Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hydraulics”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Pressure feedback control of electro-hydraulic actuators using fixed displacement hydraulic machines

This paper presents the development of a control strategy for Electro-Hydraulic Actuator (EHA) systems based on fixed displacement hydraulic machines. Although some previous studies on EHAs show benefits in terms of energy efficiency and regeneration capability compared to conventional hydraulic systems, few studies focus on the dynamic performance of EHAs. Unlike traditional valve-controlled systems, an EHA based on a fixed displacement pump typically relies on variation of the electric motor shaft speed to control the actuator velocity. This study shows that pressure feedback can be used to control the speed of the electric machine, thus achieving the required actuator/machine dynamic response. A simulation model is developed by including the EM, the hydraulic system, and the actuation system, which can give a realistic prediction of the EHA performance for a typical duty cycle. An open-circuit EHA previously proposed by the authors is used to validate the control strategy, and the simulation results show superior dynamic performance, exhibiting a reduction in response time and oscillatory behavior, compared to a feedforward EHA controller. Following the method presented in this study, it is possible to design a controller with a pressure feedback strategy for EHA systems with different size and actuation requirements

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Salinity and hydraulic retention time induce membrane phospholipid acyl chain remodeling in Halanaerobium congolense WG10 and mixed cultures from hydraulically fractured shale wells

Bacteria remodel their plasma membrane lipidome to maintain key biophysical attributes in response to ecological disturbances. For Halanaerobium and other anaerobic halotolerant taxa that persist in hydraulically fractured deep subsurface shale reservoirs, salinity, and hydraulic retention time (HRT) are important perturbants of cell membrane structure, yet their effects remain poorly understood. Membrane-linked activities underlie in situ microbial growth kinetics and physiologies which drive biogeochemical reactions in engineered subsurface systems. Hence, we used gas chromatography–mass spectrometry (GC–MS) to investigate the effects of salinity and HRT on the phospholipid fatty acid composition of H. congolense WG10 and mixed enrichment cultures from hydraulically fractured shale wells. We also coupled acyl chain remodeling to membrane mechanics by measuring bilayer elasticity using atomic force microscopy (AFM). For these experiments, cultures were grown in a chemostat vessel operated in continuous flow mode under strict anoxia and constant stirring. Our findings show that salinity and HRT induce significant changes in membrane fatty acid chemistry of H. congolense WG10 in distinct and complementary ways. Notably, under nonoptimal salt concentrations (7% and 20% NaCl), H. congolense WG10 elevates the portion of polyunsaturated fatty acids (PUFAs) in its membrane, and this results in an apparent increase in fluidity (homeoviscous adaptation principle) and thickness. Double bond index (DBI) and mean chain length (MCL) were used as proxies for membrane fluidity and thickness, respectively. These results provide new insight into our understanding of how environmental and engineered factors might disrupt the physical and biogeochemical equilibria of fractured shale by inducing physiologically relevant changes in the membrane fatty acid chemistry of persistent microbial taxa. GRAPHICAL ABSTRACT Salinity significantly alters membrane bilayer fluidity and thickness in Halanaerobium congolense WG10.

03 NATURAL GAS↗
DOE OSTI2022

Multiscale thermal hydraulic coupling methods for boiling water reactor simulation

During the last two years, the Virtual Environment for Reactor Applications (VERA) has been extended to simulate boiling water reactors (BWRs). The thermal hydraulic effects present in BWRs are far more complex than those in pressurized water reactors (PWRs). Therefore, the runtime is significantly increase and convergence behavior worse compared to PWRs. Most of the additional runtime is spent during the first few coupled iterations, when the power shape is still rapidly evolving, dramatically affecting the thermal hydraulics (TH). To alleviate the increased computational expense, a multiscale TH coupling approach was developed in VERA in which a highly efficient, simplified TH model is solved for several coupled iterations until the power shape is partially converged. The simplified solution is used to inform the assembly-wise flow distribution in the high-fidelity TH solver CTF, reducing the amount of work required to properly balance the pressure drop in each channel and reducing runtime for the couple calculation. Because the final TH calculations are performed with CTF, there is ultimately no impact on the accuracy of the converged solution. This paper presents the details of this multiscale TH coupling approach, along with results for a 4x4 array of GE-14 fuel bundles and whole-core coupled simulations of the Hatch core. These two cases show that the multiscale approach dramatically reduces the runtime of coupled BWR simulations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

