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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

ROP model for PDC bits in Geothermal drilling

Geothermal energy is a renewable source of energy, where heat extraction is preferentially balanced with the reservoir's natural heat recharge rate. The objective of this paper is to present and validate a novel rate of penetration (ROP) model for drilling hard and abrasive formations including granite formations for polycrystalline diamond compact (PDC) bits. The ROP model was developed based on a derived relationship of a threshold weight on cutter (WOC) and its corresponding depth of cut (DOC) for a single cutter. Laboratory data was used to scale the derived single cutter relationship to a full-hole ROP model for PDC bits. The ROP model includes a non-linear correlation for Phase I (inefficient drilling due to low WOB values) and a linear Phase II (efficient drilling) ROP response to WOB. The ROP model was verified using measured drilling parameter data from Utah FORGE well# 58-32 and data from Chocolate Mountains well # 17-8 in Southern California. When compared to oil and gas well drilling, geothermal drilling in granitic formations can be more difficult and complicated due to rock hardness and high temperatures. PDC bits can increase ROP and optimize drilling for these types of hard formations. This paper provides novel insight into the ROP response of PDC bits to drilling operational parameters.

15 GEOTHERMAL ENERGY↗
NASA NTRS2013

Wireline Deep Drill for the Exploration of Icy Bodies

One of the most pressing current questions in space science is whether life has ever arisen anywhere else in the universe. Water is a critical prerequisite for all life-as-we-know-it, thus the possible exploration targets for extraterrestrial life are bodies that have or had copious liquid: Mars, Europa, and Enceladus. Due to the oxidizing nature of Mars' surface, as well as subsurface liquid water reservoirs present on Europa and Enceladus, the search for evidence of existing life must likely focus on subsurface locations, at depths sufficient to support liquid water or retain biologic signatures. To address these questions, an Auto-Gopher sampler has been developed that is a wireline type drill. This drill is suspended on a tether and its motors and mechanisms are built into a tube that ends with a coring bit. The tether provides the mechanical connection to a rover/lander on a surface as well as power and data communication. Upon penetrating to a target depth, the drill is retracted from the borehole, the core is deposited into a sample transfer system, and the drill is lowered back into the hole. Wireline operation sidesteps one of the major drawbacks of traditional continuous drill string systems by obviating the need for multiple drill sections, which add significantly to the mass and the complexity of the system (i.e. penetration rate was 40 cm per hour). Drilling to 2 meter depth and recovering of cores every 10 cm took a total time of 15 hours (a single step of drilling 10 cm and retrieving the core was 45 minutes). Total energy to reach the 2 m depth was 500 Whr. The Weight on Bit was limited to less than 70 Newton. The core recovery was 100%.

sampling↗
NASA NTRS2008

Estimating Hardness from the USDC Tool-Bit Temperature Rise

A method of real-time quantification of the hardness of a rock or similar material involves measurement of the temperature, as a function of time, of the tool bit of an ultrasonic/sonic drill corer (USDC) that is being used to drill into the material. The method is based on the idea that, other things being about equal, the rate of rise of temperature and the maximum temperature reached during drilling increase with the hardness of the drilled material. In this method, the temperature is measured by means of a thermocouple embedded in the USDC tool bit near the drilling tip. The hardness of the drilled material can then be determined through correlation of the temperature-rise-versus-time data with time-dependent temperature rises determined in finite-element simulations of, and/or experiments on, drilling at various known rates of advance or known power levels through materials of known hardness. The figure presents an example of empirical temperature-versus-time data for a particular 3.6-mm USDC bit, driven at an average power somewhat below 40 W, drilling through materials of various hardness levels. The temperature readings from within a USDC tool bit can also be used for purposes other than estimating the hardness of the drilled material. For example, they can be especially useful as feedback to control the driving power to prevent thermal damage to the drilled material, the drill bit, or both. In the case of drilling through ice, the temperature readings could be used as a guide to maintaining sufficient drive power to prevent jamming of the drill by preventing refreezing of melted ice in contact with the drill.

Bar-Cohen, Yoseph↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

15 GEOTHERMAL ENERGY↗
NASA NTRS2012

Changes in Black Carbon Deposition to Antarctica from Two Ice Core Records, A.D. 1850-2000

