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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 OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2022

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Control Design of Passive Grid-Forming Inverters in Port-Hamiltonian Framework

This article presents a modified dispatchable virtual oscillator control approach for achieving the passivity of gridforming inverters (GFMs), without assuming constant voltage and constant frequency. The proposed control framework utilizes the Port-Hamiltonian (PH) based structure that mimics the behaviors of coupled harmonic oscillators, along with an energy ‘pumpingor-damping’ block and the Control by Interconnection (CbI) technique, to render the inverter passive. Once passivity is achieved, transient stability of the system will be guaranteed. The proposed control framework is composed of three loops: an outer power dispatching loop that generates the voltage and frequency references, a virtual oscillator loop that emulates the spontaneous synchronization of oscillators, and an inductor current loop that maintains lossless interconnection in PH systems. In conclusion, the study shows that the proposed control approach ensures the passivity of GFMs, facilitating the transient stability design of multi-inverter systems, as interconnections of passive systems remain passive and stable.

42 ENGINEERING↗
DOE OSTI2020

Control-Oriented Modeling of Cycle-to-Cycle Combustion Variability at the Misfire Limit in SI Engines

A control-oriented model is presented that can capture the prior-cycle correlation of combustion cycles during conditions with high levels of exhaust gas recirculation (EGR). Combustion events are modeled in discrete time and the dynamic evolution is captured by the residual air, fuel, and inert gas trapped in the combustion chamber. The mathematical formulation of the model is presented together with the calibration procedure to emulate a particular engine operating condition. A cycle-to-cycle system identification methodology is described which allows regressing model parameters from experimental data. Simulations are presented and compared to real engine measurements to show the modeling potential for analysis and control of combustion events.

Maldonado Puente, Bryan↗
DOE OSTI2022

Efficient emulation of relativistic heavy ion collisions with transfer learning

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models (“emulators”) can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Time Series Dimension Reduction for Surrogate Models of Port Scanning Cyber Emulations

Surrogate model development is a key resource in the scientific modeling community for providing computational expedience when simulating complex systems without loss of great fidelity. The initial step to development of a surrogate model is identification of the primary governing components of the system. Principal component analysis (PCA) is a widely used data science technique that provides inspection of such driving factors, when the objective for modeling is to capture the greatest sources of variance inherent to a dataset. Although an efficient linear dimension reduction tool, PCA makes the fundamental assumption that the data is continuous and normally distributed. Thus, it provides ideal performance when these conditions are met. In the case for which cyber emulations provide realizations of a port scanning scenario, the data to be modeled follows a discrete time series function comprised of monotonically increasing piece-wise constant steps. The sources of variance are related to the timing and magnitude of these steps. Therefore, we consider using XPCA, an extension to PCA for continuous and discrete random variates. This report provides the documentation of the trade-offs between the PCA and XPCA linear dimension reduction algorithms, for the intended purpose to identify key components of greatest variance in our time series data. These components will ultimately provide the basis for future surrogate models of port scanning cyber emulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations

Understanding and quantifying the inertia of power systems with the integration of converter-interfaced generation (CIG) plays an essential role in the safe transition to a future low-inertia scenario. Here we provide a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods. In particular, the estimation methods are categorized as model-based and measurement-based approaches considering both small and large disturbances. The advantages and disadvantages of different methods are carefully discussed. This paper also offers for the first time a framework to quantify the virtual inertia of CIGs at the component and aggregation levels, an open problem in the literature. Finally, future directions for inertia estimation are identified and discussed. This significantly benefits the design of appropriate control and protection schemes in achieving a more reliable, secure, and resilient power system.

42 ENGINEERING↗
DOE OSTI2024

Pushing the frontiers in climate modelling and analysis with machine learning

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here, in this study, we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An FPGA-based readout chip emulator for the CMS ETL detector upgrade

Here, we present an FPGA-based readout chip emulator board for the CMS Endcap Timing Layer (ETL) detector upgrade. The emulator board uses an Intel Cyclone 10 GX FPGA to emulate the digital functions of four Endcap Layer Readout Chips (ETROCs). Based on the actual ETROC design, the firmware is implemented and verified. The emulator board is being used for the ETROC digital design verification and system development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Lastly, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

42 ENGINEERING↗
DOE OSTI2021

Development of a Power Electronics-based Testbed for a Flexible Combined Heat and Power System

A flexible combined heat and power (F-CHP) concept, enabled by a power electronics-based power conditioning system (PCS), has been recently proposed as a cost-effective way for supporting the grid with high penetration of renewable energy sources. To verify the design, control, and performance of the overall system, a laboratory-scale testbed is essential. This paper focuses on the development of a power electronics-based testbed for such a F-CHP system. The developed testbed can test the control, grid-connected mode operation, islanded mode operation, and mode transitions, including planned/unplanned islanding and reconnection, of the F-CHP system. In the grid-connected mode, both the steady state and transients of the ac grid can be emulated, and various grid support functions can be tested with the help of the developed PCS converter. In the islanded mode, the external ac loads can also be supported by the F-CHP system. Also, the central controller, which is implemented in a NI CompactRIO, can be tested. Experimental test results for the grid-connected mode, islanded mode, and mode transitions are provided to validate the testbed design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Validation of Microgrid Algorithms using At-Scale Simulation and Emulation Real-Time (ASSERT) Framework

This paper presents a novel framework for validating microgrid algorithms using a At-Scale Simulation and Emulation Real-Time (ASSERT) setup. The proposed universal microgrid validation framework was developed at Advanced Research on Integrated Energy Systems (ARIES) platform at National Renewable Energy Laboratory in Golden, Colorado, USA. As the number of intercommunicating devices grow in power systems, along with more integration of inverter-based resources, developing and validating microgrid operation and control algorithms is becoming complex and time-consuming task. The ASSERT framework is designed to provide a comprehensive, accurate, and efficient solution for validating microgrid algorithms. It combines at-scale simulations and real-time emulation to create a virtual environment that closely mimics the behavior of a physical microgrids which are characteristic of future smart distribution systems, such as: (1) increasing variability in the physical size of new energy technologies; (2) requiring control large numbers of interconnected devices, and (3) integrating diverse technologies that have not previously worked together. The paper describes the technical details of the ASSERT framework, its validation process, and its benefits over traditional validation methods. The framework's ability to validate the performance of various algorithms is demonstrated through a case study, which shows that it can effectively identify the weaknesses of an algorithm and optimize its performance. Overall, the ASSERT framework is a promising tool that can significantly enhance the development and validation of microgrid algorithms, leading to more reliable and efficient microgrid operation.

at-scale↗
DOE OSTI2021

Virtual Framework for Development and Testing of Federation Software Stack

Softwarization of networked infrastructures combined with containerization of codes promises unprecedented computing capabilities distributed across the federations of computing systems and physical instruments. The development and testing of a software stack that implements these capabilities over an expensive physical production infrastructure is not cost-effective, and in the early stages, may potentially cause service disruptions. To address these aspects, we develop the Virtual Federated Science Instrument Environment (VFSIE), a digital twin of the physical infrastructure that emulates a multi-site federation. Each federated site is emulated using containers and virtual hosts that are connected over local-area networks, and the sites, in turn, are connected over an emulated wide-area network. We describe the framework design and implementation details. We also illustrate its application by emulating a federation of four laboratories that use Jupyter Notebook for computations and the EPICS software system for instrument control.

Al Najjar, Anees↗
DOE OSTI2025

Real-World Cyber Security Demonstration for Networked Electric Drives

In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed's capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed's security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed's usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