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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

Available Drawdowns for Each Oil Storage Cavern in the Strategic Petroleum Reserve (2022 Annual Report)

The Department of Energy maintains an up-to-date documentation of the number of available full drawdowns of each of the caverns owned by the Strategic Petroleum Reserve (SPR). This information is important for assessing the SPR's ability to deliver oil to domestic oil companies expeditiously if national or world events dictate a rapid sale and deployment of the oil reserves. Sandia was directed to develop and implement a process to continuously assess and report the evolution of drawdown capacity, the subject of this report. A cavern has an available drawdown if after that drawdown, the long-term stability of the cavern, the cavern field, or the oil quality are not compromised. Thus, determining the number of a vailable drawdowns requires the consideration of several factors regarding cavern and wellbore integrity and stability, including stress states caused by cavern geometry and operations, salt damage caused by dilatant and tensile stresses, the effect of enhanced creep on wellbore integrity, and the sympathetic stress effect of operations on neighboring caverns. A consensus has now been built regarding the assessment of drawdown capabilities and risks for the SPR caverns (Sobolik et al., 2014; Sobolik 2016). The process involves an initial assessment of the pillar-to-diameter (P/D) ratio for each cavern with respect to neighboring caverns. A large pillar thickness between adjacent caverns should be strong enough to withstand the stresses induced by closure of the caverns due to salt creep. The first evaluation of P/D includes a calculation of the evolution of P/D after a number of full cavern drawdowns. The most common storage industry standard is to keep this value greater than 1.0, which should ensure a pillar thick enough to prevent loss of fluids to the surrounding rock mass. However, many of the SPR caverns currently have a P/D less than 1.0 or will likely have a low P/D after one or two full drawdowns. For these caverns, it is important to examine the s tructural integrity with more detail using geomechanical models. Finite - element geomechanical models have been used to determine the stress states in the pillars following successive drawdowns. By computing the tensile and dilatant stresses in the salt, areas of potential structural instability can be identified that may represent "red flags" for additional drawdowns. These analyses have found that many caverns will maintain structural integrity even when grown via drawdowns to dimensions resulting in a P/D of less than 1.0. The analyses have also confirmed that certain caverns should only be completely drawn down one time. As the SPR caverns are utilized and partial drawdowns are performed to remove oil from the caverns (e.g., for occasional oil sales , purchases, or exchanges authorized by the Congress or the President), the changes to the cavern caused by these procedures must be tracked and accounted for so that an ongoing assessment of the cavern's drawdown capacity may be continued. A proposed methodology for assessing and tracking the available drawdowns for each cavern was presented in Sobolik et al. (2018). This report is the latest in a series of annual reports, and it includes the baseline available drawdowns for each cavern, and the most recent assessment of the evolution of drawdown expenditure for several caverns.

02 PETROLEUM↗
DOE OSTI2022

Module 5: End Use Assessment - Lesson 5.1: Introduction to End Use Assessment [Slides]

End use assessment is the process of performing a technical evaluation of the stated end use in relation to the requested commodity and the activities of the end user. An end use assessment requires sufficiently detailed information on the procurement request, commodity technical specifications, end use conditions, end user business activities, industry norms and trends, and WMD or delivery system applications of stated end use. Reviewers should be aware of red flags related to end use assessment. Use best practices when writing an end use assessment. Be familiar with technical resources for end use assessments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A Supply Chain Road Map for Offshore Wind Energy in the United States

