Lag Model Prediction of the NASA Juncture Flow
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Accurately estimating the distribution of aquifer properties is key to understanding contaminant movement in the subsurface. The distribution of aquifer properties is typically addressed using slug or constant-rate well tests, and the pros and cons of these tests are well known. Slug tests are appealing because they avoid removing contaminated water, but their results are affected by well skin and the small volume of displaced water limits the volume of aquifer that can be evaluated. Constant-rate well tests have the disadvantage of requiring disposal of potentially contaminated water, but they can generate properties that are more representative than slug tests, and they can be used to estimate well efficiency and storativity, which are difficult to characterize using slug tests. Periodic pumping tests are appealing because they have many of the advantages and few of the disadvantages of slug and constant-rate well tests. Periodic pumping tests involve cycling the pumping rate with a regular period and measuring the resulting response in monitoring wells. Some of these tests involve imposing a periodic rate on a constant mean rate. However, other tests involve moving water out and back into the well at rates that are balanced so the net rate after each period is zero. Some zero-net-rate (ZNR) well tests use rates that follow a sinusoidal pattern, whereas others use a square wave pattern that switches between a constant rate of pumping and a constant rate of injection to achieve a zero-net rate. ZNR well tests appear to be a useful compromise between slug and constant rate tests, but methods for conducting and analyzing the results from these tests have received limited evaluation. Periodic pumping tests are typically evaluated through recorded pressure responses to a disturbance source. Recent work has demonstrated that the strain field in the vadose zone shows a response to disturbances in the underlying aquifer. Various instrument designs are available that can record vertical and horizontal strain to very precise resolutions (10-9 ε). Measuring the strain in the vadose zone can present lower costs than measuring pressure in a monitoring well, so using strain could improve the resolution of ZNR tests, as well as make these tests potentially cheaper. A ZNR periodic pumping system was constructed and used to perform tests in the Clemson, South Carolina area. The system was designed to pump and inject with a periodic square-wave, so pumping occurs at a constant rate for half the period and is followed by injection at the same rate for half the period resulting in no net gain or loss of water from the aquifer. The system is designed to generate flow rates from 1 to 3.5 gallons per minute (gpm) with a capacity of 875-gallon per half period. Pressure and strain monitoring points are located in the vicinity of the pumping well. A series of 10 ZNR periodic pumping tests were conducted at the field site, with periods ranging from 2 to 540 minutes. Pressure data were collected and recorded in four monitoring wells in various locations around the pumping well. In addition, vertical and horizontal strain and tilt data were recorded in various locations around the pumping well. Traditional aquifer tests, including slug tests and constant-rate pumping tests were conducted at the field site to provide a baseline of aquifer parameter estimates that are compared the results of ZNR tests. Pressure data measured at monitoring wells during ZNR tests were analyzed utilizing an analytical solution (Streltsova, 1988) that assumes a confined aquifer and estimates hydraulic diffusivity and transmissivity using the time lag and amplitude of the pressure signal. The storativity can be separated out from the hydraulic diffusivity using the transmissivity estimate. The time-lag and amplitude of the pressure signals were estimated using a Fourier transform. The time-lag of the primary period of the pressure increases as a linear function of distance from the pumping well, and it increases as the square root of the period of the ZNR test over a range of periods spanning two orders of magnitudes. The first few harmonics follow similar trends, but the higher frequency harmonics diverge from this trend. Strain and tilt data from various instruments in the vadose and saturated zones are periodic during ZNR tests. The time lags of the strain components increase roughly linearly