Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “impact monitoring”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Raspberry Pi–powered temperature monitoring of growth chamber microclimates

While controlled environments are desirable for growing and measuring plants, growth chambers and greenhouses typically have microclimates that impact plant growth, development, and stress responses. Furthermore, opening and closing the doors of a controlled environment introduces variation in the environment, especially at temperature extremes, affecting both the measurements and the organisms within. Using multiple temperature data loggers to normalize results can be cost-prohibitive and rarely offers real-time feedback on temperature status. We used low-cost single-board computers, cameras, and temperature sensors to manage and capture growth chamber temperatures while acquiring plant image data. Detailed here are methods to document microclimates within a growth chamber so that data can be normalized to measured temperature information. This protocol describes a low-cost method for automated temperature monitoring, which enables both high-throughput measurements of temperature along with plant growth and stress responses via plant imaging.

Plant Sciences↗
DOE OSTI2021

Microseismic at HFTS2: A Story of Three Stimulated Wells

A microseismic data set was acquired at Hydraulic Fracturing Test Site 2 (HFTS2) in the Delaware Basin (Texas, USA) by the HFTS2 consortium as a part of data-collection program designed to help maximize the efficiency of hydraulic fracturing, understand the impacts of nearby depleted zones, and increase the production of hydrocarbons. Advanced methods of microseismic monitoring applied at HFTS2 include 1) native synchronization of five monitor arrays, 2) real-time location of microseismic events, 3) joint event hypocenter/ velocity-model inversion that locates microseismic events simultaneously with constructing azimuthally anisotropic velocity models, 4) high-resolution reservoir imaging, utilizing microseismic events as downhole sources. HFTS2 microseismic data set illustrated the influence of geologic complexity, variations in treatment designs, and the presence of parent wells on the efficiency of hydraulic fracturing. Three Wolfcamp wells, Boxwood 1H, 2H, and 4H, were stimulated sequentially, exhibiting very different patterns of microseismicity. When the Boxwood 1H well is stimulated, its toe stages quickly reacted to the presence of depleted Bitterroot section to the east of the Boxwood pad, extending microseismic events over 3,000 ft to the east. This behavior changes dramatically after the treatment passes the Bitterroot section at the toe, and the heel stages in 1H showed tight microseismic grouping around the stimulated well, indicative of treatment of virgin reservoir rock. The Boxwood 2H demonstrates this same pattern of behavior between the toe and heel stages of the well, suggesting that the depleted Bitterroot section remains easier to refracture than adjacent non-depleted rock. Microseismic activity associated with Boxwood 4H treatment is distinctly different from those in Boxwood wells 1H and 2H. The events generated during stimulation of 4H are hardly influenced by the depleted Bitterroot section and fill the space between the previously stimulated 1H and 2H wells. Altogether, microseismic patterns suggest that treatment fluids tend to find the weakest passages through geologic formations, and the relative well positions and treatment sequence have a significant impact on fracture geometry, as indicated by microseismic event locations.

58 GEOSCIENCES↗
DOE OSTI2025

Passive Acoustic Monitoring Provides Insights into Avian Use of Energycane Cropping Systems in Southern Florida

Birds are important indicators of ecosystem health and provide a range of benefits to society. It is important, therefore, to understand the impacts of agricultural land use changes on bird populations. The cultivation of energycane (EC)—a sugarcane hybrid—for biofuel production represents one form of agricultural land use change in southern Florida. We used passive acoustic monitoring (PAM) to examine bird community use of experimental EC fields and other agricultural land uses at two study sites in southern Florida. We deployed 16 acoustic recorders in different study plots and used the automatic species identifier BirdNET to identify 40 focal bird species. We found seasonal differences in daily avian species diversity and richness between EC experimental plots and reference agricultural fields (corn fields, orchards, pastureland), and between time periods (pre-planting, post-planting). Daily avian species diversity and richness were lower in the EC experimental plots during Fall and Winter months when plants reached maximum height (>400 cm in some areas). Despite seasonal differences in daily measures of species diversity and richness, we found no differences in cumulative species richness, suggesting that there may be little overall (season-long) effects of EC production. These findings could provide insight to avian seasonal habitat preferences and underscore the potential limitations of PAM in areas experiencing dynamic vegetation changes. More research is needed to better understand if utilization of EC cropping systems results in positive or negative effects on avian populations (e.g., foraging habitat quality, predator–prey dynamics, nest success).