"A Supply Chain Road Map for Offshore Wind Energy in the United States" identifies pathways to developing a domestic offshore wind supply chain that can manufacture and deploy the major components needed to set the United States on a pathway to installing 30 GW of offshore wind by 2030 and 110 GW by 2050. The report estimates that this supply chain could require an investment of at least $\$$22.7 billion this decade to meet an annual demand for components, ports, and vessels in 2030. Although this is a considerable investment, it could allow the industry to install around $\$$100 billion worth of offshore wind this decade by reducing risk of delays due to global supply chain bottlenecks and creating a robust network of assets that will continue to be effective well beyond 2030. The United States would need at least 34 manufacturing facilities employing 10,000 workers, 39,000 jobs in the supporting supply chain, 10 marshaling ports, 4-6 dedicated wind turbine installation vessels, 4-6 dedicated heavy-lift vessels, and 4-8 U.S.-flagged specialized feeder barges to come online this decade to support an average annual deployment of 4-6 gigawatts offshore wind capacity per year. This supply chain could be developed in 6-9 years, but would require near-term decision making and efficient permitting and planning to strategically develop these resources by 2030. Additional investment and expansion would be required in the 2030s as the sector expands into new regions (such as the Gulf of Mexico) and new technologies (such as larger wind turbines and floating wind energy projects). Furthermore, the planning process needs to meaningfully engage with communities that will be impacted by supply chain expansion to achieve just outcomes and maximize benefits to these stakeholders, which will result in a more equitable and sustainable supply chain. While U.S. offshore wind has made significant progress in recent years, remaining supply chain challenges include uncertainty surrounding deployment and procurement timelines; a lack of port and vessel infrastructure; and limitations in the available workforce, supporting supplier networks, and energy justice best practices. However, many of these problems can be addressed through improved communication between key stakeholder groups, support from federal and state governments, and forward-thinking designs of supply chain assets to accommodate future technology changes for fixed-bottom and floating offshore wind. Although it is a significant task, developing these domestic capabilities represents a once-in-a-generation opportunity to contribute to a decarbonized energy future and also create massive economic benefits that are distributed throughout the country.

17 WIND ENERGY↗
DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗
DOE OSTI2022

SRF Cavity Fault Classification and Prediction at Jefferson Lab

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. In additional to model performance, we also highlight challenges in working with real-world data and challenges for deploying models.

Tennant, C.↗
DOE OSTI2022

A Smart Alarm for the CEBAF Injector

We present initial results from a proof-of-concept ?smart alarm? for the CEBAF injector. Because of the injector's large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. Our approach leverages a trained neural network to not only identify an anomalous machine state, but also to identify the root-cause by pinpointing the specific element or region responsible. We developed an inverse model trained on data collected during normal operations. Using the inverse model, measurements from the machine are used to compute machine settings, which are then compared to EPICS setpoints. Instances when predictions differ from EPICS setpoints by a user-defined threshold are flagged as anomalies, and the user is alerted to the issue. We present the results of our data collection efforts, model training and performance, and initial performance metrics.

Tennant, C.↗
DOE OSTI2021

Present and future of JLab CLAS12 physics program

The CLAS12 detector at Jefferson Lab produced the first results in SIDIS and DI-HADRON reactions. Making use of the CEBAF high energy (up to 11 GeV) and highly longitudinal polartized (up to 90%) electron beam will cover unexplored territories in electron-scattering physics. Exclusive reactions on nuclons and nuclei will be measured with high precision in high luminosity (up to 10e35 cm-2s-1) experiments. Mapping out a new class of structure functions, GPDs and TMDs, detecting and reconstructing exotic meson and baryon states and accessing nucleons correlations in nuclei, in the next decade, the CLAS12 rich physics program will provide insight in the complex dynamics of the QCD paving the road to the EIC physics. In this talk, the CLAS12 detector and JLab Hall-B physics program will be described reporting some preliminary results for flag-ship reactions and plans for future upgrades of the detector.

