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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effect of free surfaces on localized plastic deformation in single-crystal nickel containing helium bubbles and radiation-induced self-interstitial atom clusters

This paper presents molecular dynamics (MD) simulations of plastic deformation in single-crystal Ni models containing internal distributions of helium (He) bubbles and self-interstitial atom (SIA) clusters corresponding to experimental characterization of ex-service Inconel X-750 components. Here, we simulate single crystals with and without free surfaces at room temperature and 588K, the latter temperature corresponding to the Inconel X-750 service conditions. Both models are deformed under multiple slip to a maximum deviatoric strain of ~10 %. A dislocation slip band forms in the model without free surfaces, but not in the model with free surfaces. However, the latter exhibits nano-twin nucleation from surfaces. Helium (He) bubbles that overlap with a slip band or a nano-twinned region deform more severely than other bubbles in the model. Nevertheless, the bubbles do not link up and do not form configurations that resemble incipient cracks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimizing Rate of Penetration and Tripping Decision-Making using Real-Time Bit Wear Monitoring While Drilling Geothermal Wells

Understanding bit wear while drilling is critical to minimizing non-productive time (NPT) and optimizing rate of penetration (ROP). Lengthening drilling runs with damaged bits does not only lower the ROP, but also elevates the risk of inducing severe bit damage, which could potentially lead to time-consuming fishing operations. When drillers believe the bit has worn off substantially, the bit is tripped out to be replaced. On geothermal wells, tripping can take up to 20% of the overall well construction time, and this is generally acknowledged as an opportunity for improvement. Ideally, a bit run should be terminated before the bit is damaged beyond repair. At the same time, premature bit pulls are to be avoided as well. This study aims to leverage bit and tooth wear metrics that can be obtained in real time to characterize bit condition in order to optimize ROP and determine the optimal time to pull the bit. Two metrics were explored in this study: a bit wear metric that incorporated depth-of-cut, and a tooth wear metric developed by Bourgoyne & Young characterizing the state of bit teeth dull. Both metrics were computed using recorded data from 12¼ inches roller cone insert bit runs in five geothermal wells targeting a granodiorite formation in the western United States. Together with the actual dull grades, determined after the bits were pulled to surface, the metric trends were interpreted to characterize the downhole bit condition and identify the point at which the bit should have optimally been tripped out. The insights from studying the actual dull grades and how they relate to the two metrics were used to establish a reliable bit pull criterion. The bit wear metric trend correctly showed a noticeable departure from baseline for bits experiencing major dulling behavior. Additionally, the tooth wear model predicted the cutter dull within two dull grades for most runs, with better performance in predicting the inner teeth dull. Moreover, the combination of the bit wear and tooth wear metrics was effective in revealing the cause of the bit performance impairment. Proactive tracking of these two metrics in real-time can facilitate geothermal drilling ROP optimization and better-informed tripping decision-making, thereby avoiding wasted time and cost.

Ashari, Rahmat↗
DOE OSTI2024

Machine learning potential assisted exploration of complex defect potential energy surfaces

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A lithium-sulfur battery with a solution-mediated pathway operating under lean electrolyte conditions

Lithium-sulfur (Li-S) battery is one of the most promising candidates for the next generation energy storage systems. However, several barriers, including polysulfide shuttle effect, the slow solid-solid surface reaction pathway in the lower discharge plateau, and corrosion of Li anode still limit its practical applications, especially under the lean electrolyte condition required for high energy density applications. Here, we propose a solution-mediated sulfur reduction pathway to improve the capacity and reversibility of the sulfur cathode and suppress dendrite growth on the Li metal anode simultaneously. With this method, a high coulombic efficiency (99%) and stable cycle life over 100 cycles were achieved under application-relevant conditions (S loading: 6.2 mg cm-2; electrolyte to sulfur ratio: 3 mLE gs-1; sulfur weigh ratio: 72 wt%). This result is enabled by a specially designed Li2S4-rich electrolyte, in which Li2S is formed through a chemical disproportionation reaction instead of electrochemical routes. A diglyme solvent was used to obtain electrolytes with the optimum range of Li2S4 concentration. Operando X-ray absorption spectroscopy confirms the solution pathway in a practical Li-S cell. This solution pathway not only introduces a new electrolyte regime for practical Li-S batteries, but also provides a new perspective for bypassing the inefficient surface pathway for other electrochemical processes.

