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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Generation of a highly attenuated strain of Pseudomonas aeruginosa for commercial production of alginate

Alginate is an important polysaccharide that is commonly used as a gelling agent in foods, cosmetics and healthcare products. Currently, all alginate used commercially is extracted from brown seaweed. However, with environmental changes such as increasing ocean temperature and the increasing number of biotechnological uses of alginates with specific properties, there is an emerging need for more reliable and customizable sources of alginate. An alternative to seaweed for alginate production is Pseudomonas aeruginosa, a common Gram-negative bacterium that can form alginate containing biofilms. However, P. aeruginosa is an opportunistic pathogen that can cause life-threatening infections in immunocompromised patients. Therefore, we sought to engineer a non-pathogenic P. aeruginosa strain that is safe for commercial production of alginate. Using a homologous recombination strategy, we sequentially deleted five key pathogenicity genes from the P. aeruginosa chromosome, resulting in the marker-free strain PGN5. Intraperitoneal injection of mice with PGN5 resulted in 0% mortality, while injection with wild-type P. aeruginosa resulted in 95% mortality, providing evidence that the systemic virulence of PGN5 is highly attenuated. Importantly, PGN5 produces large amounts of alginate in response to overexpression of MucE, an activator of alginate biosynthesis. The alginate produced by PGN5 is structurally identical to alginate produced by wildtype P. aeruginosa, indicating that the alginate biosynthetic pathway remains functional in this modified strain. The genetic versatility of P. aeruginosa will allow us to further engineer PGN5 to produce alginates with specific chemical compositions and physical properties to meet different industrial and biomedical needs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗
DOE OSTI2022

Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.

cyber-physical security↗
DOE OSTI2023

A microCT-based platform to quantify drug targeting

Abstract Background Heterotopic ossification (HO) is a frequent and debilitating complication of traumatic musculoskeletal injuries and orthopedic procedures. Prophylactic dosing of botulinum toxin type A (BTxA) holds potential as a novel treatment option if accurately distributed throughout soft-tissue volumes where protection is clinically desired. We developed a high-resolution, microcomputed tomography (microCT)-based imaging strategy to assess drug distribution and validated this platform by quantifying distribution achieved via a prototype delivery system versus a single-bolus injection. Methods We injected an iodine-containing contrast agent (iodixanol 320 mg I/mL) into dissected rabbit musculature followed by microCT imaging and analysis. To contrast the performance of distributed versus bolus injections, a three-dimensional (3D) 64-cm 3 -printed soft-tissue holder was developed. A centered 2-cm 3 volume of interest (VOI) was targeted with a single-bolus injection or an equal volume distributed injection delivered via a 3D-printed prototype. VOI drug coverage was quantified as a percentage of the VOI volume that was < 1.0 mm from the injected fluid. Results The microCT-based approach enabled high-resolution quantification of injection distribution within soft tissue. The distributed dosing prototype provided significantly greater tissue coverage of the targeted VOI (72 ± 3%, mean ± standard deviation) when compared to an equal volume bolus dose (43 ± 5%, p = 0.031) while also enhancing the precision of injection targeting. Conclusions A microCT-based imaging technique precisely quantifies drug distribution within a soft-tissue VOI, providing a path to overcome a barrier for clinical translation of prophylactic inhibition of HO by BTxA. Relevance statement This platform will facilitate rapid optimization of injection parameters for clinical devices used to effectively and safely inhibit the formation of heterotopic ossification. Key points • MicroCT provides high-resolution quantification of soft-tissue drug distribution. • Distributed dosing is required to maximize soft-tissue drug coverage. • Imaging platform will enable rapid screening of 3D-printed drug distribution prototypes. Graphical Abstract

Ausk, Brandon J. (ORCID:0000000335338303)↗
DOE OSTI2024

Dynamic modeling studies of basin-scale pressure interference and CO 2 plume evolution in multi-well geologic CO 2 storage