MURR LEU structural and thermal hydraulics analyses: Part II – Impacts of irradiation thermo-mechanical behavior on thermal hydraulics safety analyses

A series of structural analyses have been performed to support the conversion of the University of Missouri Research Reactor (MURR) from the use of highly enriched uranium (HEU; ≥20 wt% U-235) to low-enriched uranium (LEU; <20 wt% U-235) fuel. The irradiation thermo-mechanical analysis evaluated the effects of fuel swelling, irradiation creep, thermal expansion, as well as thermal resistance from the oxide layer growth for the MURR LEU element in prototypic thermal and irradiation conditions as presented in Part I of this article. Overall, this irradiation thermo-mechanical analysis predicts smaller gap thickness reductions in previously limiting regions, and larger reductions in the middle of the outermost end channels where power density is not typically a maximum. Due to substantial differences between the channel gap reductions assumed for the previous safety analyses and those predicted by the irradiation thermo-mechanical analysis, a need to evaluate their impact on the thermal hydraulics safety analyses arose. This article presents the results from the steady-state safety analysis for normal operation as well as the two most limiting accident scenarios. The calculation models were revised in order to account for the spatial and temporal variation of the channel gap thicknesses. The results show that sufficient safety margins are still maintained for normal operation as well as during the postulated accident transients. This work provides a methodology of incorporating the irradiation thermo-mechanical behavior of plate-type fuel into the thermal hydraulics safety analyses.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Experimental Validation of Thermal Hydraulic Behavior in Sodium Fast Reactors (SFR) with the Thermal Hydraulic Experimental Test Article (THETA)

Thermal stratification and transition to natural circulation pose two of the largest sources of uncertainty in systems-level modeling of liquid metal-cooled fast reactors. As these phenomena typically develop during transient event sequences, licensing-basis events analyzed using systemslevel models may have considerable uncertainties associated with thermal-hydraulic parameters of the system to account for these phenomena. As a result, the validation basis for these phenomena for systems-level codes is insufficient to fully support the wide range of liquid metal fast reactors being developed in the US. Currently, the most viable path for licensing a design is to take significant conservatisms and maintain sufficiently large safety margins to account for this uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2020

Predicting Saturated Hydraulic Conductivity over Time for Degrading Saltstone Vault Concrete - 20376

At the Savannah River Site (SRS), low-level liquid tank waste is mixed with cementitious materials to produce a solid, monolithic waste form referred to as saltstone. The liquid saltstone mixture is pumped into massive concrete structures referred to as saltstone disposal units (SDUs) for curing and long-term storage. SDUs are vaults made of high performance concrete forming a barrier between saltstone and the surrounding environment. Predicting the long-term performance of SDU vault concrete, as well as the saltstone material itself, is critical to understanding the potential releases of radionuclides to the surrounding environment as described in the Saltstone Disposal Facility (SDF) performance assessment (PA). The hydraulic conductivity profile for SDU vault concrete was conservatively assumed to degrade linearly from the initial saturated hydraulic conductivity of the intact concrete over a long period of time. The initial hydraulic properties of saturated SDU vault concrete have been measured, and the final hydraulic properties of degraded vault concrete are assumed to be the same as the porous backfill material that will surround the vault after closure. The assumed final condition of the vault concrete is also conservative in that it provides a greater estimate of water penetration through the vault than would generally be expected. Therefore, insights that provide more realistic but conservative estimates as to how degradation processes may change the hydraulic properties of the SDU vault concrete, the time dependent relationship of the vault concrete hydraulic properties to degradation (and thus time), and the final hydraulic properties of the vault concrete provides useful additions to the SDF PA. This paper focuses on the assumption that the saltstone vault concrete saturated hydraulic conductivity would decrease linearly as a function of degradation time; this assumption resulted from a worst-case, weighted (by relative layer thickness), arithmetic average of intact and backfill material hydraulic conductivities representing flow parallel to a layered system. This research indicates that the effective saturated hydraulic conductivity of the degraded SDU vault concrete should be based on the relative thickness-weighted geometric mean of the intact and degraded layers hydraulic conductivities. This alternative, which is supported extensively in the relevant literature and evaluated in this paper, leads to a more realistic and defensible estimate of the effective saturated hydraulic conductivity in degraded vault concrete while remaining conservative. The impact of using the more realistic estimate of the effective saturated hydraulic conductivity for the degraded concrete decreases the effective saturated hydraulic conductivity by up to several orders of magnitude for early times (i.e., when the SDU vault concrete is assumed degraded via sulfate attack and carbonation). This revised estimate of the effective saturated hydraulic conductivity has been implemented in the recently revised SDF PA. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Geothermal Hydraulic Stimulation: Overview of Methods and Best Practices

