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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Sci—Thur PM: Imaging — 01: Position-sensitive noise characteristics in multi-pinhole cardiac SPECT imaging

Myocardial perfusion imaging (MPI) with Single Photon Emission Computed Tomography (SPECT) is invaluable in the diagnosis and management of heart disease. It provides essential information on myocardial blood flow and ischemia. Multi-pinhole dedicated cardiac-SPECT cameras offer improved count sensitivity, and spatial and energy resolutions over parallel-hole camera designs however variable sensitivity across the field-of-view (FOV) can lead to position-dependent noise variations. Since MPI evaluates differences in the signal-to-noise ratio, noise variations in the camera could significantly impact the sensitivity of the test for ischemia. We evaluated the noise characteristics of GE Healthcare's Discovery NM530c camera with a goal of optimizing the accuracy of our patient assessment and thereby improving outcomes. Theoretical sensitivity maps of the camera FOV, including attenuation effects, were estimated analytically based on the distance and angle between the spatial position of a given voxel and each pinhole. The standard deviation in counts, σ was inferred for each voxel position from the square root of the sensitivity mapped at that position. Noise was measured experimentally from repeated (N=16) acquisitions of a uniform spherical Tc-99m-water phantom. The mean (μ) and standard deviation (σ) were calculated for each voxel position in the reconstructed FOV. Noise increased ∼2.1× across a 12 cm sphere. A correlation of 0.53 is seen when experimental noise is compared with theory suggesting that ∼53% of the noise is attributed to the combined effects of attenuation and the multi-pinhole geometry. Further investigations are warranted to determine the clinical impact of the position-dependent noise variation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2024

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵ m ≃ 7 x 10 -4 g 2 kg −2 s −1 for mixing ratio and ϵ θ ≃ 1.6 x 10 -3 K 2 s −1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃ 10 -2 m 2 s −3 . We also demonstrate that the vertical wind ACF coefficient k w ∝ $\overline{w'^{2}}$ and the TKE dissipation ϵ ∝ ($\overline{w'^{2}}$) 3/2 . For the molecular destruction rates, we show that ϵ m ∝ $\overline{m'^{2}}$ ($\overline{w'^{2}}$) 1/2 and ϵ θ ∝ $\overline{θ'^{2}}$ ($\overline{w'^{2}}$) 1/2 . These equations can be used for parameterizations of ϵ, ϵ m , and ϵ θ . All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

dissipation rate↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗
DOE OSTI2013

Evaluation of wall boundary condition parameters for gas-solids fluidized bed simulations

Wall boundary conditions for the solids phase have significant effects on numerical predictions of various gas-solids fluidized beds. Several models for the granular flow wall boundary condition are available in the open literature for numerical modeling of gas-solids flow. In this study, a model for specularity coefficient used in Johnson and Jackson boundary conditions by Li and Benyahia (AIChE Journal, 2012, 58, 2058-2068) is implemented in the open-source CFD code-MFIX. The variable specularity coefficient model provides a physical way to calculate the specularity coefficient needed by the partial-slip boundary conditions for the solids phase. Through a series of 2-D numerical simulations of bubbling fluidized bed and circulating fluidized bed riser, the model predicts qualitatively consistent trends to the previous studies. Furthermore, a quantitative comparison is conducted between numerical results of variable and constant specularity coefficients to investigate the effect of spatial and temporal variations in specularity coefficient.

42 ENGINEERING↗
DOE OSTI2026

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.

42 ENGINEERING↗
DOE OSTI2016

Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24 (~4 km) grid resolution for the baseline (1966 2005) and future (2011 2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snow-covered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. Furthermore, the projected hydrological changes described in this study have implications for various aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Novel Multicompartment 3-Dimensional Radiochromic Radiation Dosimeters for Nanoparticle-Enhanced Radiation Therapy Dosimetry

