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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the MAJORANA DEMONSTRATOR 76 Ge neutrinoless double beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearites. Here, a simple measurement protocol was developed that did not require sophisticated equipment or lengthy data taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearites by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Maximum Likelihood Spectrum Decomposition for Isotope Identification and Quantification

A spectral decomposition method has been implemented to identify and quantify isotopic source terms in high-resolution gamma-ray spectroscopy in static geometry and shielding scenarios. Monte Carlo simulations were used to build the response matrix of a shielded high-purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared with decay-corrected values from the calibration certificate. For most nuclei in the standard ( 241 Am, 109 Cd, 137 Cs, and 60 Co), the deviations from the certificate values were generally no more than 6% with a few outliers as high as 10%. Furthermore, for 57 Co, the radionuclide with the lowest activity, the deviations from the standard reached as high as 25%, driven by the meager statistics in the calibration spectra. In addition, a complete treatment of error propagation for the technique is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Real-Time Ethernet Interface for NSTX-U’s Thomson Scattering Diagnostic (2023)

Here, the multipoint Thomson scattering (MPTS) diagnostic system at the National Spherical Torus Experiment Upgrade (NSTX-U) facility is undergoing an upgrade to operate in real-time and interface with the plasma control system (PCS) for NSTX-U. Previous prototyping efforts have shown that spectral analysis and rapid calculations of electron temperature and density are possible on a real-time Linux machine when using up to a 100-Hz laser pulse repetition rate. A remaining challenge was transferring the real-time data to NSTX-U’s PCS, which utilizes the front panel data port (FPDP) protocol. The original proposed method was to convert the real-time data into analog values, but a new solution was developed to keep the output format digital by using an Ethernet controller with a field-programmable gate array (FPGA). This article focuses on a new input module that has been developed to accept incoming user datagram protocol (UDP) packets sent over Ethernet, convert into FPDP format, and integrate into the existing data stream under NSTX-U’s real-time framework.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Climate reconstruction for the Entre‐Douro‐e‐Minho region ( NW Portugal) between AD 1626 and AD 1820: synthesis of viticulture data and foraminiferal evidence

This work presents two novel climate‐related time series for the northwest of Portugal. The first is an AD 1626–1820 triennial‐resolved wine production series, based on the Benedictine accounts from six monasteries of the Entre‐Douro‐e‐Minho ( EDM ) region. The second, an AD 1654–2010 benthic foraminiferal record from the Caminha salt marsh, located in the lower estuary of the Minho River. The series were analysed together for the common period to outline how both palaeoclimatic proxies respond to the most likely natural environmental drivers of temporal variability, solar forcing included. Singular spectral analysis revealed a common significant multidecadal periodicity agreeing with recognized long‐term changes in solar activity, i.e. the Lower Gleissberg cycle (50–80 years). The application of wavelet analysis allowed the detection of high coherence at this time scale (centred at c . 64 years) between marsh foraminifera and both total solar irradiance and the North Atlantic Oscillation index. This relationship persists throughout the c . AD 1730–1875 period. The continuous wavelet transform results for wine production were inconclusive. As the time‐span analysed is recognized as one of high socio‐economic and political distress, the main human‐driven impacts on wine production, particularly in the two periods of greatly reduced solar activity – the Maunder and Dalton Minima – are reviewed in the light of the available historical records. In addition to a documented climate‐related agricultural crisis in Portugal, damage and losses to wine production may have been triggered by several local and international conflicts in which the country was involved. But to what extent the two influences contributed to the wine production variations observed in the EDM region during both periods remains an open question.