This research employs numerical modeling to analyze how CO 2 plumes and pressure fronts evolve when CO 2 is injected into a single storage formation from multiple projects located in proximity. Here, this analysis also evaluates the extent to which injection well spacing alleviates pressure buildup in the absence of active pressure management tactics. The simulation approach was based on a single, homogenous saline aquifer in which CO 2 injection occurs under a one-injector baseline case and several multi-well cases where well spacing varies. Analysis results show that the extent of pressure buildup is in the range of tens or a few hundreds of kilometers and contingent upon the defining pressure buildup demarcating the front edge. For the geological setting evaluated in this paper, our analysis suggests that without active basin pressure management strategies, commercial-scale projects would likely need to be sited far apart to avoid pressure interference from one another. Analysis results show the radius of CO 2 plume varies approximately from 2 to 3 km from injection wells (each injecting 1 Mt/year for 30 years) depending on cases and modeling parameters assumed. Given the pressure interference, this paper thus draws attention to the importance of greater coordination among storage operators and regulatory stakeholders. Because this analysis assumes a very specific geologic setting, this exploratory analysis bears further investigations across other geologic scenarios.

42 ENGINEERING↗
DOE OSTI2024

EBIS Beam Intensity Online Optimization with GPTune and Offline Analysis with XGBoost

The application of machine learning techniques to accelerator research has led to significant breakthroughs in optimization strategies. This paper presents a pioneering study using a novel machine learning algorithm, GPTune, to optimize beam intensity by adjusting parameters in the EBIS injection and extraction beam lines. Our research demonstrates substantial improvements, achieving a remarkable 22% and 70% increase in beam intensity at two separate measurement locations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

An approach to sulfate geoengineering with surface emissions of carbonyl sulfide

Sulfate geoengineering (SG) methods based on lower stratospheric tropical injection of sulfur dioxide (SO 2 ) have been widely discussed in recent years, focusing on the direct and indirect effects they would have on the climate system. Here a potential alternative method is discussed, where sulfur emissions are located at the surface or in the troposphere in the form of carbonyl sulfide (COS) gas. There are two time-dependent chemistry–climate model experiments designed from the years 2021 to 2055, assuming a 40 Tg-Syr -1 artificial global flux of COS, which is geographically distributed following the present-day anthropogenic COS surface emissions (SG-COS-SRF) or a 6 Tg-Syr -1 injection of COS in the tropical upper troposphere (SG-COS-TTL). The budget of COS and sulfur species is discussed, as are the effects of both SG-COS strategies on the stratospheric sulfate aerosol optical depth (~Δτ=0.080 in the years 2046–2055), aerosol effective radius (0.46 µm), surface SO x deposition (+8.9 % for SG-COS-SRF; +3.3 % for SG-COS-TTL), and tropopause radiative forcing (RF; ~-1.5 W m –2 in all-sky conditions in both SG-COS experiments). Indirect effects on ozone, methane and stratospheric water vapour are also considered, along with the COS direct contribution. According to our model results, the resulting net RF is –1.3 W m –2 , for SG-COS-SRF, and –1.5 W m –2 , for SG-COS-TTL, and it is comparable to the corresponding RF of –1.7 W m –2 obtained with a sustained injection of 4 Tg-Syr -1 in the tropical lower stratosphere in the form of SO 2 (SG-SO2, which is able to produce a comparable increase of the sulfate aerosol optical depth). Significant changes in the stratospheric ozone response are found in both SG-COS experiments with respect to SG-SO2 (~5 DU versus +1.4 DU globally). According to the model results, the resulting ultraviolet B (UVB) perturbation at the surface accounts for –4.3 % as a global and annual average (versus –2.4 % in the SG-SO2 case), with a springtime Antarctic decrease of –2.7 % (versus a +5.8 % increase in the SG-SO2 experiment). Overall, we find that an increase in COS emissions may be feasible and produce a more latitudinally uniform forcing without the need for the deployment of stratospheric aircraft. However, our assumption that the rate of COS uptake by soils and plants does not vary with increasing COS concentrations will need to be investigated in future work, and more studies are needed on the prolonged exposure effects to higher COS values in humans and ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Prediction and Analysis of Utah FORGE Injection Activities using a Coupled Thermo-hydro-mechanical and Earthquake (THM+E) Modeling Workflow

