Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “operational analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Precision Møller polarimetry for PREX-2 and CREX

The PREX-2 and CREX experiments in Hall A at Jefferson Lab are precision measurements of parity violating elastic electron scattering from complex nuclei. One requirement was that the incident electron beam polarization, typically , be known with 1% precision. We commissioned and operated a Møller polarimeter on the beam line that exceeds this requirement, achieving a precision of 0.89% for PREX-2, and 0.85% for CREX. The uncertainty is purely systematic, accumulated from several different sources, but dominated by our knowledge of the target polarization. Our analysis also demonstrates the need for accurate atomic wave functions in order to correct for the Levchuk Effect. We describe the details of the polarimeter operation and analysis, as well as (for CREX) a comparison to results from a different polarimeter based on Compton scattering.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2020

DAQExpert the service to increase CMS data-taking efficiency

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly in the event of hardware or software failure of the subsystems, an expert system, the DAQ Expert, has been developed. It aims at improving the data taking efficiency, reducing the human error in the operations and minimising the on-call expert demand. Introduced in the beginning of 2017, it assists the shift crew and the system experts in recovering from operational faults, streamlining the post mortem analysis and, at the end of Run 2, triggering fully automatic recovery without human intervention. DAQ Expert analyses the real-time monitoring data originating from the DAQ components and the high-level trigger updated every few seconds. It pinpoints data flow problems, and recovers them automatically or after given operator approval. We analyse the CMS downtime in the 2018 run focusing on what was improved with the introduction of automated recovery; present challenges and design of encoding the expert knowledge into automated recovery jobs. Furthermore, we demonstrate the web-based, ReactJS interfaces that ensure an effective cooperation between the human operators in the control room and the automated recovery system. We report on the operational experience with automated recovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Improving Stability of Triple-Cation Perovskite Solar Cells under High-Temperature Operation

Metal halide perovskite photovoltaic performance required for commercial technology encompasses both efficiency and stability. Advances in both these parameters have recently been reported; however, these strategies are often difficult to directly compare due to differences in perovskite composition, device architecture, fabrication methods, and accelerated stressors applied in stability tests. In particular, it is found that there is a distinct lack of elevated temperature, operational (light and bias) stability data. Furthermore, significant testing is required to understand the interactions when combinations are used (e.g., additives used with posttreatments). Herein, individual and combined additive, posttreatment, and contact layer strategies from recent literature reports under standardized operational stability tests of p-i-n CsMAFA perovskites at 70 degrees C are evaluated. Through analysis of over 1000 devices, it is concluded that the hole-transport layer (HTL) is the most significant component impacting elevated temperature operational stability. This analysis motivates future development of high-performance HTLs.

14 SOLAR ENERGY↗
DOE OSTI2022

The DPG Method for the Convection-Reaction Problem, Revisited

In this work, we study both conforming and non-conforming versions of the practical DPG method for the convection-reaction problem. We determine that the most common approach for DPG stability analysis – construction of a local Fortin operator – is infeasible for the convection-reaction problem. We then develop a line of argument based on a direct proof of discrete stability; we find that employing a polynomial enrichment for the test space does not suffice for this purpose, motivating the introduction of a (two-element) subgrid mesh. The argument combines mathematical analysis with numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Validation of Fast Reactor Depletion Tools Using EBR-II Measured Data

The validation of simulation tools for calculating fuel depletion and evolution in fast reactors is vital for design, licensing, deployment, operations, and material accountancy. The Physics Analysis Database (PADB) and Analytical Laboratory (AL) database contain measured data collected from Experimental Breeder Reactor II (EBR-II) and were used to validate the most recent versions of the Argonne Reactor Computation (ARC) tool suite and ORIGEN-S for calculating isotopic compositions in irradiated fast reactor fuel. The PADB contains important modeling and operational information about the EBR-II core design, fuel cycle, and analytical results from the legacy versions of the ARC tool suite. The AL database contains the measured isotopic compositions of irradiated samples taken from core subassemblies. A new procedure was developed for the ARC tool suite to perform the EBR-II depletion simulation, as well as to perform more detailed isotopic calculations using ORIGEN-S calculations by coupling it with the ARC suite. Both the ARC and ARC-ORIGEN results were compared with the AL measured data for all relevant samples and showed good agreement for the major actinides. Good agreement with measured data was also achieved using the ARC-ORIGEN approach for several fission products.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Analysis of basic airflow configurations for separate sensible and latent cooling systems with indoor air recirculation

Separate sensible and latent cooling (SSLC) is a technology with efficiency and comfort advantages over conventional cooling systems used for space conditioning of buildings. Using multiple cooling processes at different temperatures allows SSLC to save energy by raising the evaporation temperature of the sensible cooling process. In this paper, all possible airflow configurations of SSLC systems are enumerated under the following constraints: exactly two heat exchangers are used, and air is recirculated to the conditioned space (no exhaust or outdoor air treatment). Seven designs are identified, with varying free operating variables, and each is modeled. Analysis reveals that several configurations are equivalent, and there is only one unique basic airflow SSLC configuration: the one with the sensible and latent heat exchangers placed in series. The efficiency of the SSLC system is compared against that of the conventional system. Under standard conditions, an SSLC system can improve the coefficient of performance by 14.8%. In addition to the numerical simulation, the optimal operating condition of the basic air configuration of the SSLC system is derived analytically. The basic SSLC system is shown to offer the highest performance improvement when the outdoor temperature is relatively cool and the space sensible heat ratio is high.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2022

Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. Here, to meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

The Local Seismoacoustic Wavefield of a Research Nuclear Reactor and Its Response to Reactor Power Level

In this article, we describe the seismoacoustic wavefield recorded outdoors but inside the facility fence of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (Tennessee). HFIR is a research nuclear reactor that generates neutrons for scattering, irradiation research, and isotope production. This reactor operates at a nominal power of 85 MW, with a full-power period between 24 and 26 days. This study uses data from a single seismoacoustic station that operated for 60 days and sampled a full operating reactor cycle, that is, full-power operation and end-of-cycle outage. The analysis presented here is based on identifying signals that characterize the steady, that is, full-power operation and end-of-cycle outage, and transitional, that is, start-up and shutdown, states of the reactor. We found that the overall seismoacoustic energy closely follows the main power cycle of the reactor and identified spectral regions excited by specific reactor operational conditions. In particular, we identified a tonal noise sequence with a fundamental frequency around 21.4 Hz and multiple harmonics that emerge as the reactor reaches 90% of nominal power in both seismic and acoustic channels. We also utilized temperature measurements from the monitoring system of the reactor to suggest links between the operation of reactor’s subsystems and seismoacoustic signals. We demonstrate that seismoacoustic monitoring of an industrial facility can identify and track some industrial processes and detect events related to operations that involve energy transport.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Advanced Power Systems Measurements: A Literature Review

In 2020, a literature survey was performed as part of a project aimed at producing guidance for the Department of Energy in the form of a Roadmap. The topic of the Roadmap was measurement. That was viewed as being generally underrepresented in consideration of sensing and measurement or instrumentation and measurement. Sensing is just the beginning of a process, and the instrument is just the container for the process. Measurement, the experimental process, has acquired a considerable body of theory over the last few decades, theory that is not widely taught and disseminated. The results of the survey can be said broadly to reflect that lack of appreciation. Specifically, the review found that there is support for the development of a more “capable” PMU. New algorithms and new documentary standards will be needed. Some of the findings are: (1) Point-on-wave technology adds new capability to the existing suite of measurements, and could allow for improved operation and protection (2) Power quality analysis has historically been concerned with assessment of how non-sinusoidal the delivered voltage shows promise in signature recognition, a departure from the modeling that has historically characterized power system measurements. (3) The PMU is assed as being a remarkable measurement system, but its performance is held back by the lack of understanding of the measurement theory underpinning its operation. (4) The PMU is being considered for application in the distribution system. There is a danger that it will be seen only in the light of the successful PMU implementation in transmission, and the two have different requirements. These various expansions of measurements will no doubt benefit from a better appreciation of the theoretical aspects of measurement. In this, and other, regards, our survey of the literature has identified gaps as well as possibilities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Scrambling and quantum chaos indicators from long-time properties of operator distributions

Scrambling is a key concept in the analysis of nonequilibrium properties of quantum many-body systems. Most studies focus on its characterization via out-of-time-ordered correlation (OTOC) functions, particularly through the early-time decay of the OTOC. However, scrambling is a complex process which involves operator spreading and operator entanglement, and a full characterization requires one to access more refined information on the operator dynamics at several timescales. In this work we analyze operator scrambling by expanding the target operator in a complete basis and studying the structure of the expansion coefficients treated as a coarse-grained probability distribution in the space of operators. Here we study different features of this distribution, such as its mean, variance, and participation ratio, for the Ising model with longitudinal and transverse fields, kicked collective spin models, and random circuit models. We show that the long-time properties of the operator distribution display common features across these cases and discuss how these properties can be used as a proxy for the onset of quantum chaos. Finally, we discuss the connection with OTOCs and analyze the cost of probing the operator distribution experimentally using these correlation functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling [SWR-25-20]

Code repository for the experiments performed in the paper: On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling (https://doi.org/10.1017/eds.2025.11) Overall, our work investigates the zero-shot downscaling potential of neural operators. To summarize, our contributions are: 1. We provide a comparative analysis based on two challenging weather downscaling problems, between various neural operator and non-neural-operator methods with large upsampling factors (e.g., 8x and 15x) and fine grid resolutions (e.g., 2 km × 2 km wind speed). 2. We examine whether neural operator layers provide unique advantages when testing downscaling models on upsampling factors higher than those seen during training, i.e., zero-shot downscaling. Our results instead show the surprising success of an approach that combines a powerful transformer-based model with a parameter-free interpolation step at zero-shot weather downscaling. 3. We find that this Swin-Transformer-based approach mostly outperforms all neural operator models in terms of average error metrics, whereas an enhanced super-resolution generative adversarial network (ESRGAN)-based approach is better than most models in capturing the physics of the system, and suggests their use in future work as strong baselines. However, these approaches still do not capture variations at smaller spatial scales well, including the physical characteristics of turbulence in the HR data. This suggests a potential for improvement in transformer or GAN-based methods and neural-operator-based methods for zero-shot weather downscaling.

Sinha, Saumya [National Renewable Energy Laborator↗
DOE OSTI2021

Assimilation of X-band Phased-Array Radar Data with EnKF for the Analysis and Warning Forecast of a Tornadic storm

The impact of assimilating China's operational X-band Phased-Array radar's (X-PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S-band operational radars and one X-PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X-PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. Furthermore, the assimilation of X-PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X-PAR data assimilation, assimilating X-PAR data improved the predictability of tornadic storm.

54 ENVIRONMENTAL SCIENCES↗