Wang, Hui↗
DOE OSTI2023

The effect of current and lambda on white-etch-crack failures

White etching cracks (WECs) have been associated with premature failure of wind turbine roller bearings. Various drivers for the generation of WECs have been identified such as loading conditions, slip, steel quality, lubrication, hydrogen embrittlement, corrosion fatigue cracking, and stray electrical currents passing through the surface. Here, in this work, a benchtop test rig utilizing a three-ring-on-roller test configuration was used to investigate the effect of electrical current and operation in different lubricating regimes, defined by lambda (λ), on high-quality bearing steel samples tested in a commercially available power transmission EP gear lubricant. It was observed that there is an inverse correlation between the magnitude of electric current applied to the ring/ roller system and time-to-failure. Higher current magnitudes lead to shorter time-to-failure than lower current magnitudes, with macropitting as the main failure mode. Sub-surface investigation revealed the presence of WECs in all cases. For the same current magnitude, tests conducted in boundary and mixed lubrication regimes showed that time-to-failure increased as lambda increased, and the tests resulted in WEC related macropits, whereas tests conducted in near-hydrodynamic regime resulted in surface damage with no macropit. It was also noted that a shift toward near-hydrodynamic lubrication resulted in a distinct surface distress on the roller surface. Furthermore, there seems to be a transition in the mixed regime during which the surface distress occurred. The damage on the surface of the test samples resembled non-spatially, periodic, groove-like corrugations and, in some cases, crater-like depressions. Sub-surface imaging, performed by sequential sectioning, revealed the presence of WECs in all cases, and broad, branching cracks that were more prevalent under the more severe boundary conditions.

17 WIND ENERGY↗
DOE OSTI2026

Stimulated geologic hydrogen: from mechanistic control to engineered rock transformation

Geologic hydrogen (GeoH 2 ) generated from subsurface iron-rich rock–water reaction (i.e., serpentinization) is emerging as a promising candidate for the next primary energy source. Yet, accelerating GeoH 2 production from geological to human timescales via enhanced serpentinization remains a formidable scientific and technical challenge. Here, in this Review, we decipher the mechanistic control and explore strategies for accelerating in situ, engineered iron-rich rock transformation into carbon-free GeoH 2 by orders of magnitude. Serpentinization rate is hindered by low porosity and permeability of source rocks, suboptimal temperatures, unfavorable water chemistry, inefficient Fe 2+ -to-Fe 3+ conversion, thermodynamic constraints, and low reactive surface area. While closed-system experiments provide valuable mechanistic insights, open-system conditions with fluid circulation are more crucial for economically viable GeoH 2 production. We assess stimulation techniques from enhanced hydrocarbon and geothermal recovery as tools to be adopted or adapted for increasing reactive surface areas for stimulated GeoH2 production. We estimate that 7.40 × 10 5 to 1.73 × 10 6 million metric tons (Mt) of hydrogen could be engineered over 20 to 50 years from about 10% iron-rich rocks within 10 km depth of continental crust. Enabling GeoH 2 as a viable energy source requires not only advancing scientific frontiers but also forming a global GeoH 2 research network and innovation ecosystem to address the critical scientific, technical, societal, economic, and policy challenges.