Continuous flow analysis was based on a steady sample flow and in-line detection of BC and other chemical substances as described in McConnell et al. (2007). In the cold room, previously cut one meter ice core sticks of 3x3cm, are melted continuously on a heated melter head specifically designed to eliminate contamination from the atmosphere or by the external parts of the ice. The melted ice from the most inner part of the ice stick is continuously pumped by a peristaltic pump and carried to a clean lab by Teflon lines. The recorded signal is continuous, integrating a sample volume of about 0.05 mL, for which the temporal resolution depends on the speed of melting, ice density and snow accumulation rate at the ice core drilling site. For annual accumulation derived from the WAIS and Law Dome ice cores, we assumed ~3.1 cm water equivalent uncertainty in each year's accumulation from short scale spatial variability (glaciological noise) which was determined from several measurements of annual accumulation in multiple parallel ice cores notably from the WAIS Divide ice core site (Banta et al., 2008) and from South Pole site (McConnell et al., 1997; McConnell et al., 2000). Refractory black carbon (rBC) concentrations were determined using the same method as in (Bisiaux et al., 2011) and adapted to continuous flow measurements as described by (McConnell et al., 2007). The technique uses a single particle intracavity laser induced incandescence photometer (SP2, Droplet Measurement Technologies, Boulder, Colorado) coupled to an ultrasonic nebulizer/desolvation (CETAC UT5000) Flow Injection Analysis (FIA). All analyses, sample preparation etc, were performed in a class 100 cleanroom using anti contamination "clean techniques". The samples were not acidified.

Bisiaux, Marion M.↗
NASA NTRS2011

Percussive Augmenter of Rotary Drills (PARoD)

A percussive augmenter is being developed and it is designed to add ultrasonic hammering to a conventional commercial rotary drill through an adapter using a piezoelectric actuator. The combination of ultrasonic hammering and rotation creates a drill that requires low power and low axial load while providing faster penetration than conventional rotary-only drills. These characteristics make percussive augmentation of rotary drills ideal for not only planetary exploration but commercial applications as well. The purpose of this internship was to produce, test, and optimize an augmenter that drives a 2 inch diameter bit. This task was part of the percussive augmenter's phase II of an ongoing contract and it involved increasing the capability of the .25 inch version of the augmenter prototype that was developed in phase I. Due to fabrication delays of the augmenter, an extensive part of the test effort was conducted using previous rotary and hammer drill prototypes. These tests focused on drilling deep over long periods of time to provide for effective stress test of the drill. Modifications were made to the drill, its components, and the testing procedure to achieve desired borehole depths. These results were used to start initial testing on the 2 inch augmenter and provide preliminary data on the augmenter's ability to significantly improve penetration rate over conventional rotary-only drills.

rotraty hammer↗
DOE OSTI2024

2024 Geothermal Rising Conference Poster

As part of a U.S. DOE Geothermal Technologies Office funding opportunity, Geysers Power Company, LLC, an indirect subsidiary of Calpine Corporation, partnered with Sandia National Labs, EGI at the University of Utah, and Texas A&M University to demonstrate increased drilling performance at The Geysers Geothermal Field. The performance target in the drilling demonstrations is at least a 25% improvement in rates of penetration with increased time on bottom for each bit. The planned drilling demonstrations are being conducted as part of an existing drilling campaign intended to enhance reservoir utilization. A major goal of the project is to assess the effectiveness of implementing mechanical specific energy (MSE) and drilling dysfunction diagnosis and remediation in these challenging environments, as well as alternate bit technologies. The first demonstration well has been completed, with a total of 15 PDC bit runs in the 17.5”, 12.25” and 8.5” sections. Initial analysis shows ROP gains in all three hole sections, especially in the 17.5” and 12.25” sections, compared with conventional roller cone bit runs in the demonstration well and offset wells. However in the 8.5” hole, wear and damage to the PDC bits resulted in relatively short bit runs. Analysis is underway to take advantage of the positive results and remediate the challenges.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Experimental Investigation of Drillstring Torsional Vibration Effect on Rate of Penetration with PDC Bits in Hard Rock

Polycrystalline diamond compact (PDC) bits have shown tremendous performance compared to roller cone bits and have gained attraction in deep geothermal drilling applications. While PDC bits can reach a higher rate of penetration compared to other bits, they are more prone to drilling vibrations. The scope of this paper is to quantify the effects of drillstring torsional vibrations on PDC bit performance using a laboratory testing facility. Here, the tests were performed with three drillstring configurations to evaluate the effect of drillstring vibrations. The results showed that rigid testing overestimates the bit rate of penetration by 35% due to torsional vibrations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 5: Utah FORGE Well 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 4: Utah FORGE Well 78B-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 3: Utah FORGE Well 56-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets

This repository contains simulated outputs from superwell – a hydro-economic tool for long-term assessment of groundwater cost and supply – providing globally gridded groundwater extractable volumes and associated unit costs ($/km³) for accessible groundwater production, based on a variety of user-defined depletion and ponded depth scenarios. Key model documentation: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 Find the source code of the superwell model on GitHub: https://github.com/JGCRI/superwell Repository Overview Main output: superwell_outputs.7z contains 6 files (4.5 GB) named as superwell_py_deep_all_0.*PD_0.*DL.csv. These files present superwell outputs of global groundwater extraction volumes and cost estimates on a 0.5° scale for six scenarios with different Ponded Depth (PD; 0.3 and 0.6 m) and Depletion Limit (DL; 5%, 25%, and 40% of available volume) targets over the entire pumping lifetime of a grid cell superwell_py_deep_all_0.3PD_0.25DL_sample_100.csv contains superwell outputs for 100 data points sampled to match the global inputs' distribution superwell_py_deep_all_0.3PD_0.25DL_Grid_72548.csv contains superwell output for a single grid cell concept_v5.png provides an overview of the superwell workflow Outputs Description year_number: year of pumping depletion_limit: set depletion limit (DL) as a volume fraction of total available groundwater Mappings: continent, country, gcam_basin_id, Basin_long_name, grid_id: geographic identifiers and basin information Inputs: grid_area (km²): area of the grid cell whyclass: hydrogeological classification of the aquifer permeability (m/day), porosity (%), total_thickness (m), depth_to_water (m): aquifer properties. The geo-processed input data has been published separately: https://doi.org/10.57931/2307831 Model outputs: orig_aqfr_sat_thickness (m), aqfr_sat_thickness (m): original and remaining/instantaneous saturated thickness of the aquifer hydraulic_conductivity (m/day), transmissivity (m²/day): hydraulic properties of the aquifer radius_of_influence (m), areal_extent (km²): well radius and area of influence from the center of the well number_of_wells (-): number of wells in a grid cell determined by a ratio of well area and grid area max_drawdown (m), drawdown (m), drawdown_interference (m): well and aquifer drawdown during extraction total_head (m): total lift for the groundwater (depth to water plus drawdown) total_well_length (m): total depth of wells drilled well_yield (m³/day): pumping rate or well yield power (kW), energy (kWh): power and energy required for pumping groundwater Volume Outputs: volume_produced_perwell (m³), cumulative_vol_produced_perwell (m³): production volume metrics per well volume_produced_allwells (m³), cumulative_vol_produced_allwells (m³): aggregate extraction volumes for all wells in a grid cell available_volume (m³): available groundwater in storage for the grid cell as determined by aquifer properties depleted_vol_fraction: fraction of total volume pumped over available volumes in a grid cell (same as depletion limit) Cost Outputs: well_installation_cost ($): well installation cost based on the hydrogeological complexity of the aquifer annual_capital_cost, maintenance_cost, nonenergy_cost ($): nonenergy costs energy_cost_rate ($/kWh): electricity rate energy_cost ($): energy cost of pumping groundwater total_cost_perwell ($), total_cost_allwells ($): total annual energy and non-energy cost for each and all wells in a grid cell a unit_cost ($/m³), unit_cost_per_km3 ($/km³), unit_cost_per_acreft ($/acre-ft): total cost of pumping a unit of groundwater, indicated for different spatial units Key Resources Model documentation: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. Input data: Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data repository. https://doi.org/10.57931/2307831 superwell source code: https://github.com/JGCRI/superwell Cite as Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024). Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets. MSD-LIVE Data repository. https://doi.org/10.57931/2307832 Contact Reach out to Hassan Niazi or Stephen Ferencz or open an issue in the superwell repository for questions or suggestions.

Earth Systems↗
NASA NTRS1985

On the progenitors of white dwarfs

Direct observational evidence is presented which indicates that the immediate progenitors of white dwarfs are the central stars of planetary nebulae (approximately 70%), other post-AGB objects (approximately 30%), and post-HB objects not massive enough to climb the AGB (approximately 0.3%). The combined birth rate for these objects is in satisfactory agreement with the death rate of main-sequence stars and the birth rate of white dwarfs.

Drilling, J. S.↗
NASA NTRS1985

On the progenitors of white dwarfs

In view of the direct observational evidence presented here, the immediate progenitors of white dwarfs are the central stars of planetary nebulae in as many as 80 percent of all cases; secondary progenitors being other postasymptotic giant branch objects, to the extent of nearly 20 percent, and posthorizontal branch objects insufficiently massive to climb the asymptotic giant branch, with about 0.7 percent. The combined birth rate of these objects, at 2-3 x 10 to the -12th/cu pc per year, is in satisfactory agreement with the death rate of main sequence stars and the birth rate of white dwarfs.

Drilling, J. S.↗
NASA NTRS2014

Single Piezo-Actuator Rotary-Hammering (SPaRH) Drill

A Single Piezo-Actuator Rotary-Hammering (SPaRH) Drill includes a horn actuator having high power piezoelectric materials and a flexure pre-stress to increase the actuators effectiveness. The drill is a low mass, low power, compact coring drill measuring 20-cm high by 7-cm diameter and having a total weight of 2 kg including drive electronics. Using an average power of 50-Watts, the drill basalt is expected to cut basalt at a rate of 0.2 cm/min down to depth of 10-cm and create cuttings and an intact core. The drill is expected to operate under different environments including Martian ambient (6 Torr and down to -50 degree C), and liquid nitrogen temperatures (77 K) and low pressure (<<1 Torr) to simulate lunar polar and Europa conditions. Materials expected to be sampled include Kaolinite, Saddleback Basalt, Limestone, Volcanic Breccia, Siltstone, ice, permafrost and layered rocks with different hardness.

Sherrit, Stewart↗