This paper outlines important information related to the hydraulic stimulation of geothermal wells and considerations for regulators responsible for evaluating applications to conduct these operations. Hydraulic stimulation applied to geothermal wells is an evolving technology that is different from the hydraulic fracturing process widely used to complete unconventional oil and gas wells. Important differences relate to operating pressures, variations in rock and fracture processes, and characteristics and disposal of hydraulic fracture fluids. Hydraulic stimulation is not widely used in the geothermal industry; however, future development of enhanced geothermal systems (EGS) will require hydraulic stimulation in order to enhance and create fracture permeability to allow wells to deliver sufficient heat and fluid to power plants. Gigawatts of EGS potential have been identified (U.S. Department of Energy 2019), so it is expected that the use of hydraulic stimulation of geothermal wells will be more common in the future. At present, there are no formal regulations guiding drilling programs or sundry notices that propose hydraulic stimulation of geothermal wells. On federal lands, important constraints and oversight are embedded in the process for obtaining Geothermal Drilling Permits outlined in the Code of Federal Regulations (43 CFR Section 3262.11); in Geothermal Resource Order 2 (GRO 2) guidance for assessment and mitigation of impacts of geothermal operations as well as construction and testing of geothermal wells; and in a Bureau of Land Management (BLM) Induced Seismicity Instruction Memorandum (BLM 2018). Additionally, local knowledge from BLM field offices and expertise of BLM engineers, or that of state regulators for operations on private and state lands, will be applied to fully assess operator applications to conduct geothermal hydraulic stimulation activities. Significant research effort has been directed toward EGS, so geothermal hydraulic stimulation techniques, impacts, and results are evolving and becoming better understood, including adaptation of decades of oil and gas industry experience with hydraulic fracturing of unconventional reservoirs. Recent EGS activities in the United States provide details about hydraulic stimulation with respect to oversight, stimulation design, execution, and results. Based on experience to date, the important issues to address when hydraulically stimulating a geothermal well include the following: 1. Wellbore construction and integrity must be appropriate to protect groundwater and manage stimulation pressures. 2. Understanding of lithology, faults, fractures, and subsurface stress state is necessary to design stimulation plans and predict results. 3. Seismic monitoring allows for observation and mitigation of induced seismicity.

geothermal↗
DOE OSTI2020

Interpretation of Hydraulic Fractures Based on Microseismic Response in the Marcellus Shale, Monongalia County, West Virginia, USA: Implications for Shale Gas Production

Summary Hydraulic fracturing is critical for extracting shale gas in the subsurface. The treatment technique of multistage hydraulic fracturing is widely used to maximize production. In multistage hydraulic fracturing, not all pumping stages make the same contribution for production, although the designed stimulation process is almost same in every pumping stage. In this study, we characterize the microseismic responses of hydraulic fracturing. Significant variations of microseismic characteristics are observed among the different pumping stages. Pre-existing natural fractures are examined along the horizontal well in selected stages. Combined with the Mohr’s circle analysis, results of the fracture study show that induced hydraulic fractures can be captured by pre-existing natural fractures. Induced hydraulic fractures are simulated by the unconventional fracture model (UFM), and the result reveals that the stress-shadow effect diverts the direction of hydraulic fractures. The diverted hydraulic fracture affects the development of the hydraulic fracture network, which has influence on production. The treatment of two-step pumping is investigated by application of hydraulic diffusivity. Hydraulic fracturing performance and production could be optimized by the treatment of two-step pumping in a single stage. The second pumping creates new fractures and fills the fractures with additional proppants to maintain production for a long duration.

