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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Material-Dependent Antagonistic Effects between Soot and ZDDP

While soot in engine oil is known to accelerate the wear of diesel and gasoline direct-injection compression-ignition engines, there is a lack of consensus on the wear mechanism though various hypotheses have been proposed in the literature. Particularly, some recently observed antagonistic effects between soot and a common lubricant antiwear additive, zinc dialkyldithiophosphate (ZDDP), while others did not. The discrepancy is, in part, explained by the strong alloy dependence of such antagonism discovered in this study. Specifically, four alloys, 52100 steel and M2, M50, and A2 tool steels, are tested in lubricants containing carbon black (CB, a soot surrogate) with and without ZDDP present. Adding the CB alone to the oil increases the wear rate for all steel alloys as expected. However, distinct wear performance is observed for the four steel alloys when ZDDP is introduced to the CB-containing oil: while the 52100 steel has notable wear reduction, the three tool steels suffer significant wear increase. Comprehensive tribofilm characterization suggests that the Mo content in the steel alloy and the sulfur from ZDDP strongly influence wear behavior. The combination of CB/Mo-catalyzed sulfidation and CB-accelerated abrasion is hypothesized to be responsible for the high wear of the Mo-alloyed tool steels.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Transportability of exogenous microbial community correlates with interwell connectivity in deep aquifers

Subsurface resource engineering operations often utilize continuous injection of externally-sourced water into geological reservoirs for formation pressure maintenance, resource recovery or energy/waste storage. Such injected water generally contains naturally occurring microbes. Little is known, however, about how the injectate microbes transport through geological media as a community, how such transportability is affected by injector-producer connectivity, and whether such knowledge can be utilized for flowpath characterization. In this study, we analyzed daily-to-weekly timeseries microbial community data from the injected- and produced-fluids of a ten-month flow test at a deep, well-characterized engineered aquifer. We found that the injectate microbial community was distinct from the indigenous community at the amplicon sequence variant (ASV) level, and that the transportability of injectate community towards a given producer, quantified by an “nASV-Overlap” metric we propose, had strong and significant positive correlation with known injector-producer connectivities at our site. This suggests that the better the connectivity, the higher the probability for more injectate species to flow through the interwell region and arrive at a producer. Because interwell connectivity is an important yet usually unknown parameter in subsurface resource engineering, such correlation in turn points to nASV-Overlap as a useful indicator of interwell connectivity for aquifer characterization and long-term monitoring. Based on our findings, an nASV-Overlap-based microbial tracing approach was developed for characterizing and monitoring the relative connectivities across multiple producers with a given injector. A side-by-side comparison between the new nASV-Overlap approach and traditional artificial tracer methods is presented, and their respective strengths and limitations are discussed.

Deep biosphere↗
DOE OSTI2023

Towards elastic in situ analysis for high-performance computing simulations

In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing

A long-lived central engine embedded in expanding supernova ejecta can alter the dynamics and observational signatures of the event, producing an unusually luminous, energetic, and/or rapidly evolving transient. We use 2D hydrodynamics simulations to study the effect of a central energy source, varying the amount, rate, and isotropy of the energy deposition. We post-process the results with a time-dependent Monte Carlo radiation transport code to extract observational signatures. The engine excavates a bubble at the centre of the ejecta, which becomes Rayleigh–Taylor unstable. Sufficiently powerful engines are able to break through the edge of the bubble and accelerate, shred, and compositionally mix the entire ejecta. The breakout of the engine-driven wind occurs at distinct rupture points, and the outflowing high-velocity gas may eventually give rise to radio emission. The dynamical impact of the engine leads to faster rising optical light curves, with photon escape facilitated by the faster expansion of the ejecta and the opening of low-density channels. For models with strong engines, the spectra are initially hot and featureless, but later evolve to resemble those of broad-line Ic supernovae. Under certain conditions, line emission from ionized, low-velocity material near the centre of the ejecta may be able to escape and produce narrow emission similar to that seen in interacting supernovae. We discuss how variability in the engine energy reservoir and injection rate could give rise to a heterogeneous set of events spanning multiple observational classes, including the fast blue optical transients, broad-line Ic supernovae, and superluminous supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Non‐Traditional Positively‐Biased Narrow‐Band Perovskite Single‐Crystal Photodetectors Enabled by Interfacial Engineering