BirdNET↗
DOE OSTI2025

Satellite remote sensing for environmental sustainable development goals: A review of applications for terrestrial and marine protected areas

With few years left to achieve the vital United Nations Sustainable Development Goals (SDGs), member nations must urgently leverage technological advancements in environmental monitoring to succeed. Remote sensing now provides decades of global observations at a variety of spatio-temporal scales and a litany of data products to guide comprehensive measures for climate action, and aquatic and terrestrial biota preservation. Protected areas, such as national parks and wildlife preserves, represent largely untapped resources for both applying robust conservation measures and testing ambitious new approaches to sustainable development that could jumpstart the much-needed adoption of strategies to efficiently pursue global sustainability. This review summarizes recent demonstrated utilities of remotely sensed data applied to protected areas for research related to SDG goals 13, 14, and 15: “Climate Action”, “Life below Water”, and “Life on Land”. We identify successful uses of such data for each SDG, identify areas for improvement, and provide recommendations from the literature on how to expand what others have done to achieve lofty goals with global impact. We demonstrate that remote sensing provides a valuable tool for achieving SDGs as it facilitates monitoring vegetation health, water quality and condition, and climate variables at large spatial and fine temporal scales, while also evaluating the effectiveness of management and conservation practices. Issues remain, however, in that there is currently no reference from which to relate goal progress to human livelihoods. Further, the current relationship between remotely sensed indices and ecological services that determine sustainable development omit steps that would establish this connection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A formaldehyde and nitrogen dioxide toxics monitor for environmental justice

DOE’s Integrated Field Laboratories (IFLs) require a vast array of measurements to measure urban air quality. Nitrogen dioxide (NO2) and formaldehyde (HCHO) are ubiquitous pollutants with numerous sources, most notably vehicle exhaust. High-spatial density measurements are critical to understanding how certain neighborhoods are more adversely impacted by pollutants. This report describes the results of a Phase I SBIR project to develop and demonstrate a rapid, affordable, low-power monitor to provide high spatial resolution mobile measurements of formaldehyde and nitrogen dioxide at street-level, where people live and walk. The spatial density will be achieved via rapid measurements on a mobile platform. This sensor will quantify spatial disparities in cities of the toxics, HCHO and NO2, as well as carbon monoxide and carbon dioxide for better source identification. These species are detected in the mid infrared by laser absorption spectroscopy, and fiberoptics are used. Tasks accomplished include identification and testing of the appropriate spectroscopic regions; coupling of laser light into a fiberoptic system; development of software to control miniature electronics to scan and control two lasers simultaneously; coupling of the mid infrared fiber into a multipass cell for enhanced pathlength and sensitivity; characterization of sources of instrument noise; and design work for a Phase II prototype.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Explosive Byproduct Gas Transport Through Sorptive Geomedia

Current underground nuclear explosion (UNE) detection strategies rely heavily on atmospheric noble gas sampling of radioxenon. However, discriminating nuclear weapons testing programs from civilian sources is difficult due to highly variable atmospheric radioxenon backgrounds and processes affecting subsurface transport of parent radionuclides. Here, we aim to study the transport of gases produced by subsurface explosions as novel stable signatures for underground nuclear explosion (UNE) monitoring. These gases may be produced in large quantities with distinct molecular ratios, which will be impacted by subsurface transport processes. To demonstrate how ratios of gases produced by explosions can change during transport in geomaterials, we conducted laboratory benchtop experiments on the transport of carbon dioxide (CO 2 ) and hydrogen (H 2 ) gases through variably saturated zeolitic tuff, which is abundant at the historic US testing site. We observed that zeolitic tuff sorbs substantial quantities of CO 2 while allowing H 2 to transport more freely, leading to changes in the molecular ratios of the two gases along the transport pathway. Gas uptake in the dry zeolitic tuff core was 72.3% for CO 2 , compared with 53.4% for xenon and 7.6% for H 2 . The presence of 20% water saturation disrupted the CO 2 sorption process, though to a lesser extent than observed for noble gases, with a 36.7% drop in xenon sorption compared with a 21.9% drop for CO 2 . These results represent the first observations of zeolite sorption altering explosive gas ratios during transport through geomedia relevant to nuclear proliferation monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Biodiversity and Global Health: Intersection of Health, Security, and the Environment