Purpose: Gold nanoparticles (AuNps), because of their high atomic number (Z), have been demonstrated to absorb low-energy X-rays preferentially, compared with tissue, and may be used to achieve localized radiation dose enhancement in tumors. The purpose of this study is to introduce the first example of a novel multicompartment radiochromic radiation dosimeter and to demonstrate its applicability for 3-dimensional (3D) dosimetry of nanoparticle-enhanced radiation therapy. Methods and Materials: A novel multicompartment phantom radiochromic dosimeter was developed. It was designed and formulated to mimic a tumor loaded with AuNps (50 nm in diameter) at a concentration of 0.5 mM, surrounded by normal tissues. The novel dosimeter is referred to as the Sensitivity Modulated Advanced Radiation Therapy (SMART) dosimeter. The dosimeters were irradiated with 100-kV and 6-MV X-ray energies. Dose enhancement produced from the interaction of X-rays with AuNps was calculated using spectrophotometric and cone-beam optical computed tomography scanning by quantitatively comparing the change in optical density and 3D datasets of the dosimetric measurements between the tissue-equivalent (TE) and TE/AuNps compartments. The interbatch and intrabatch variability and the postresponse stability of the dosimeters with AuNps were also assessed. Results: Radiation dose enhancement factors of 1.77 and 1.11 were obtained using 100-kV and 6-MV X-ray energies, respectively. The results of this study are in good agreement with previous observations; however, for the first time we provide direct experimental confirmation and 3D visualization of the radiosensitization effect of AuNps. The dosimeters with AuNps showed small (<3.5%) interbatch variability and negligible (<0.5%) intrabatch variability. Conclusions: The SMART dosimeter yields experimental insights concerning the spatial distributions and elevated dose in nanoparticle-enhanced radiation therapy, which cannot be performed using any of the current methods. The authors concluded that it can be used as a novel independent method for nanoparticle-enhanced radiation therapy dosimetry.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Long-Term Measurements of Methane Ebullition From Thaw Ponds

Arctic regions are experiencing rapid warming, leading to permafrost thaw and formation of numerous water bodies. Although small ponds in particular are considered hot spots for methane (CH 4 ) release, long-term studies of CH 4 efflux from these surfaces are rare. We have collected an extensive data set of CH 4 ebullition (bubbling) measurements from eight small thaw ponds (<0.001 km 2 ) with different physical and hydrological characteristics over four summer seasons, the longest set of observations from thaw ponds to date. The measured fluxes were highly variable with an average of 20.0 mg CH 4 · m -2 · day -1 (median: 4.1 mg CH 4 · m -2 · day -1 , n = 2,063) which is higher than that of most nearby lakes. The ponds were categorized into four types based on clear and significant differences in bubble flux. We found that the amount of CH 4 released as bubbles from ponds was very weakly correlated with environmental variables, like air temperature and atmospheric pressure, and was potentially more related to differences in physical characteristics of the ponds. Using our measured average daily bubble flux plus the available literature, we estimate circumpolar thaw ponds <0.001 km 2 in size to emit between 0.2 and 1.0 Tg of CH 4 through ebullition. Our findings exemplify the importance of high-frequency measurements over long study periods in order to adequately capture the variability of these water bodies. Through the expansion of current spatial and temporal monitoring efforts, we can increase our ability to estimate CH 4 emissions from permafrost pond ecosystems now and in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Blue Carbon Stocks Along the Pacific Coast of North America Are Mainly Driven by Local Rather Than Regional Factors

Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha −1 , respectively), 45% higher than marshes (245 Mg ha −1 ), and more than 500% higher than seagrass (68 Mg ha −1 ). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha −1 ). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

emergent marsh↗
DOE OSTI2014

The atmospheric circulation and observable properties of non-synchronously rotating hot Jupiters

We study the feasibility of observationally constraining the rotation rate of hot Jupiters, planets that are typically assumed to have been tidally locked into synchronous rotation. We use a three-dimensional General Circulation Model to solve for the atmospheric structure of two hot Jupiters (HD 189733b and HD 209458b), assuming rotation periods that are 0.5, 1, or 2 times their orbital periods (2.2 and 3.3 days, respectively), including the effect of variable stellar heating. We compare two observable properties: (1) the spatial variation of flux emitted by the planet, measurable in orbital phase curves, and (2) the net Doppler shift in transmission spectra of the atmosphere, which is tantalizingly close to being measurable in high-resolution transit spectra. Although we find little difference between the observable properties of the synchronous and non-synchronous models of HD 189733b, we see significant differences when we compare the models of HD 209458b. In particular, the slowly rotating model of HD 209458b has an atmospheric circulation pattern characterized by westward flow and an orbital phase curve that peaks after secondary eclipse (in contrast to all of our other models), while the quickly rotating model has a net Doppler shift that is more strongly blueshifted than the other models. Our results demonstrate that the combined use of these two techniques may be a fruitful way to constrain the rotation rate of some planets and motivate future work on this topic.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TuckerCompressMPI v. 1.0

As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five-way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed-memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. This software provides a method for compressing large-scale multiway data.