A coupled thermo-hydro-mechanical (THM) numerical workflow that is capable of modeling seismic slip is critical for the successful development of enhanced geothermal systems (EGS). By integrating key physical processes, this workflow enables accurate simulation of temperature and pressure diffusions, stress changes, and induced seismicity. As a result, it serves as a vital tool for predicting induced seismicity and optimizing reservoir stimulation strategies. The Utah FORGE (Frontier Observatory for Research in Geothermal Energy) project, located near Milford, Utah, is a U.S. Department of Energy initiative aimed at advancing EGS technology. In April 2024, eight new stimulation stages (Stages 3R-10) were conducted in well 16A (injection well) subsequent to the first series of stimulation (Stages 1-3) performed in April, 2022. To monitor the induced seismicity, geophones were deployed in wells 58-32, 56-32, and 78B-32, while fiber optic cables were also installed in wells 16B, 78-32, and 78B-32 to collect microseismic data and detect frac hits Preliminary analyses of microseismic catalogs and fiber optic data suggest that the stimulated fractures in Stages 3R–6 closely align with that generated during Stage 3, indicating that the new stimulations were likely reactivating the previously stimulated fracture. To better understand the underlying process, a comprehensive modeling approach that can accurately capture thermal, hydrological, mechanical, and seismic responses is essential. In this work, we propose and utilize a coupled thermo-hydro-mechanical and earthquake (THM+E) simulation workflow to numerically investigate the stimulation activities on well 16A. The specific objective is to confirm whether the new stimulation stages (Stages 3R–6) reactivated fractures previously stimulated during Stage 3. For this purpose, we perform THM+E simulations individually for Stages 3, 3R, 4, and 5, incorporating the discrete fracture networks (DFNs) created by the plane-fitting technique based on the microseismic catalogs. The simulation workflow consists of two separate models: a THM model and an earthquake model, coupled in a one-way manner. Detailed descriptions of the workflow are provided in Section 3. Simulation results are presented in terms of injection pressure, permeability evolution, and predicted seismic catalogs, which are then compared with field data for further analyses. This report is structured as follows. In Section 2, we present detailed analyses of the field data and propose the hypothesis that the new stimulation stages (Stages 3R–6) were probably reactivating the previously stimulated fractures in Stage 3. In Section 3, we introduce the coupled THM+E workflow and the problem setup to validate our hypothesis, followed by the simulation results for each stage in Section 4. Meanwhile, discussions are included to analyze the model predictions and their comparison with field data. Lastly, we conclude the report and outline future plans in Section 5.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Potential Effects of Climate Change and Solar Radiation Modification on Renewable Energy Resources

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Kumler, Andrew [National Renewable Energy Laborato↗
DOE OSTI2020

Heliostat Aiming Strategies for Arbitrary Flux Profiles for High Temperature Gas-Based Receivers

High temperature gas-based receivers are seen as one potential path toward achieving the next generation of concentrated solar power tower systems. A key challenge associated with high temperature, high pressure heat transfer fluids is the lifetime of receiver containment materials. Achieving the desired lifetime while also designing an efficient receiver requires implementation of unique flux profiles, tailored to place high fluxes at cold fluid injection points. An aimpoint selection methodology was developed in the present study to allow user-defined flux profiles to be prescribed for a receiver. The methodology was implemented in NREL's open-source software SolarPILOT, modifying the existing “image size priority” aimpoint strategy. A case study for a tubular gas-based receiver is presented, examining the effect of various parameters on the ability to replicate the desired flux profile.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Dual interfacial H-bonding-enhanced deep-blue hybrid copper–iodide LEDs

Solution-processed light-emitting diodes based on non-toxic copper–iodide hybrids are a compelling solution for efficient and stable deep-blue lighting, owing to their tunability, high photoluminescence efficiency and environmental sustainability. Here we present a hybrid copper–iodide that shows near-unity photoluminescence quantum yield (99.6%) with an emission wavelength of 449 nm and colour coordinates (0.147, 0.087), alongside its emission mechanism and charge transport characteristics. Here, we use the thin film of this hybrid as the sole active emissive layer to fabricate deep-blue light-emitting diodes and subsequently enhance the device performance through a dual interfacial hydrogen-bond passivation strategy. This synergetic surface modification approach, integrating a hydrogen-bond-acceptor self-assembled monolayer with an ultrathin polymethyl methacrylate capping layer, effectively passivates both heterojunctions of the copper–iodide hybrid emissive layer and optimizes charge injections. We achieve a maximum external quantum efficiency of 12.57%, a maximum luminance of 3,970.30 cd m −2 with colour coordinates (0.147, 0.091) and an excellent operational stability (half-lifetime) of 204 hours under ambient conditions. We further showcase a large-area device of 4 cm 2 that maintains high efficiency. Our findings reveal the potential of copper–iodide-based hybrid materials for applications in solid-state lighting and display technologies, offering a versatile strategy for enhancing device performances.