Battaglieri, Marco↗
DOE OSTI2023

2023 Annual Report of Available Drawdowns for Each Oil Storage Cavern in the Strategic Petroleum Reserve

DOE maintains an up-to-date documentation of the number of available full drawdowns of each of the caverns at the U.S. Strategic Petroleum Reserve (SPR). This information is important for assessing the SPR’s ability to deliver oil to domestic oil companies expeditiously if national or world events dictate a rapid sale and deployment of the oil reserves. Sandia was directed to develop and implement a process to continuously assess and report the evolution of drawdown capacity, the subject of this report. This report covers impacts on drawdown availability due to SPR operations during Calendar Year 2022. A cavern has an available drawdown if, after that drawdown, the long-term stability of the cavern, the cavern field, or the oil quality are not compromised. Thus, determining the number of available drawdowns requires the consideration of several factors regarding cavern and wellbore integrity and stability, including stress states caused by cavern geometry and operations, salt damage caused by dilatant and tensile stresses, the effect of enhanced creep on wellbore integrity, and the sympathetic stress effect of operations on neighboring caverns. Finite-element geomechanical models have been used to determine the stress states in the pillars following successive drawdowns. By computing the tensile and dilatant stresses in the salt, areas of potential structural instability can be identified that may represent red flags for additional drawdowns. These analyses have found that many caverns will maintain structural integrity even when grown via drawdowns to dimensions resulting in a pillar-to-diameter ratio of less than 1.0. The analyses have also confirmed that certain caverns should only be completely drawn down one time. As the SPR caverns are utilized and partial drawdowns are performed to remove oil from the caverns (e.g., for oil sales, purchases, or exchanges authorized by the Congress or the President), the changes to the cavern caused by these procedures must be tracked and accounted for so that an ongoing assessment of the cavern’s drawdown capacity may be continued. A methodology for assessing and tracking the available drawdowns for each cavern is reiterated. This report is the latest in a series of annual reports, and it includes the baseline available drawdowns for each cavern, and the most recent assessment of the evolution of drawdown expenditures. A total of 222 million barrels of oil were released in calendar-year 2022. A nearly-equal amount of raw water was injected, resulting in an estimated 34 million barrels of cavern leaching. Twenty caverns have now expended a full drawdown. Cavern BC 18 has expended all its baseline available drawdowns, and has no drawdowns remaining. Cavern BM 103 has expended one of its two baseline drawdowns, and is now a single-drawdown cavern. All other caverns with an expenditure went from at-least-5 to at-least-4 remaining drawdowns.

02 PETROLEUM↗
DOE OSTI2023

Advanced Radiation Panel design for applications in National Security and Food Safety

We describe a new concept for a basic radiation detection panel based on conventional scintillator technology and commercially available solid-state photo-detectors. The panels are simple in construction, robust, very efficient and cost-effective and are easily scalable in size, from tens of cm 2 to tens of m 2 . We describe two possible applications: flagging radioactive food coontamination and detection of illicit radio nucleides, such as those potentially used in a terrorist attack with a dirty bomb.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

On the flow-stress model of Preston, Tonks, and Wallace - Observations, parameter constraints, and implementations (Rev. 1)

A detailed discussion about the PTW flow-stress model has been presented, including theoretical considerations, model calibrations to experimental data, and model implementations in LANL ASC hydro-codes. The main findings are: In order to reproduce the intended behavior of the PTW model at high strain rates, the following conditions on the model parameters need to be valid: y 1 ≥ s 0 and y 2 ≥ β ; When the conditions above are not satisfied, PTW and PTW_Mod1 have slightly different behavior in the phonon-drag regime, and should be treated as different models. PTW exhibits a discontinuity above the critical strain rate. PTW_Mod1 does not show such a discontinuity but implies a different strain-rate dependence at high strain rates. Examples of this behavior include older calibrations for stainless steel and depleted uranium; In the thermal-activation regime, some flexibility was introduced in the original PTW model to allow for different rate-dependent behaviors at intermediate strain rates. Example: copper. However, many material calibrations do not require this extra flexibility, and the complexity and ambiguity of the intended behavior can be partially blamed for the inconsistent model implementations across ASC codes; The limit for the material constant p going to zero is well defined and leads to a simplified flow-stress equation, discontinuity free; Modern calibrations of the PTW parameters are done using Impala. Constraints on the PTW parameters are enforced; Implementation of the PTW model in LANL ASC hydro-codes suffered of several shortcom ings. At the time of writing, FLAG and xRAGE contain consistent and correct implemen tations of the PTW and PTW_Mod1 models. Corresponding changes to Pagosa have been suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Validation Simulations for Multi-Component Mixture Model [Slides]

Set up and ran a large, 3D, 3 component Rayleigh-Taylor Mixing Simulation: Match initial conditions from; Run with multiple mesh sizes to demonstrate convergence to expected mixing layer growth rate. Wrote and tested new FLAG capability to enforce a specified temporal temperature profile.