Abstract The positive bias in theory narrows down the depletion region and thus results in significant charge injection, which should be detrimental to charge generation and collection performance for traditional photodetectors. Here, instead, it is found that the external quantum efficiency (EQE) is increased by more than 50 times when the photodetector is positively biased. A positive bias of +6 V drives ion migration of Br − and Cs + towards the anode and cathode, respectively, leading to self‐doping within bulk single crystals to form an advantageous p‐i‐n junction for better charge collection in the devices. Meanwhile, the injected holes are allowed to tunnel through the cesium lead bromide/fullerene interface to reach the cathode which also significantly contributes to the enhancement of EQE in the forward‐biased devices. The positively‐biased narrow‐band (full width at half maxima (FWHM) = 16 nm) photodetectors exhibit a specific detectivity of 6.5 × 10 10 Jones at 550 nm, along with the −3 dB cutoff frequency of 2776 Hz. By manipulating charge injection and ion migration using interfacial engineering, a class of non‐traditional, positively‐biased, and highly narrow‐band photodetectors is demonstrated, which offers an alternative design strategy for imaging, biosensing, automatic control, and optical communication.

Li, Junchi↗
DOE OSTI2024

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

58 GEOSCIENCES↗
DOE OSTI2023

Commercialization of a Comprehensive Spark-Ignition Model for Automotive Engine Applications in CONVERGE CFD

The work completed under this CRADA commercializes an advanced hybrid Lagrangian-Eulerian spark-ignition (LESI) model for multi-dimensional engine simulations. In this report, LESI is coupled with combustion models (WSR, TFM, G-equation) to model spark channel elongation and kernel and flame development in a constant volume combustion vessel, and a four-valve direct-injection spark-ignition (4VDISI) engine in two operating conditions: stoichiometric E30 with no dilution, and stoichiometric PACE-20 with dilution using CONVERGE CFD solver.

42 ENGINEERING↗
DOE OSTI2022

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO 2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

NETL Well Integrity Workshop: Identifying Well Integrity Research Needs for Subsurface Energy Infrastructure

Wells are a critical component of subsurface energy infrastructure. Ensuring the integrity of wells as engineered pathways for the safe extraction, injection, and storage of fluids in the subsurface is key to maximizing the effectiveness and resilience of that infrastructure. Addressing well integrity issues in a technically robust manner that promotes environmental sustainability and social equity is also an important focus of the United States (U.S.) Department of Energy’s (DOE) Office of Fossil Energy and Carbon Management. Industry best practices, regulatory standards, modern monitoring data acquisition and control systems, and decades of research and development have dramatically improved the performance and reliability of wells for hydrocarbon extraction and underground injection in the oil and gas industry. Yet, important innovation is required to improve and ensure well integrity performance in engineered geologic systems where operational environments (fluid composition, temperature, pressure, and/or stress conditions) and long functional life cycles of well systems present unique challenges. Additionally, work is needed to understand and manage the long-term integrity and risks associated with legacy wells—especially those located adjacent to and presenting hazards for new subsurface activity.

02 PETROLEUM↗
DOE OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗
DOE OSTI2021

Findings from the NETL Well Integrity Workshop: Insights for multiple subsurface energy operations

Wells are a core aspect of subsurface energy infrastructure. Ensuring the resilience of that infrastructure relies on the integrity of wells as engineered pathways that safely extract, inject, and store fluids in the subsurface. To identify areas where focused research can yield new insights to improve integrity and extend the operational life of wells, the United States (US) Department of Energys (DOE) National Energy Technology Laboratory (NETL) organized and hosted a workshop on well integrity research needs in May and June of 2021. The three-day workshop consisted of six technical sessions and 18 presentations from international well integrity experts on topics of geologic storage of carbon dioxide, natural gas, and hydrogen, as well as hydrocarbon and geothermal energy production. There were over 350 registrants from more than 10 countries, 4 major oil & gas companies, 14 US states, 8 US DOE National Laboratories, US Departments of Interior, Transportation, and Energy, and regulators from eight of ten US Environmental Protection Agency (EPA) regions and US EPA headquarters. Through presentations, moderated discussions, and an attendee survey several important areas for future research were identified. In this presentation, we highlight the findings of the NETL well integrity workshop and discuss future well integrity research needs.