08 HYDROGEN↗
DOE OSTI2026

Decoupling plasma, catalyst, and gaseous mechanisms for non-oxidative methane conversion

Direct non-oxidative methane (CH 4 ) conversion to value-added hydrogen (H 2 ) and C 2 products remains hindered by fundamental catalytic scaling constraints and rapid surface deactivation at elevated temperatures. Plasma-enabled catalysis offers a promising route to overcome the thermodynamic and kinetic barriers of direct non-oxidative methane upgrading at mild conditions, yet control over C–C product selectivity and catalyst stability remains elusive. Here, we establish a unified mechanistic framework including Langmuir–Hinshelwood (L–H) and Langmuir–Rideal (L–R) mechanisms that disentangles the roles of plasma excitation (including vibrationally activated species and radicals), surface temperature (T sur ), and catalyst binding energy in steering CH 4 conversion to H 2 and C 2 hydrocarbons. Through a combination of density functional theory (DFT) informed microkinetic modeling, in situ and ex situ surface characterization, and product quantification under dielectric barrier discharge conditions, we show that vibrationally excited CH 4 lowers activation barriers selectively for dissociative chemisorption, enabling surface activation across a wide range of transition metal catalysts at low thermal energy input. We find that once CH 4 is dissociatively chemisorbed, the branching between C 2 H 2 , C 2 H 4 , and C 2 H 6 is governed by surface properties (carbon binding energy, T sur , etc), regardless of plasma excitation. The DFT informed microkinetic model decouples the effects of molecular activation from surface properties and indentifies operating windows that maximize target yields while suppressing carbon accumulation and subsequent catalytic inactivation. Experiments on polycrystalline Cu/Al 2 O 3 , Ni/Al 2 O 3 , and Pt/Al 2 O 3 validate these predictions, revealing catalyst-dependent branching toward ethane or ethylene and distinct deactivation profiles. We unify these trends into a generalized three-dimensional plasma-thermal-catalytic design space, from which reduced descriptors such as T vib /T sur in the limit of vibrationally excited L–H pathways emerge as predictive metrics. These results enable rational tuning of methane conversion pathways and unlock selective C 2 formation using earth-abundant metals under mild plasma conditions.

catalyst inactivation↗
DOE OSTI2022

Investigation of Ni catalyst activation during plasma-assisted methane oxidation

Atmospheric pressure plasma has shown promise in improving thermally activated catalytic reactions through a process termed plasma-catalysis synergy. In this work, we investigated atmospheric pressure plasma jet (APPJ)-assisted CH 4 oxidation over a Ni/SiO 2 .Al 2 O 3 catalyst. Downstream gas-phase products from CH 4 conversion were quantified by Fourier transform infrared spectroscopy. The catalyst near-surface region was characterized by in-situ diffuse reflectance infrared Fourier transform spectroscopy. The catalyst was observed to be activated at elevated temperature (500 °C) if it was exposed to the APPJ operated at large plasma power. 'Catalyst activation' signifies that the purely thermal conversion of CH 4 using catalysts which had been pre-exposed to plasma became more intense and produced consistently CO product, even if the plasma was extinguished. Without the application of the APPJ to the Ni catalyst surface this was not observed at 500 °C. The study of different exposure conditions of the activated catalyst indicates that the reduction of the catalyst by the APPJ is likely the cause of the catalyst activation. We also observed a systematic shift of the vibrational frequency of adsorbed CO on Ni catalyst when plasma operating conditions and catalyst temperatures were varied and discussed possible explanations for the observed changes. Furthermore, this work provides insights into the plasma-catalyst interaction, especially catalyst modification in the plasma catalysis process, and potentially demonstrates the possibility of utilizing the surface CO as a local probe to understand the plasma-catalyst interaction and shed light on the complexity of plasma catalysis.

methane oxidation↗
DOE OSTI2026

Advanced Multiphysics Code Coupling for Cladding Surface Thermocouples During Two-Phase Heat Transfer from Nuclear Fuel