with distance from the well. Many of the time lags of the strain data from the vadose zone are similar to the time lags observed in the pressure data from a similar distance and pumping period. Theoretical experiments were performed to understand how pressure and strain responds to ZNR periodic pumping. The time-lags of the simulated pressure responses match the field data trends. The results were used to validate the Streltsova (1988) solution for periodic pumping to estimate hydraulic diffusivity. Hydraulic diffusivity was estimated to be 1.8 x 10-2 m2s-1 < Dh < 9.0 x 10-2 m2s-1 using pressure data from all the wells during ZNR tests. Transmissivity was estimated to be 0.8 x 10-4 m2s-1 < T < 4.3 x 10-4 m2s-1. Assuming Storativity = T/Dh gives 1.1 x 10-3 < S < 19 x10-3. Transmissivity is used to estimate the hydraulic conductivity, K, by dividing by the assumed aquifer thickness. Two constant-rate pumping tests were conducted and analyzed using two conceptual models: The Hantush (1961) solution was used to analyze data assuming confined conditions and the Neuman (1974) solution was used to analyze data assuming unconfined conditions. The Hantush (1961) solution estimated transmissivity to be 1.6 x 10-4 m2s-1 < T < 1.8 x 10-4 m2s-1 when using data from all the wells. Storativity was estimated to be 4.4 x 10-3 < S < 8.2 x 10-3. Assuming Dh = T/S gives 0.22 x 10-2 m2s-1 < Dh < 3.6 x 10-2 m2s-1. The Neuman (1974) solution estimated transmissivity to be 0.80 x 10-4 m2s-1 < T < 0.84 x 10-4 m2s-1. Total storativity (S + Sy) was estimated to be 22 x 10-3 < S < 110 x 10-3. Storativity excluding the Sy term was estimated to be 2.2 x 10-3 < S < 2.5 x 10-3. Assuming Dh = T/S gives 0.073 x 10-2 m2s-1 < S < 0.36 x 10-2 m2s-1. Two slug tests were performed on the pumping well, PW-2. The Bouwer and Rice (1976) solution for slug tests was used to analyze the data. This solution works for both confined and unconfined conditions, so both conceptual models were covered through the analysis. Hydraulic conductivity was estimated to be 1.95 x 10-6 ms-1 < K < 2.49 x 10-6 ms-1. Statistical comparison show generally no significant difference in the estimates of Dh, K, or S made using ZNR and conventional pumping tests.. However, the total storage estimated by the Neuman (1974) solution for unconfined settings is larger than that estimate using the Streltsove analysis applied to the ZNR tests. This is likely because the Neuman solution considers delayed yield from storage that the water table, whereas the Streltsova (1988) solution assumes confined conditions. The analysis also indicates that there is a statistically significant difference between parameters estimated with conventional slug tests and those measured with either constant-rate pumping tests or ZNR tests. Hydraulic diffusivity is estimated from the time-lag and the distance from the well. The time-lags of the strain data from the vadose zone are similar to the lags of the pressure data from a similar distance. This indicates that hydraulic diffusivities estimated from the strain data measured in the vadose zone would be similar to the estimates from the pressure data measured in the aquifer. There appear to be sign reversals in some of the strain data that were corrected to estimate the time lag. The dissolved oxygen (CO) concentration was measured during several ZNR tests to evaluate the feasibility of using the procedure to increase contaminant degradation kinetics that are related to DO. The results indicate that DO can be increased during the cycling of ZNR tests in some cases.
Abstract The high temporal variability of the soil‐to‐atmosphere CO 2 flux (soil respiration, R S ) has been studied at hourly to multiannual time scales but remains less well understood than R S spatial variability. How R S fluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of R S over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global R S database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11–12%) time scales. Successive R S values measured at a given collar exhibited a strong hour‐to‐hour correlation ( r = 0.931) and a moderate correlation at a 2‐hr lag (0.289); day‐to‐day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily R S means were well correlated at a 1‐day lag ( r = 0.856) but not at any further time lag. In a linear mixed‐effects model predicting R S , soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of R S fluxes, quantify sampling requirements, and improve error propagation.