Energy & Fuels↗
DOE OSTI2022

Hybrid Hydraulic-Electric Architecture (HHEA) for Mobile Machines (Final Report)

Traditionally, off-road mobile machines such as excavators and wheel loaders are primarily powered by hydraulics, and throttling valves are used to control their work circuits. In recent years, two general trends are towards more energy efficient systems and electrification. With electrification, both efficiency and control performance can be improved by the elimination of throttling losses and the use of high-bandwidth inverter control. Electrification is generally accomplished with Electro-hydraulic actuators (EHA) but they are limited to lower powered systems due to the high cost of electric machines capable of high power or high torque. This project proposes a new system architecture for off-road vehicles - Hybrid Hydraulic Electric Architecture (HHEA) to improve efficiency and control performance without requiring large electric machines. The widely applicable architecture combines hydraulic power and electric power in such a way that the majority of power is provided hydraulically while electric drives are used to modulate this power. In particular, HHEA utilizes multiple common pressure rails to transmit the majority of power and small electric machines to modulate the power. The energy-saving potential of the the HHEA has been validated for the work circuits of a variety of mobile machines, from small 5-ton excavators to medium sized 20-ton excavators and wheel loaders, and representative duty cycles to reduce energy input by 50-80% compared to the commercial state-of-art load-sensing systems. In addition, the corner power requirements of the electrical machines can be downsized by 85% compared to the EHA approach. Various tradeoff studies have also been conducted, including sensitivities to individual components performances, controllers, accumulator sizes, and variations of the system architecture etc. A control strategy has been developed to maintain or exceed the motion control precision compared to current systems. The motion control strategy consists of a nominal controller, based on a passivity-based backstepping design, and a transition controller, based on least-norn feedforward design. The nominal controller is used in between common pressure rail switches whereas the transition controller compensates for any disturbance that common pressure rail switchings inflict on the system. The control strategy has been experimentally validated on both a medium pressure (200bar) hardware-in-the-loop (HIL) testbed and a high pressure (300+bar) HIL testbed. An efficient and power-dense integrated electric-hydraulic machine consisting of an axial flux electric machine and a radial hydrostatic piston hydraulic machine has been designed, constructed and tested. The machine has an active material power density of 6.1kW/kg, a rated speed of 12500 RPM, and a design efficiency of 85%. This is among the highest power density electric machines using conventional materials. While the integrated machine was designed for modulating the hydraulic power within the HHEA, it can also be used in other applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Design and Performance Evaluation of a Resistive Control Using a Hydraulic PTO System for the TALOS Wave Energy Converter

This study is focused on developing a numerical model to evaluate the performance of a hydraulic PTO system for the TALOS Wave Energy Converter. The WEC device is described and the architecture of the hydraulic PTO system is presented with detail. The WEC is modeled using WEC-Sim, and the PTO is modeled using the Simscape Fluids library from Simulink. The hydraulic PTO is based on a constant pressure configuration that is suitable for WEC passive control. The hydraulic system is composed by a set of rectifying valves and two hydraulic accumulators that reduce the stiffness of the system and also serve as energy storage devices. One of the advantages of this hydraulic PTO architecture is the possibility of controlling the electric generator to operate around the optimal efficiency operating point. The main components of the hydraulic PTO are off-the-shelf devices that are commercially available, which will facility a future deployment of the designed system. The design variables used for this study are the accumulator size, the maximum pressure in the accumulators, the hydraulic motor maximum displacement, and the shaft speed in the electric generator. The performance of the system is evaluated individually, using sinusoidal inputs that replicates regular wave conditions. In addition to this, the numerical model of the PTO is coupled to a WEC-Sim simulation of the TALOS Wave Energy Converter with six PTOs to generate a wave-to-wire model. The main objective of this work is to present a comprehensive design methodology that could serve as a guideline for future research efforts focused on implementing control algorithms on multi degree of freedom WECs.