Transient testing of nuclear fuel involves the evaluation of fuel performance under off-normal and accident conditions and is essential for proving design performance. Instrumentation included in such experiments commonly includes thermocouples attached to the outer surface of the cladding to provide temperature measurements throughout the transient. However, the presence of thermocouples on the cladding surface can alter the local heat transfer characteristics with the surrounding coolant. These localized effects can influence the temperature of the nearby cladding surface and introduce uncertainties in interpreting the thermocouple data. Understanding the impact of thermocouples attached to the outer surface of the cladding is crucial for accurate data interpretation as well as its effect on the thermomechanical behavior of the cladding. This paper presents a novel methodology for simulating the impact of outer cladding thermocouples during transient testing of nuclear fuels. The simulation framework leverages the thermal-hydraulic capabilities of RELAP5-3D coupled to the BISON fuel performance code through the RELAPCouplingApp interface. The methodology is compared against Accident Tolerant Fuel Reactivity Initiated Accident-1-E experiment performed at Idaho National Laboratory. The results reveal approximately 100°C difference between thermocouple-altered temperature and virgin cladding surface. The model overpredicts surface rewet time due to conservative correlations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Assessing the role of surface carbon on the surface stability and reactivity of β-Mo 2 C catalysts

Transition-metal carbides, e.g., Mo 2 C, promote industrially important catalytic reactions with activities comparable to precious-metal catalysts. Yet, the nature of the catalytically active sites remains unclear. Herein, density functional theory calculations are applied to systematically assess the stability of low-Miller index facets of β-Mo 2 C as a function of carbon chemical potential (uC) and surface-carbon coverage (nC). When reconstruction of surface carbon is considered to characterize β-Mo 2 C under varying environments, the (111) and (101) surfaces are predicted to be most stable, dominating the Wulff particle surface area across a broad range of uC. The surface-carbon coverage is predicted to steadily increase for all low-index facets under more carburizing reaction conditions, leading to distinct effects on the adsorption energetics for atomic hydrogen and oxygen. Whereas H binding is only slightly affected by surface carbon, O is systematically destabilized. The findings reported indicate that consideration of only bulk-terminated surfaces, and the common focus of theoretical studies on the bulk-truncated, close-packed (100) surface, is likely insufficient to capture the reactivity of the working ..beta..-Mo 2 C catalyst. Similar to metal oxides, β-Mo2C catalysts are predicted to be dynamic materials with surface compositions and reactivities that evolve in response to the environment.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
DOE OSTI2025

Kelvin probe force microscopy under ambient conditions

Kelvin probe force microscopy (KPFM) is a technique derived from atomic force microscopy that provides maps of surface potential or work function differences across material systems, with nanometre-scale resolution. KPFM is a useful tool for investigating electrical phenomena such as dipole orientation, interfacial charge transfer, charge accumulation, band bending and doping levels. This Primer aims to provide an overview of typical ambient-condition KPFM measurements, covering their underlying principles, experimental implementations and wide-ranging applications. Key KPFM variants, including amplitude and frequency modulation, heterodyne detection schemes and innovative open loop and pulsed force techniques, are discussed, with practical guidance on optimizing signal acquisition and reducing errors. Specialized approaches, such as time-resolved KPFM and multimodal KPFM, are discussed for their ability to capture dynamic charge processes and chemical information, respectively. Here, we highlight recent advances in KPFM applications, spanning metal alloys, soft matter, ferroelectrics, photovoltaics and 2D materials, showcasing its versatility across research domains. By addressing current limitations and identifying future opportunities, this Primer underscores the transformative potential of KPFM in advancing the understanding of nanoscale electrical phenomena.

Zahmatkeshsaredorahi, Amirhossein [Lehigh Univ., B↗
DOE OSTI2020

Controlling surface adatom kinetics for improved structural and optical properties of high indium content aluminum indium nitride

A non-traditional, for AlInN, method of controlling adatom kinetics and a low temperature growth condition were employed to improve the quality of high indium content aluminum indium nitride films. Metal-rich surfaces were used to enhance adatom mobility and compensate for the low growth temperature (Tsub ≤ 400 °C) effect of reducing surface diffusion lengths. The metal-rich approach resulted in 12 times lower x-ray diffraction full-width at half-maximum rocking curve figures of merit when compared to literature. In addition to promising photoluminescence emission, these results indicate improved structural quality over other reported approaches. AlInN films with ∼70% indium content were characterized via x-ray diffraction, atomic force microscopy, and photoluminescence spectroscopy with each technique indicating an optimal growth temperature of 350 °C. Al0.3In0.7N grown above 400 °C exhibited phase separation and a reduction in quality, while samples grown colder were predominantly single-phase and displayed improved photoluminescence at ∼1.45 eV. The photoluminescence spectra suggest emission from quantum wire-like structures with dimensions ranging from 15 to 18 nm. These low-temperature, metal-rich findings for high indium content AlInN are promising for future long-wavelength III-nitride optical devices.