14 SOLAR ENERGY↗
DOE OSTI2025

FLEX-FUEL MIXING CONTROLLED COMBUSTION ENABLED BY PRECHAMBER IGNITION

There is an imminent need to displace fossil diesel fuel with cleaner burning, domestically produced, renewable fuels for use in heavy-duty engines. Bioethanol is a prime candidate as it widely adopted in the U.S. as a gasoline additive ranging in volume percentage from 10% (E10) up to 85% (E85). Direct substitution of market available ethanol-gasoline blends for diesel fuel is not plausible as the stark reactivity differences would not constitute the same ignition quality nor achieve auto-ignition at all. This work focuses on the development of prechamber enabled mixing-controlled combustion (PC-MCC) as an advanced combustion strategy to facilitate reliable ignition and diffusion style combustion ethanol-gasoline fuel blends. PC-MCC involves integration of an actively fueled prechamber (PC) into a conventional compression ignition combustion system. When ignited, the PC ejects hot turbulent jets into the main combustion chamber that then interact with the direct injected fuel, prompting immediate ignition. The PC jet flames provide a robust thermal ignition source that allows the engine to operate agnostic of fuel composition, or flex-fuel. Computational fluid dynamics (CFD) modeling was used to assess critical design features of the PC while garnering insights into the ignition strategies that facilitate robust performance. A key finding was the ignition performance benefits of fuel-rich PC operation which yield exothermic jets. Based on the numerical findings, a prototype igniter was tested experimentally on both single and multi-cylinder engine platforms at a variety of operating conditions. The experimental results indicate flex-fuel PC-MCC is well capable of diesel-like combustion processes by demonstrating matched or improved gross thermal efficiencies and load variability within 2%. Fuel grade ethanol (E98) exhibited consistently lower NOx and immeasurable soot across the load space. E98 also demonstrated a significant improvement in thermal efficiency at light loads.

Zeman, Jared↗
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

Frequency Response Improvement in a Standalone Small Hydropower Plant Using Battery Storage

This paper proposes a control architecture for frequency, current, and voltage control that facilitates using battery storage to improve the response of standalone small hydropower plants. The frequency controller uses rate-of-change of frequency and frequency-Watt-based generations to produce active power commands. The distinctive feature of the controller design is that it nicely integrates response to frequency change with constraints on frequency and state of battery to enable power injections. The current and voltage control scheme allows incorporating the frequency controller. The distinctive feature of this controller is that it incorporates a bounded integral control strategy that guarantees stability. Results on the stability of the hydropower plant with proposed scheme are presented and robust ways to choose the controller gains are investigated via root locus analysis. In conclusion, simulations performed show that: the hydropower plant response is significantly improved with battery storage using the proposed scheme; the load carrying capability of the hydropower plant is significantly improved with battery storage; the proposed scheme has the capability to recharge the battery; and the proposed control scheme gives improved performance.

13 HYDRO ENERGY↗
DOE OSTI2025

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Seamless Transition of Critical Infrastructures using Droop Controlled Grid-forming Inverters

Seamless recovery of power to critical infrastructures, after grid failure, is a crucial need arising in scenarios that are increasingly becoming more frequent. Here, this article proposes a seamless transition strategy using a single and unified mode-dependent droop controlled grid-forming inverters. The control strategy achieves the following objectives: 1) regulates the output active and reactive power by the droop-controlled inverters to a desired value while operating in on-grid mode; 2) seamless transition and recovery of power injections into the load after grid failure by inverters that operates in grid-forming mode all the time; 3) requires only a single bit of information on the grid/network status for the mode transition. A framework for assessing the stability of the system and to guide the choice of parameters for controllers is developed using control-oriented modeling. A controller hardware-in-the-loop-based real-time simulation study on a test system based on the realistic electrical network of a commercial-scale medical center is conducted for initial prototyping of the control strategy. A hardware experiment is conducted with two 3 - $\phi$, 480 -V, 125 -kVA grid-forming inverters, a 3 - $\phi$, 480 -V, 270 -kVA grid simulator, a physical grid switch, and a physical load bank. The experimental data establishes the effectiveness of the always grid-forming operation and control of inverters in meeting power delivery objectives when on-grid and off-grid under various kinds of loads and scenarios while minimizing transients during transitions. Furthermore, performance comparison with existing strategies showcases the advantage of the proposed strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