hydraulic systems↗
DOE OSTI2023

Leaves as bottlenecks: The contribution of tree leaves to hydraulic resistance within the soil–plant–atmosphere continuum

Within vascular plants, the partitioning of hydraulic resistance along the soil-to-leaf continuum affects transpiration and its response to environmental conditions. In trees, the fractional contribution of leaf hydraulic resistance (R leaf ) to total soil-to-leaf hydraulic resistance (R total ), or fR leaf (= R leaf /R total ), is thought to be large, but this has not been tested comprehensively. Here, we compiled a multi-biome dataset of fR leaf using new and previously published measurements of pressure differences within trees in situ. Across 80 samples, fR leaf averaged 0.51 (95% confidence interval [CI] = 0.46-0.57) and it declined with tree height. We also used the allometric relationship between field-based measurements of soil-to-leaf hydraulic conductance and laboratory-based measurements of leaf hydraulic conductance to compute the average fR leaf for 19 tree samples, which was 0.40 (95% CI = 0.29-0.56). The in-situ technique produces a more accurate descriptor of fR leaf because it accounts for dynamic leaf hydraulic conductance. Both approaches demonstrate the outsized role of leaves in controlling tree hydrodynamics. A larger fR leaf may play a role in protecting stems from loss of hydraulic conductance. Thus, the decline in fR leaf with tree height would contribute to greater drought vulnerability in taller trees and potentially to their observed disproportionate drought mortality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of an Integrated Electro-Hydraulic Machine to Electrify Off-highway Vehicles

Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper proposes and develops an axial flux machine integrated with a hydraulic pump to realize a single modular, electro-hydraulic machine to electrify off-highway vehicle implements. This integrated machine eliminates redundant bearings, couplings, and shaft seals, re-uses surfaces, and enables direct cooling of the electric machine with the hydraulic fluid, to significantly increase power density. Three popular axial flux machine variants are first compared using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to characterize the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and enamelled copper wire). Furthermore, a prototype axial flux machine has been experimentally characterized and integrated with a hydraulic pump to demonstrate the integrated electro-hydraulic machine concept.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

The Durability of Piston Seals in Hydraulic Power Take-off Systems in Wave Energy Converters

Hydraulic cylinder seals are a critical component of hydraulic power take-off (PTO) systems in wave energy converters (WECs). Primary hydraulic piston seal wear is a major concern for the longevity of hydraulic PTOs, especially in the context of the effort and expense associated with seal replacement. Piston seals, made from polymeric elastomers, are used to contain and isolate high pressure fluids within PTO systems. A specific challenge for WEC designers is knowing, with confidence, the relative expected lifetimes of commercially available seals and seal materials for the unique long travel and continuous use case of WEC hydraulic systems. This information is critical to accurately determine operating expense (OPEX) and levelized cost of electricity (LCOE). If failures of seals occur earlier than their designed lifetime, the estimated operations, and maintenance (O&M) and LCOE costs may double based on estimation. Unfortunately, information from seal manufacturers on longevity in these applications is not generally available and quantitative performance comparison between different manufacturers is not available, creating significant uncertainty on use of hydraulic PTO system in wave power generation. In this project, PNNL, with advice from different WEC device and seal manufacturers, has created a framework to address the industry need for available, dependable and comparative data for seals and seals materials for WEC hydraulic applications including piston seals, glide rings, and shaft seals. Commonly used and candidate seal materials were identified and available information on the materials such as mechanical and fatigue performance, chemical (fluid) compatibility, and cost has been compiled. Hardware and strategy for bench scale measurement of key materials and seal performance and pathway for publicly available library of hydraulic seal materials, properties, suppliers, and options have also been identified for future implementation. The results of this project were presented at WPTO Seedling Symposium 2023 and OCEANS 2023. The results of the literature review including identified polymer seals and ideal operating conditions were summaried and compiled to a database WEC-SealsDB hosted locally at PNNL.

16 TIDAL AND WAVE POWER↗