One of the biggest consequences of large-scale environmental change is the loss of biodiversity. Recent studies predict a loss of 1 million species in the near future. Biodiversity loss and land use change through anthropogenic disturbance are known to affect disease exposure, disease severity, and disease impacts. It is becoming increasingly clear that biodiversity loss will lead to an increase in infectious diseases in certain regions. Changes in biodiversity may contain important signatures for prediction of infectious diseases and outbreaks. Here, we argue that areas of high or rapid biodiversity loss will be critical to monitor under the umbrella of global health security. Global Health Security consists of the actions and activities required to reduce the impact of public health events in populations living in different geographic regions and across international borders. Like infectious diseases, climate change and other causes of environmental changes do not respect international borders and are becoming more widespread in all parts of the world. Here, the relationships among biodiversity, climate and environmental changes, and pathogen prevalence are dynamic, context dependent, and complex. Accounting for this complexity requires the inclusion of the environment sector, presently missing, when evaluating the potential of global health security actions for mitigating disease risks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

2018 Long-Term Hydrologic Monitoring Program Report for Rio Blanco, Colorado, Site

This report presents the monitoring data collected by the U.S. Department of Energy (DOE) Office of Legacy Management (LM) at the Rio Blanco, Colorado, Site (Figure 1). The Rio Blanco site was the location of an underground nuclear test during which three nuclear devices were detonated nearly simultaneously in a single borehole in 1973. The test resulted in residual radionuclide contamination at the detonation depths of 5840, 6230, and 6690 feet (ft) (DOE 2015). Monitoring includes the collection of samples from groundwater wells and surface water locations near the site to assess for any potential impacts that may be attributed to the Rio Blanco nuclear test. This report summarizes the laboratory analytical results obtained from the sampling event conducted in 2018. This annual report and previous reports are available on the LM public website at https://www.lm.doe.gov/rio_blanco/Sites.aspx. Data collected during this and previous monitoring events are available on the Geospatial Environmental Mapping System (GEMS) website at https://gems.lm.doe.gov/#site=RBL.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Kimberlina 1.2 Velocity Models and Seismic Data

Kimberlina 1.2 Velocity model and synthetic seismic data, produced in collaboration of teams at the National Energy Technology Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory through the National Risk Assessment Partnership. Data is associated with the following publication: Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019. The Kimberlina 1.2 Velocity models were produced by Zan Wang, Robert Dilmore, William Harbert, and Lianjie Huang at NETL. The following citations are directly related to the creation of the velocity models: Wang, Z. Harbert, W., Dilmore, R., Huang, L. Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities. International Journal of Greenhouse Gas Control. V. 76, September 2018, Pages 39-52. https://doi.org/10.1016/j.ijggc.2018.06.011 Wang, Z., Dilmore, R., Harbert, W. Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites. International Journal of Greenhouse Gas Control, V. 100, September 2020. https://doi.org/10.1016/j.ijggc.2020.103115 The velocity models were built based on the Kimberlina 1.2 aquifer impact data which is associated with the following publications: Buscheck, T.A., Mansoor, K., Yang, X., Wainwright, H., and Carroll, S. (2019). Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir. International Journal of Greenhouse Gas Control. 91. 102812. 10.1016/j.ijggc.2019.102812. Xianjin Yang, Thomas A. Buscheck, Kayyum Mansoor, Zan Wang, Kai Gao, Lianjie Huang, Delphine Appriou, Susan A. Carroll, Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, Volume 90, 2019, 102803, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2019.102803 The synthetic seismic data was produced by Youzuo Lin and team at LANL, and are associated with the following citations: Jordan, P. D., and J. L. Wagoner. Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California. Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019.

CO2 Leakage↗
DOE OSTI2026

Evolution of storage monitoring – update in response to commercial and regulatory drivers

Carbon Capture and Storage (CCS) is in transition from first-of-a kind projects and research-orientated pilots to commercially-motivated applications. Monitoring results from many newly developed and planned large scale commercial projects are limited; however, it is worthwhile to assess their evolution and consider new strategies as part of an effort to assess and document best practices. Commercial monitoring is targeted to activities that comply with regulatory drivers and de-risk investments. Commercial monitoring also supports accounting that storage has occurred and is tied to project financing. It deals with long time frames and large volumes injected into multiple wells and multiple projects in favorable areas. We see developing trends toward reproducible workflows that systematically reduce risks and clarify expectations for oversight and long-term surveillance. Monitoring techniques showing increasing trends include injection zone pressure as a history-matching and compliance tool. To reduce cost and environmental impact of time-lapse seismic data collection, deploying new approaches and tools, such as use of fibre and installed sources are increasingly applied. Concern over the risk of induced seismicity by regulatory bodies and the general public has increased, which has also resulted in increased monitoring. Some techniques used in the early research phases have been sidelined or used only in restricted applications. For example, geochemical analyses in the injection zone as well as the environment are now being deployed less than it was in research-oriented programs, except in the US where it is required by the permitting process. Expectations of frequent area-wide near surface monitoring have also decreased.