42 ENGINEERING↗
DOE OSTI2022

AI Data Quality Monitoring with Hydra

Hydra is an extensible framework for training and managing AI for near real time monitoring that aims to replace the tedious and repetitive data quality monitoring activities the shift crew and online monitoring coordinator typically perform. It continuously scans incoming data in the form of monitoring plots for signs of problems, flagging them for human review. A web app was developed such that experts can efficiently label images for training. Labels are stored in a database for use in training and model validation. Backed up by a comprehensive database, it utilizes an additional web based front-end for viewing the current monitoring status from anywhere in the world. The system has been in production use for the GlueX experiment at Jefferson Lab for more than 2 years with new features still under active development.

Britton, Thomas↗
DOE OSTI2023

0BGRaman: Graph Network based Simulator for Forecasting Molecular Polarizability

This report presents the work performed under the GRaman project, sponsored by the PCSD LDRD Seed program. The project aimed at accelerating ab initio molecular dynamics simulation using Graph Networks. The Graph Network framework is a ML framework that has been successfully employed to simulate the dynamics of several physical systems: including water splashing in a container and flags moving with the wind. In this effort, we performed a data collection campaign for 3 different molecules of interest. We have built tools for preprocessing the trajectories obtained by simulating Raman Spectroscopy with NWChem and translating them into a suitable format for training. We have developed a training algorithm to train the Graph Network based simulators based on our data and developed a simulator that produces trajectories in the same NWChem format. While the tool has improved with each iteration of development and subsequent experiments, the current state of the tool does not allow to directly incorporate the technology within the NWChem framework because the trajectories produced by the tool are not yet accurate enough. However, the technology has proved to have good potential and it is certainly worth further research and development.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Hydro-Code Implementation and Testing of a Kinetic Phase Transition Framework

In this report we describe the Kinetic Phase Transition (KPT) framework that has been worked out over the last 10 years (from around 2014) and the implementation of it into three different codes, the one-dimensional hydro- LASLO and the three-dimensional magneto-hydro- ALEGRA, Sandia codes, via subroutines in the LAMBDA Equations of State and constitutive models package, and Flag, an arbitrary Lagrangian-Eulerian multiphysics code developed within the Lagrangian Applications project (LAP) at LANL. We discuss the introduction of phase mass (and/or volume) fractions that are needed in a code for it to be ‘phase aware’, that is, not only the thermodynamic state is known in each point but also the mixture of the materials’ phases in that point. Further we point to the need of a full Equations of State for each phase in a material to achieve phase awareness and we review the equilibrium phase model, where a phase mixture is at its lowest Gibbs free energy state, to make this point clear. Contrasting the kinetic phase transition to this equilibrium model seamlessly introduce us to the KPT framework that is subsequently thoroughly discussed. While the determination of the total state and the states and mass fractions of phases in each point is a problem that can borrow many of its numerical details from Eulerian codes and mixture of materials (not phases), the update of mass fractions with time in a KPT framework needs a new set of considerations. General for any update model is that we need to prevent mass fractions from becoming unphysical (negative or their sum to be larger than one). We have solved this problem by implementing a subdivision of the hydro time step that prevents the phase from being fully present to not present at all in one subdivided time step by limiting the size of the subdivided time step. This scheme also corrects numerical problems from abrupt changes in parameter values, the so called Gibbs phenomena, that gives rise to slushing between phases in the KPT framework. Interspersed throughout the report are discussions on different thermodynamics considerations. EOS validity windows, limitations on the EOS phase space, are needed for the KPT framework and are discussed separately and exemplified. The KPT framework described in this report has been verified by code comparison, but validation is still an active area of research. There is room for improvement in the update model, both in the model for determination of rates and in how to prevent the mass fractions from becoming unphysical. In addition, the parameters in the KPT update model and the placement of the phase boundary in the EOS phase space, and interactions with other constitutive models, are closely related and interfering with each other. One possible way forward is to simultaneously develop KPT parameters, EOS, and constitutive models for each material.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Reaction Evolution Flow Chart - The Critical Path to DDT [Slides]