Moreno, João↗
DOE OSTI2021

Elastic and thermodynamic properties of cerium-doped yttrium aluminum garnets

Cerium-doped yttrium aluminum garnets (Y 3-x Ce x Al 5 O 12 , Ce:YAGs) are promising yellow light-emitting components of solid-state white light-emitting diodes. Although there have been numerous studies examining the effects of Ce concentrations on the luminescent properties of Y 3-x Ce x Al 5 O 12 , the impacts of Ce dopant on the elastic and thermodynamic properties are not well understood. In this work, we used resonant ultrasound spectroscopy (RUS) to determine the effects of Ce doping (0.025, 0.1, 1 at. %) on the elastic and thermodynamic properties of Y 3-x Ce x Al 5 O 12 . The elastic moduli calculated via the Voigt–Reuss–Hill (VRH) method demonstrated that low Ce dopant concentrations (≤0.1 at. %) induced negligible effects on the elasticity of the YAG host matrix, while a high Ce concentration (1 at. %) yielded significant softening. RUS spectral analysis and SEM images suggested that the elastic softening originated from microstructural differences induced at higher Ce dopant concentrations. Additionally, we demonstrated an increase in elastic anisotropy at higher Ce concentrations, which further elucidated the correlations between structure and elasticity of Y 3-x Ce x Al 5 O 12 . Debye temperatures ($θ_D$), heat capacities ($C_p$), and thermal conductivities ($\mathit{κ}$) were calculated for Ce:YAGs through the relations of RUS-derived parameters (sound velocities, elastic moduli) and previously determined thermal expansion coefficients. Ce:YAG was found to have a significant reduction in $θ_D$, Cp, and $\mathit{κ}$ at Ce concentrations ≥1 at. %. Lastly, extrapolation of $C_p$ and $\mathit{κ}$ to higher temperatures allowed the modeling of thermal stress experienced by Y 3-x Ce x Al 5 O 12 disks up to 1073.15 K.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-T-398: Improving Radiotherapy Treatment Planning Using Dual Energy Computed Tomography Based Tissue Characterization

Purpose: Both kVp settings and geometric distribution of various materials lead to significant change of the HU values, showing the largest discrepancy for high-Z materials and for the lowest CT scanning kVp setting. On the other hand, the dose distributions around low-energy brachytherapy sources are highly dependent on the architecture and composition of tissue heterogeneities in and around the implant. Both measurements and Monte Carlo calculations show that improper tissue characterization may lead to calculated dose errors of 90% for low energy and around 10% for higher energy photons. We investigated the ability of dual-energy CT (DECT) to characterize more accurately tissue equivalent materials. Methods: We used the RMI-467 heterogeneity phantom scanned in DECT mode with 3 different set-ups: first, we placed high electron density (ED) plugs within the outer ring of the phantom; then we arranged high ED plugs within the inner ring; and finally ED plugs were randomly distributed. All three setups were scanned with the same DECT technique using a single-source DECT scanner with fast kVp switching (Discovery CT750HD; GE Healthcare). Images were transferred to a GE Advantage workstation for DECT analysis. Spectral Hounsfield unit curves (SHUACs) were then generated from 50 to 140-keV, in 10-keV increments, for each plug. Results: The dynamic range of Hounsfield units shrinks with increased photon energy as the attenuation coefficients decrease. Our results show that the spread of HUs for the three different geometrical setups is the smallest at 80 keV. Furthermore, among all the energies and all materials presented, the largest difference appears at high Z tissue equivalent plugs. Conclusion: Our results suggest that dose calculations at both megavoltage and low photon energies could benefit in the vicinity of bony structures if the 80 keV reconstructed monochromatic CT image is used with the DECT protocol utilized in this work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a "sniff-and-seek" mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus-molybdenum disulfide (bP-MoS2) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

Zhang, Dehui↗
DOE OSTI2017

Evaluation of the Component of Bias of X-Ray Microanalysis Related to Surface Relief of the Specimen

A semiempirical approach to the a priori evaluation of the effect of the surface relief on the results of X-ray spectral analysis of binary compounds is proposed. Dimensionless parameter k characterizing the effect of the relief on the intensity of intrinsic X-ray radiation is determined using the experimental data obtained for reference monocrystal specimens GaAs with known geometrical parameters of the relief.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Crystal structure of 2-(thiophen-2-yl)-1-(5-thioxo-4,5-dihydro-1,3,4-oxadiazol-2-yl) ethenyl]benzamide: N,N-dimethylformamide (1 : 1)

The title compound, 2-(thiophen-2-yl)-1-(5-thioxo-4,5-dihydro-1,3,4-oxadiazol-2-yl)ethenyl] benzamide:N,N-dimethylformamide (1: 1), (C{sub 15}H{sub 11}N{sub 3}O{sub 2}S{sub 2} · C{sub 3}H{sub 7}NO), was synthesized, and its structure was established by spectral analysis and X-ray diffraction studies. The compound crystallizes in the monoclinic space group P2{sub 1}/n with a = 10.8714(7), b = 9.0497(5), c = 19.8347(13) Å, β = 91.093(5)°, Z = 4. The crystal structure is stabilized by N–H···S, C–H···O and N–H···O hydrogen bonds. The π···π interactions are also observed between the rings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Randomized Projection for Rank-Revealing Matrix Factorizations and Low-Rank Approximations

Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation techniques. QR with Column Pivoting (QRCP) is usually suitable for these purposes, but it can be much slower than the unpivoted QR algorithm. For large matrices, the difference in performance is due to increased communication between the processor and slow memory, which QRCP needs in order to choose pivots during decomposition. Our main algorithm, Randomized QR with Column Pivoting (RQRCP), uses randomized projection to make pivot decisions from a much smaller sample matrix, which we can construct to reside in a faster level of memory than the original matrix. This technique may be understood as trading vastly reduced communication for a controlled increase in uncertainty during the decision process. Furthermore, for rank-revealing purposes, the selection mechanism in RQRCP produces results that are the same quality as the standard algorithm, but with performance near that of unpivoted QR (often an order of magnitude faster for large matrices). Additionally, we also propose two formulas that facilitate further performance improvements. The first efficiently updates sample matrices to avoid computing new randomized projections. The second avoids large trailing updates during the decomposition in truncated low-rank approximations. Our truncated version of RQRCP also provides a key initial step in our truncated SVD approximation, TUXV. These advances open up a new performance domain for large matrix factorizations that will support efficient problem-solving techniques for challenging applications in science, engineering, and data analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2025

Initial measurement of reactor antineutrino oscillation at SNO+

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference Δ$m^2_{21}$. In the ranges allowed by previous measurements, the best-fit Δ$m^2_{21}$ is ($8.85^{+1.10}_{-1.33}$) × 10 -5 eV 2 . This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on Δ$m^2_{21}$ with about three years of data. In the following, we first describe the configuration of the SNO+ detector when it was partially filled with scintillator, and a characterization of the detector response using intrinsic radioactivity. Next, we detail the event selection and expectations for reactor IBDs and (α, n) reactions. Then, we present the results of an energy spectrum analysis using 114 tonne-years of data. We conclude with prospects of future results from the SNO+ detector, which has been operating fully-filled with 780 tonnes of scintillator

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Three-beam rotational coherent anti-Stokes Raman spectroscopy thermometry in scattering environments

Three-beam rotational coherent anti-Stokes Raman scattering (CARS) measurements performed in highly scattering environments are susceptible to contamination by two-beam CARS signals generated by the pump–probe and Stokes–probe interactions at the measurement volume. If this occurs, differences in the Raman excitation bandwidth between the two-beam and three-beam CARS signals can add significant errors to the spectral analysis. This interference to the best of our knowledge has not been acknowledged in previous three-beam rotational CARS experiments, but may introduce measurement errors up to 25% depending on the temperature, amount of scattering, and differences between the two-beam and three-beam Raman excitation bandwidths. Here, the presence of two-beam CARS signal contamination was experimentally verified using a femtosecond–picosecond rotational CARS instrument in two scattering environments: (1) a fireball generated by a laboratory-scale explosion that contained particulate matter, metal fragments, and soot, and (2) a flow of air and small liquid droplets. A polarization scheme is presented to overcome this interference. By rotating the pump and Stokes polarizations +55° and –55° from the probe, respectively, the two-beam and three-beam CARS signals are orthogonally polarized and can be separated using a polarization analyzer. Using this polarization arrangement, the Raman-resonant three-beam CARS signal amplitude is reduced by a factor of 2.3 compared to the case where all polarizations are parallel. This method is successfully demonstrated in both scattering environments. A theoretical model is presented, and the temperature measurement error is studied for different experimental conditions. The criteria for when this interference may be present are discussed.

42 ENGINEERING↗
DOE OSTI2020

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Seismoacoustic Signatures Observed During a Long-Term Deployment of Infrasound Sensors at the Nevada National Security Site

Earthquakes have repeatedly been shown to produce inaudible acoustic signals (<20 Hz), otherwise known as infrasound. These signals can propagate hundreds to thousands of kilometers and still be detected by ground-based infrasound arrays depending on the source strength, distance between source and receiver, and atmospheric conditions. Another type of signal arrival at infrasound arrays is the seismic induced motion of the sensor itself, or ground-motion-induced sensor noise. Measured acoustic and seismic waves produced by earthquakes can provide insight into properties of the earthquake such as magnitude, depth, and focal mechanism, as well as information about the local lithology and atmospheric conditions. Large earthquakes that produce strong acoustic signals detected at distances greater than 100 km are the most commonly studied; however, more recent studies have found that smaller magnitude earthquakes ($M$ w <2.0) can be detected at short ranges. In that vein, this study will investigate the ability for a long-term deployment of infrasound sensors (deployed as part of the Source Physics Experiments [SPE] from 2014 to 2020) to detect both seismic and infrasonic signals from earthquakes at local ranges (<50 km). Methods used include a combination of spectral analysis and automated array processing, supported by U.S. Geological Survey earthquake bulletins. Further, this investigation revealed no clear acoustic detections for short range earthquakes. However, secondary infrasound from an $M$ w 7.1 earthquake over 200 km away was detected. Important insights were also made regarding the performance of the SPE networks including detections of other acoustic sources such as bolides and rocket launches. Finally, evaluation of the infrasound arrays is performed to provide insight into optimal deployments for targeting earthquake infrasound.