Austin, Woody↗
DOE OSTI2025

Data and scripts associated with “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA"

This data package is associated with the publication “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA” submitted to JGR-Biogeosciences (Regier et al. 2025).This study used reach-scale modeled estimates of hyporheic aerobic respiration made by the River Corridor Model (Fang et al. 2020) and watershed characteristics across the Willamette and Yakima River basins to explore potential allometric scaling (i.e., power-law relationships between size and function) of cumulative hyporheic respiration across catchment-to-basin scales. Scaling was explored quantitatively via the R2, slope, and y-intercept of relationships between cumulative hyporheic respiration and watershed area, divided into hyporheic exchange flux (HEF) quantiles. We also explored relationships between allometric scaling and other watershed characteristics through linear regression, spatial patterns, and mutual information analyses. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales. This data package is associated with the GitHub repository found at https://github.com/peterregier/rc_wrb_yrb_scaling. The data package is organized into several key directories. The “data” folder contains multiple CSV files, including landscape heterogeneity, scaling analysis, and watershed boundary data. The “figures” folder has all figure files in both PDF and PNG formats. Core analysis scripts and figure generation scripts are in the “scripts” directory, systematically numbered for sequential execution. The root directory includes essential project files; please see the file ending in “flmd.csv” for a list and description of all files contained in this data package and the file ending in “dd.csv” for data dictionaries used to describe tabular column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Parallel Tensor Compression for Large-Scale Scientific Data.

As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Advanced Instrumentation for Transient Reactor Testing

Transient testing involves placing fuel or material into the core of specialized materials test reactors that are capable of simulating a range of design basis accidents, including reactivity insertion accidents, that require the reactor produce short bursts of intense highpower neutron flux and gamma radiation. Testing fuel behavior in a prototypic neutron environment under high-power, accident-simulation conditions is a key step in licensing nuclear fuels for use in existing and future nuclear power plants. Transient testing of nuclear fuels is needed to develop and prove the safety basis for advanced reactors and fuels. In addition, modern fuel development and design increasingly relies on modeling and simulation efforts that must be informed and validated using specially designed material performance separate effects studies. These studies will require experimental facilities that are able to support variable scale, highly instrumented tests providing data that have appropriate spatial and temporal resolution. Finally, there are efforts now underway to develop advanced light water reactor (LWR) fuels with enhanced performance and accident tolerance. These advanced reactor designs will also require new fuel types. These new fuels need to be tested in a controlled environment in order to learn how they respond to accident conditions. For these applications, transient reactor testing is needed to help design fuels with improved performance. In order to maximize the value of transient testing, there is a need for in-situ transient realtime imaging technology (e.g., the neutron detection and imaging system like the hodoscope) to see fuel motion during rapid transient excursions with a higher degree of spatial and temporal resolution and accuracy. There also exists a need for new small, compact local sensors and instrumentation that are capable of collecting data during transients (e.g., local displacements, temperatures, thermal conductivity, neutron flux, etc.).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Environmental impacts of dam reservoir filling in the East Amazon

Mitigating the environmental impacts caused by hydroelectric dams is a worldwide challenge. The aquatic ecosystem is the most impacted during the reservoir filling phase, yet biogeochemical dynamics at this stage are not well-studied. Here we evaluate water quality and hydraulic parameters in the Araguari River (Amapá/Brazil) during the filling of the Ferreira Gomes Hydroelectric Power Plant reservoir (UHEFG). Five field campaigns were performed from July 2014 to August 2015 across nine sample sites within the reservoir (P1, P2, …P9) and only one downstream (P10). The following key variables were monitored: Trophic State Index (TSI), Total Coliforms (TC), E. coli (EC) and Chlorophyll-a (Chl-a), along with physical and chemical variables (Temperature, Suspended Solids, Total Dissolved Solids, Electrical Conductivity, Turbidity, Color, pH, DO, Al3+, NH4+, Cl-, Mg1+, Ca2+, total Phosphorous, NH3, NO3-, and SO42). Besides that, hydraulic-operational variables were also investigated: inflows (Qa), outflows (Qd), and variation in reservoir volume (Vol%). Multiple Regression Analyses showed that the key parameters were both significantly influenced by physio-chemical and hydraulic variables (0.46=R2adj=0.99, p<0.05). The DO showed significant spatial variation, being influenced by the turbulence from the Coaracy Nunes dam (UHECN) upstream and the UHEFG dam downstream. The Vol% influenced the TSI, which ranged from oligotrophic to hypertrophic and eventually stabilized at mesotrophic. The levels of TSI, TC, and Chl-a decreased and the level of E. coli increased (p<0.05) as a function of Vol%. A Cluster Analysis showed the formation of three spatial groups - two inside the reservoir and one downstream (P10). This suggests that in the rainy season or transition season, the hydraulic residence time in the reservoir is very low (16 = thr = 36 h) when hydrodynamic processes are dominant. In the dry season (thr ˜ 1 month), biogeochemical and hydrodynamic processes occur at similar time scales to the nutrient and microbial abundance. These results confirm the hypothesis that the filling phase has significant impacts on the key parameters of the water quality (p<0.05). We conclude that the filling phase of the UHEFG reservoir generated significant environmental impacts, which have repercussions even retrospectively.

Amazon, biogeochemistry, ecosystem, extreme event,↗