Simultaneous X-ray, ultraviolet, optical, infrared, and radio monitoring data were used to test and constrain models of continuum emission from the BL Lacertae object PKS 2155-304. Intensively sampled ultraviolet and soft X-ray light curves showed a clear temporal correlation with the X-rays leading the ultraviolet by 2-3 hr. This lag was found to be significantly different from zero after an exhaustive comparison of four different techniques for measuring temporal correlations. Variations in the ultraviolet trough optical wave bands were also strongly correlated, with no measurable lag down to limiting timescales of approximately less than 1-2 hr. This strong correlation extends to the near-infrared, but the less intensive sampling precludes measurement of any lag beyomnd an upper limit of approximately less than 1 day. These lags and limits of the order of hours are much shorter than most rapid observed single-band variations. Because of the very sparse radio sampling, it was not possible to measure quantitatively the correlation and lag with shorter wavelengths, but the data do suggest that the radio may lag the optical/ultraviolet by approximately 1 week, with longer delays and weaker variations to longer radio wavelengths. The epoch-folding Q(exp 2) statistic was used to test for periodicity, and no evidence for strict or quasi-periodicity was found in any of the light curves. Because they lead the lower frequencies, the soft X-rays (approximately less than 1 keV) cannot arise from synchrotron self-Compton scattering. These results also rule out the accretion disk model, which predicts a measurable lag between ultraviolet/optical wavelength bands and a correlation between hardness and brightness, neither of which were seen. They are consistent with the entire radio through X-ray continuum arising from direct synchrotron emission from a relativistic jet. However, the tapered jet model, in which the X-ray emission is produced closer in, has problems explaining the magnitude of the ultraviolet/X-ray lag, because the X-ray-emitting electrons have very short lifetimes (t(sub 1/2) much less than 1 s). The result that the lag is much smaller than the variability timescale suggests instead that the radiation may be produced in a flattened region such as a shock front.
Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.
At the time of this testing, the Low-Activity Waste Pretreatment System (LAWPS) was to provide for the initial production of immobilized low-activity waste by feeding Hanford tank supernate from tank farms to the Hanford Tank Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility for immobilization. Washington River Protection Solutions requested that Hanford tank waste collected from tank 241-AP-107 (hereafter called AP-107) be processed using conceived pretreatment steps (suspended solids removal by filtration, Cs removal by ion exchange) then vitrified. A small-scale test platform to demonstrate the solids filtration, Cs removal, and LAW vitrification was constructed and installed at Pacific Northwest National Laboratory. Bench-scale ion exchange testing with approximately 9 L of AP-107 supernate was conducted using crystalline silicotitanate (CST) ion exchange media. The IONSIV R9140-B CST was provided by Honeywell UOP, LLC in 2018 (Batch 2081000057). The ion exchange media was first tested with simulant and was previously described. This report describes the Cs ion exchange batch contact and column test results with the AP-107 tank waste. Batch contact testing helps to evaluate CST performance on tank waste supernate prior to processing it in the ion exchange columns. Batch contacts were performed with the waste at four Cs concentrations at a phase ratio of 200 (liquid volume to exchanger mass) with AP-107. The distribution coefficient (K d ) at the equilibrium condition of 8.57 µg Cs/mL (AP-107 feed condition) was determined to be 669 mL AP-107/g CST. With a CST bed density of 1.00 g/mL, this K d corresponded to a predicted 50% Cs breakthrough of 669 bed volumes (BVs). The Cs load capacity at the equilibrium feed condition was determined to be 7.5 mg Cs/g dry CST. The column testing was prototypic to the intended LAWPS operations in a lead-lag column format, although on a small-scale basis with 10-mL CST beds. The feed was processed downflow through the lead column and then through the lag column at ~2.2 BV/h. Loading continued until the lag column reached the WTP waste acceptance criteria (WAC) for receiving supernatant waste for vitrification (a function of the Na and 137 Cs concentrations). For AP-107, the WAC is 0.114% of the influent 137 Cs concentration; this required a Cs decontamination factor of 876. The Cs effluent from the lag column reached the WAC after processing ~410 BVs. To keep the subsequent product effluent below the WAC, a replacement lag column was prepared, the lead column was removed from service (after processing a total of 471 BVs), the lag column was put into the lead column position, and the replacement lag column was installed. Feed processing continued and after another ~290 BVs the Cs effluent from the lag column again exceeded the WAC. In both cases, the lead columns only reached 25% Cs breakthrough before removal. Although 50% Cs breakthrough was not reached, this value was estimated and averaged based on extrapolation of the loading curves (640 BVs) and agreed within 4% of the predicted 50% Cs breakthrough from batch contact test results (669 BVs). Table ES.1 summarizes the observed column performance and relevant Cs loading characteristics.
