industry. In that regard, a Graphical User Interface (GUI) was generated by integrating the individual MATLAB scripts that make up the model and adding additional features to aid the general understanding of the problem to a user. This report focuses on the model development and features included. Further, predictions from the GUI are compared with experimental data for validation. The strength of the GUI is the ability of a user to plug in basic parameters such as the initial pressure conditions and canister model specifications in order to obtain a first principal approximation of vital information such as the blowdown pressure differential, aerosol penetration, and deposition as a function of time. The user can obtain this without the know-how of the underlying MATLAB scripts and thus enables the model to be readily applied by regulators, industry, and shareholders to reduce the uncertainty in the off-site radiological consequences. The report also lists future tasks under this work scope. This includes ongoing efforts to include additional crack geometries (divergent and divergent-convergent slots) to the model; GUI development and improvement based on feedback from users, and integration of aerosol source term data from Sibling Pin tests into the model as we approach realistic canister stress corrosion cracking–induced aerosol release scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating the 3D PBL scheme for wind energy applications in the complex terrain of Altamont Pass, CA

In this study, the 3D PBL scheme, developed and tested during previous WETO projects, was used to simulate daily speedup flows in the Altamont Pass Wind Resource Area of California. These flows were observed as part of the WFIP2 companion experiment HilFlowS, which examined potential power output from taller turbines in the Altamont region. The regularity of the observed speedup events during the summer months, combined with the importance of terrain-induced wind acceleration, makes them a useful case study for evaluating mesoscale forecast models, which often underperform in regions of complex terrain. Compared to a traditional one-dimensional PBL scheme, the 3D PBL scheme shows evidence of improved wind speed predictions, including an ability to capture atypical wind speed profiles and reduce model overestimates of wind speed across the turbine rotor region. Such improvements will be necessary for wind farm siting and power forecasting, especially as taller turbines are deployed in complex terrain.

17 WIND ENERGY↗
DOE OSTI2022

Understanding the Physics Representation of Deep Learning Models in Environmental Applications

Deep learning (DL) models have been popular in earth and environmental modeling and analysis, which exhibit huge potential in capturing and reconstructing the non-linearity of relevant environmental processes. They are extensively used as analytical tools or emulators for multiple domains (atmosphere, land surface, ocean, and biogeochemistry). Despite their success, their internal working mechanism remains largely unknown. Such a lack of knowledge hinders the identification of physically consistent models that are fully adaptive to non-stationary climate, as well as the development of physics-informed machine learning such as physics-informed neural network (PINN). To establish preliminary knowledge and framework of such physics representation evaluation, this project focuses on an improved understanding of DL models in the environmental applications. DL models are increasingly applied to environmental modeling and prediction. However, they have been evaluated mostly from a performance perspective, and there is a gap in understanding how they represent the known physics internally. Such knowledge is especially critical when applying DL models under climate change conditions, where new inputs are likely outside the ranges of the training datasets. In this project, we reveal how the known physical processes are represented within DL models from both statistical and mechanistic perspectives. Leveraging the traditional model evaluations that focus more on the accuracies of predictions, we establish a framework that examines both the accuracy and physics representation of DL models. This analysis framework can identify DL models that make the correct predictions based on correct physics, thus enhancing the existing explainable artificial intelligence (explainable-AI) portfolio. It lays a foundation for developing novel metrics to evaluate the emerging DL models in environmental applications. This knowledge also informs the development of physics-informed DL models by revealing the direct connections between the known physical processes and specific model components or structures.

54 ENVIRONMENTAL SCIENCES↗