carbon capture utilization and storage (CCUS)↗
DOE OSTI2022

PERLE A ‘STEPPING STONE’ FOR THE NEXT GENERATION ERLS

PERLE (Powerful ERL for Experiments) is a novel Energy Recovery Linac (ERL) test facility, which has been designed to validate choices for a 50 GeV ERL foreseen in the design of the LHeC and the FCC-eh, and to later serve a set of dedicated nuclear and particle physics experiments. Its main thrust is to probe high current, continuous wave (CW), multi-pass operation with superconducting cavities at 802 MHz (and perhaps testing other frequencies of interest). With very high transient beam power (~10 MW), PERLE offers an opportunity for controllable study of every beam dynamic effect of interest in the next generation of ERL design and become a ?stepping stone? between present state-of-art 1 MW ERLs and future 100 MW scale applications. PERLE design features flexible momentum compaction lattice architecture for six vertically stacked return arcs and a high current, 7 MeV, photo-injector. With only one pair of four cavity cryomodules, 500 MeV beam energy can be reached in three re-circulation passes, with beam currents of about 20 mA. The beam is decelerated in three consecutive passes back to the injection energy. Work on the engineering design of PERLE has just begun.

Bogacz, S.↗
DOE OSTI2024

Design and Engineering of LUPIN: A Test-Bed Radiofrequency Ion Source for Enhanced Neutral Beam Injection on DIII-D

The Large, Uniform Plasma for Ionizing Neutrals (LUPIN) is an RF inductively coupled plasma (ICP) chamber for demonstrating plasma performance of an RF ICP positive ion source upgrade for the DIII-D Neutral Beam Injection (NBI) system. LUPIN will be used to investigate ion source physics, including neutral gas dynamics, plasma density uniformity, interactions with Faraday shields, and power coupling to novel RF antenna designs. LUPIN has an RF generator capable of delivering 20 kW of power at 2 MHz, which is coupled into a cylindrical quartz vessel measuring 20 cm in length and 10 cm in radius. This configuration matches the power density requirements for a full-scale ion source. Target hydrogen and deuterium plasma densities exceeding 10 18 m -3 would relate to extracted ion current densities of 2100 A/m 2 for 10 s. Vacuum conductance and gas flow calculations predict a maximum achievable neutral gas flow rate of 1675 Pa·L/s at 5 Pa of He, which mimics the gas flow of the DIII-D NBI system. Designs have been developed for an internal Faraday shield to mitigate heat flux and ion sputtering on the dielectric vessel. Thermomechanical finite element simulations demonstrated the Faraday shield design to be capable of withstanding anticipated heat loads from worst-case operation scenarios. Results of upcoming experimental investigations on LUPIN will guide the design of a full-scale prototype for DIII-D integration.

Faraday shield↗
DOE OSTI2022

Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor

Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.

42 ENGINEERING↗
DOE OSTI2020

Capacitor Voltage Ripple Estimation and Optimal Sizing of Modular Multi-Level Converters for Variable-Speed Drives

This article deals with the sizing of submodule capacitors for modular multilevel converters (MMCs) used for variable speed drives for medium voltage and high voltage electric machines. Variable speed drives are required to run over a wide frequency range from zero to a few hundred Hz or even over 1 kHz. Under low frequency operation (<30 Hz), a large current ripple flowing through the MMC submodule capacitors will cause significant capacitor voltage fluctuation. This article proposes an optimal submodule capacitor selection method based on the common-mode voltage injection. The proposed sizing model includes two functions: 1) accurately estimating the capacitor voltage ripple based on the drive system parameters; and 2) identifying the minimum capacitance of the submodule capacitors based on the allowed maximum capacitor voltage fluctuations. Simulation and experimental results show that the error between the actual and estimated values of the voltage fluctuation is within 3% using the proposed model.

42 ENGINEERING↗