58 GEOSCIENCES↗
DOE OSTI2016

Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices

The Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices (Project) investigated, analyzed and modeled advanced turbine control schemes with the objective of increasing the energy harvested by hydrokinetic turbines in turbulent flow. Ocean Renewable Power Company (ORPC) implemented and validated a feedforward controller to increase power capture; and applied and tested the controls on ORPC’s RivGen® Power Systems in Igiugig, Alaska. Assessments of performance improvements were made for the RivGen® in the Igiugig environment and for ORPC’s TidGen® Power System in a reference tidal environment. Annualized Energy Production (AEP) and Levelized Cost of Energy (LCOE) improvements associated with implementation of the recommended control methodology were made for the TidGen® Power System in the DOE reference tidal environment. System Performance Advancement (SPA) goals were selected for the project. SPA targets were to improve Power to Weight Ratio (PWR) and system Availability, with the intention of reducing Levelized Cost of Electricity (LCOE). This project focused primarily reducing in PWR. Reductions in PWR of 25.5% were achieved. Reductions of 20.3% in LCOE were achieved. This project evaluated four types of controllers which were tested in simulation, emulation, a laboratory flume, and the field. The adaptive Kω2 controller performs similarly to the non-adaptive version of the same controller and may be useful in tidal channels where the mean velocity is continually evolving. Trends in simulation were largely verified through experiments, which also provided the opportunity to test assumptions about turbine responsiveness and control resilience to varying scales of turbulence. Laboratory experiments provided an essential stepping stone between simulation and implementation on a field-scale turbine. Experiments also demonstrated that using “energy loss” as a metric to differentiate between well-designed controllers operating at an optimal tip-speed ratio set-point is difficult, which anticipated the outcome from field experiments. The clear message is that the feedforward Kω2 controller out-performs the feedback controllers in almost all aspects and modes of evaluation. The controllers proved a substantial improvement over the baseline performance of the TidGen® turbine, in terms of energy capture. The effects of noise-contaminated angular velocity signals were investigated and validated by simulation as an explanation for the performance limitations observed for TidGen® turbine operations in Eastport, Maine. Measurements of loads performed as part of the laboratory testing indicate that there are limited differences in average axial thrust force between control architectures. This suggests that none of the control strategies are likely to substantially affect loads on the turbine support structure. Velocity measurements during the ORPC RivGen® turbine deployment at Igiugig, Alaska, in 2014 were used to assess the variability of the river flow. Results suggest that the river flow is approximately steady, in the mean sense, at any particular location in the river, with random turbulent fluctuations that are around 10% of the mean flow. The mean flow in the center channel of the river is 2.5 m/s, with reductions near the riverbanks and in the shallows. Spectral analysis and lagged correlation results indicate that temporal fluctuations at a given point are dominated by large scale fluctuations, such that measurements at the turbine location are just as useful for inflow control implementation as upstream measurements. At this site, and likely at many other river sites, flow is generally steady at a given location, but flow varies dramatically between locations, particularly laterally across the river. The primary result is that a lateral change in position of a few meters results in changes to flow speed that far exceed the turbulence fluctuations at any given location. The turbulence is dominated by long time scales. Following final system tests, the RivGen® device was submerged and each evaluated controller was tested across a range of gain/set point values and filter configurations for a minimum of 5 minutes, with longer runs attempted for well-performing cases. In addition to testing controllers during the 2015 deployment season, LGL Alaska Research Associates Inc. (LGL) performed a fish monitoring study in compliance with Alaska Department of Fish and Game fisheries habitat permit for the project. During the season, LGL reviewed 10% of 179 one-hour blocks of footage (6 minutes on the hour) and documented a total of over 1200 fish in the vicinity of the RivGen® device, including over 800 salmon smolt and over 350 adult salmon. No evidence of adverse effects including passage delay by upstream migrating salmon was noted. This is an important result for future deployments and has a direct impact on commercial system designs.

16 TIDAL AND WAVE POWER↗
DOE OSTI2018

Directional Unfolded Source Term (DUST) for Compton Cameras.

A Directional Unfolded Source Term (DUST) algorithm was developed to enable improved spectral analysis capabilities using data collected by Compton cameras. Achieving this objective required modification of the detector response function in the Gamma Detector Response and Analysis Software (GADRAS). Experimental data that were collected in support of this work include measurements of calibration sources at a range of separation distances and cylindrical depleted uranium castings.

47 OTHER INSTRUMENTATION↗