25 ENERGY STORAGE↗
DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗
DOE OSTI2025

Heavy metal imprints in Antarctic snow from research and tourism

Antarctica, long regarded as one of the last pristine environments on Earth, is increasingly affected by human activity. As tourism surges and scientific operations expand, air pollution from local emissions is raising new environmental concerns. Here, in this study, we analyze surface snow samples collected along a ~2,000 km transect, from the South Shetland Islands (62°S) to the Ellsworth Mountains (79°S), to map the geochemical fingerprints of aerosol deposition. We identify distinct spatial patterns shaped by crustal, marine, biogenic, and anthropogenic sources. Notably, we detect heavy metal imprints in the snow chemistry of the northern Antarctic Peninsula, where major research stations are concentrated and marine tourism traffic is most intense. Our findings shed light on the extent of the impacts from energy-intensive local activities in Antarctica, underscoring the need for enhanced environmental monitoring and sustainable management strategies in this fragile region.

Cordero, Raúl R. [Univ. of Groningen, Leeuwarden (↗
DOE OSTI2025

Governance and resilience as entry points for transforming food systems in the countdown to 2030

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Schneider, Kate R. [Johns Hopkins Univ., Washingto↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗
DOE OSTI2026

Effects of White Sturgeon (Acipenser transmontanus) Passage Through a Novel Hydropower Turbine and Implications for Long-Term Survival

Researchers from UC Davis and Natel Energy compared a novel "Restoration Hydropower Turbine" against a conventional turbine design to evaluate their physical and physiological effects on juvenile White Sturgeon. The study found that the new design achieved over 99% survival across all test speeds, whereas the conventional turbine caused significant mortality and injury due to blade strikes. Furthermore, long-term monitoring showed that fish passing through the new turbine experienced no meaningful or persistant negative impacts on their growth, behavior, or stress levels.

13 HYDRO ENERGY↗
DOE OSTI2024

How Earth System Models Can Inform Key Dimensions of Marine Food Security in the Alaskan Arctic

The Arctic is home to several groups of Indigenous Peoples, each with distinct ways of interacting with their environment and ways of life. Arctic, Indigenous Peoples’ food sovereignty is tightly linked with food security. Subsistence harvesting activities provide nutritious and culturally vital foods for Alaska Native households and communities. Climate change is causing rapid and more unpredictable shifts in environmental conditions that impact three of the key aspects of food security, availability, stability, and accessibility. While communities monitor the abundance and health of food webs and environments as part of subsistence harvest practices, anticipating major transformations and changes in these systems is challenging. We explored the potential of Earth System Model output in helping anticipate or project physical or ecosystem changes relevant to Alaska Indigenous peoples’ food security needs. Through examples of model products, that provide measures of accessibility and availability of marine resources, we show that modern models, such as the Energy Exascale Earth System Model presented here, can provide estimates of a broad suite of variables relevant to food security. We investigate how Earth System Model output could contribute to exploring questions related to aspects of Arctic food security such as accessibility and availability and highlight present model shortcomings that, if addressed, would move Earth System Models closer to being a useful tool for understanding environmentally driven changes to the availability and accessibility of harvestable food resources. Our example model-derived food security indicators illustrate how Earth System Model output could be combined with relevant, non-model, information sources; These model products are meant only as a starting point and a tool for engaging community members and to present, in an accessible way, the model’s potential utility, or current lack thereof, to rights holders and stakeholders concerned about food security. We are hopeful that with example products in hand, additional model development efforts will have a higher likelihood of success in achieving an iterative discussion with stakeholders regarding feasible and desired products.

Gibson, Georgina A.↗
DOE OSTI2023

Parametric and Sensitivity Analysis of a Steam Generator Model Using Python and Machine-Learning Tools

For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.

20 FOSSIL-FUELED POWER PLANTS↗