The influence of basic parameters that govern flap lag stability of hingeless rotor blades in hover is reviewed, and potential methods are studied for improving the lead lag damping of soft inplane configurations for low thrust conditions. These conditions are relevant for ground and air resonance stability of coupled rotor body dynamic systems. Results indicate that the isolated rotor blade lead lag damping can be usefully increased by a combination of flap lag elastic coupling and pitch lag coupling. For a typical soft inplane configuration, 6% of critical damping can be obtained for moderate pitch lag coupling. For large values of the coupling parameters, the lead lag frequency is substantially reduced at high pitch angles and airfoil stall effects also reduce the lead lag damping.
The intensity variations of Cyg X-2 and GX 5-1 are analyzed with a Fourier cross-spectral technique making it possible to measure the frequency dependence of the time lags between the intensity variations in two X-ray spectral bands. In the 20-40 Hz frequency range of the quasi-periodic oscillations (QPO), the hard (about 5-18 keV) signal lags the soft (about 1-5 keV) one, while the reverse is true for the associated red noise. The observed time lags range from 0.4 to 8 ms. For similar QPO parameters, the observed lags are much smaller in GX 5-1 than they are in Cyg X-2. The 'hard lags' previously measured in the cross-correlation functions of these sources are now identified as due to lags in the QPO. It is argued that the 'soft lags' now detected in the red noise are not inconsistent with the interpretation of the hard lags as due to inverse Compton scattering.
A rigid flap-lag blade analysis was developed to simulate a rotor in a wind tunnel undergoing an emergency power shutdown. Results show that for a rotor at a nonzero shaft tilt angle undergoing an emergency power shutdown, the oscillatory lag response is divergent. The mean lag response is large when tested at high collective pitch angles. Reducing the collective pitch during the emergency shutdown reduces the steady lag response. Increasing the rotor shaft tilt angle increases the oscillatory lag response component. The blade lag response obtained by incorporating a nonlinear lag damper model indicates that in this case the equivalent linear viscous damping is lower than originally expected. Simulation results indicate that large oscillatory lag motions can be suppressed if the rotor shaft is returned to the fully vertical position during the emergency power shutdown.
Variation of the sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) is investigated focusing on the decadal difference in time-lagged response to ENSO between the late 20th century (1980-2000, L20) and the early 21st century (2001-2021, E21). Moving correlation of the ENSO index against the SIF and sea surface temperature (SST), respectively, over the RAB region indicates that they are more strongly linked in the L20 than in the E21, with a time lag of up to two seasons. Time-lagged regressions onto the mature phase of El Niño reveal that the SIF decrease and SST increase over the RAB is relatively weaker in the E21 and most pronounced at a shorter time-lag of 0-4 months. In contrast, the SIF melt in the L20 continues to grow and reaches its peak at approximately two season lag (5-7 month). Near-surface wind, upper-level circulation and geopotential height, and wave activity in response to the ENSO in the L20 explain that they create a zonally oriented system of high- and low-pressure areas with a two-season lag, enhancing the poleward meridional flow that plays a key role in sea ice melt in the Ross and Amundsen Sea during El Niño. However, in the E21, this pattern is no longer active at the same lag, and the circulation determines the region of maximum sea ice melt east of the pattern observed in the L20. This study suggests that stronger ENSO on average and higher frequency of eastern Pacific ENSO occurrence in the L20 are associated with this decadal change in the lagged response of the RAB SIF to ENSO.