Purpose (and limitations): The following flow-chart identifies and organizes a generalized, and simplified, sequence of necessary conditions for accidental ignition and reaction growth in a consolidated, polymer-bonded explosive (PBX) charge that has been insulted. A ‘yes’ answer to each question will lead down the critical path and produce violent, explosive reactions, up to and possibly including transition to detonation. Alternatively, any ‘no’ answer will serve as an off-ramp from the path resulting in less violent responses. It is important to note that this flow-chart is a high-level tool to flag potential operational vulnerabilities, but lacks the details needed to stand alone as a sole-source for risk determination. Each question represents a significant area of study and, therefore, the flow chart is intended to be supplemented by subject matter expert elicitations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Venado acceptance: results and tips [Slides]

Nvidia compiler support is not available through cray-mpich/compiler wrapper interface. Adjust CMAKE files to use the correct COMPILER_ID in conditionals and explicit variables to package flags. Use pinned host memory in cray-libsci_acc and cublasXt calls. Set a large blockDim for cublasXt calls. Try MPS and/or explicit numactl binding if performance is lackluster. Use CRAY_MALLOPT_OFF=1 if unexpected OOM errors appear using cce. Use MPICH_SMP_SINGLE_COPY_MODE=CMA for xpmem issues. Use MPICH_OPT_THREAD_SYNC=0 for MPI_THREAD issues. Poor CUDA-aware MPI performance remains an issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2024

Towards a NEAMS-based high-fidelity model of the MARVEL reactor

This report outlines the progress of Idaho National Laboratory in developing a high-fidelity and high-resolution model of the Microreactor Applications Research Validation and Evaluation reactor. The model was developed under the Nuclear Energy Advanced Modeling and Simulation microreactor application driver at Idaho National Laboratory. The overarching objective of this activity is the development of a high-fidelity multiphysics MARVEL model using NEAMS tools, and to verify and validate NEAMS tools against MARVEL reference simulation and experimental data, respectively. This is a unique opportunity to conduct multiphysics analysis on a soon-to-be-deployed microreactor. This multiphysics model developed under the NEAMS-funded INL microreactor application driver leverages three single-physics models coupled via the MOOSE’s MultiApp and Transfer systems. The latter systems enable in-memory data transfer between MOOSE-based and MOOSE-wrapped applications. The first single-physics model, that functions as main application, leverages Griffin to model the neutron transport in the core through the discontinuous finite element (DFEM) discrete ordinates solver (SN). Several optimization flags that were developed by the Griffin developer team were beta-tested to enhance the solver’s performance. These include the combined use of using_average_xs and update_averaged_xs_on that enable to avoid expensive on-the-fly cross sections evaluations at each linear iterations in favor of evaluations of the macroscopic cross sections at each Picard iteration. The second single-physics model uses BISON to handle solid heat transfer and asymptotic hydrogen redistribution analysis in the fuel. While the model returns consistent results for the temperature and hydrogen distribution in the fuel, a mismatch was noticed in the calculated temperature in the reflector due to the value of the gap conductance used in our model. Ongoing investigations are being performed to assess the origin of this discrepancy. Finally, the System Analysis Module (SAM) was used to model the flow of the sodium-potassium eutectic in the primary loop. A first verification was also performed showing good agreement in terms of mass flow rate and inlet temperature. All mesh files were generated using the MOOSE Reactor module, removing the need for external meshing tools. Notably, this workscope represents one of the initial applications of the MOOSE Reactor module for modeling highly irregular geometries. The use of the reactor module significantly streamlined the mesh generation process. The full multiphysics mode, that combines all the single physics models, was leveraged to conduct initial steady-state multiphysics simulations to compute power, and temperature distribution in the reactor. Initial testing was performed for transient simulations as well. In this case, the new checkpoint restart capability for eigenvalue calculations was tested showing the capability for streamlined restart of transient calculations. Future work will focus on improving the fidelity of the model by performing comprehensive code-to-code comparisons. For instance, the full-core Griffin neutronics model will be benchmarked against MCNP reference results, that were provided by the MARVEL design team. Additionally, the SAM T/H model will be verified against reference RELAP-5 results for selected accident scenarios. Besides code-to-code verification exercises, the model fidelity will be improved by replacing the single-channel SAM model with a more complex SAM-Pronghorn coupled model, in which the sub-channel capability is deployed to obtain radial temperature resolution in the coolant. This model will be developed in synergy with the NEAMS thermal hydraulics team.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