Engel, Zachary (ORCID:0000000205021981)↗
DOE OSTI2020

Tailored Graphene Micropatterns by Wafer-Scale Direct Transfer for Flexible Chemical Sensor Platform

2D materials, such as graphene, exhibit great potential as functional materials for numerous novel applications due to their excellent properties. The grafting of conventional micropatterning techniques on new types of electronic devices is required to fully utilize the unique nature of graphene. However, the conventional lithography and polymer-supported transfer methods often induce the contamination and damage of the graphene surface due to polymer residues and harsh wet-transfer conditions. In this work, a novel strategy to obtain micropatterned graphene on polymer substrates using a direct curing process is demonstrated. Employing this method, entirely flexible, transparent, well-defined self-activated graphene sensor arrays, capable of gas discrimination without external heating, are fabricated on 4 in. wafer-scale substrates. Finite element method simulations show the potential of this patterning technique to maximize the performance of the sensor devices when the active channels of the 2D material are suspended and nanoscaled. This study contributes considerably to the development of flexible functional electronic devices based on 2D materials.

2D materials, Graphene, Microscale patterning, Che↗
DOE OSTI2020

Catalytic Oxidation of CO on a Curved Pt(111) Surface: Simultaneous Ignition at All Facets through a Transient CO-O Complex

The light-off of the CO oxidation is simultaneous on all Pt crystal surfaces vicinal to the (111) plane, regardless of the reaction conditions, and in contrast with the structural dependence of Pd. In this work, using ambient-pressure XPS we find that, immediately prior to ignition, atomic oxygen incorporates to the subsurface plane, leading to buckling of the topmost CO-Pt layer, and effectively equaling the CO desorption temperature at terraces and steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A 30-yr high-resolution weather research and forecasting model downscaling data over California and Nevada

This dataset presents a 30-year high resolution meteorological dataset obtained using the WRF model (Advanced version Research WRF version 4.4). We used WRF and European Centre for Medium-Range Weather Forecasts Reanalysis v5 as initial and boundary conditions to generate gridded meteorological variables. A large number of surface weather stations was used for model validation. A multi-physics analysis was first developed to identify a good physics suite extended from 6 November 00 UTC to 10 November 23 UTC, 2018, which included the Camp Fire in northern California. Based on the best physics suite, the downscaling dataset extends from 1 December to 28 February, 1990–2021 and the horizontal domain has 1.5 km grid spacing covering the entire states of California and Nevada in the United States. Comparisons between hourly surface observations and WRF simulations of air temperature, relative humidity and wind speeds show mean absolute errors on the order of (1.6-2.0 C), (10 %) and 1.2–1.5 m s -1 , respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High speed manufacturing of aluminum alloy 7075 tubing by Shear Assisted Processing and Extrusion (ShAPE)

Shear assisted processing and extrusion (ShAPE) was used to extrude aluminum alloy 7075 tubing at speeds up to 12.2 m/min. This is in comparison to 2.0 m/min which is generally the limit for conventionally extruded 7075. The increased speed is primarily attributed to more extensive shear deformation, compared to conventional extrusion, which results in a high density of low angle grain boundaries that facilitate continued deformation and delay the onset of surface tearing. Mechanical testing after heat treating to the T6 condition gave an ultimate tensile strength of 565.3 ± 4.6 MPa, yield strength of 495.7 ± 8.7 MPa, and elongation of 16.8 ± 0.8%. Strength values exceed the ASTM minimum standard and are on par with ASM typical values, while elongation was substantially improved compared to 7 and 11% for the ASTM and ASM values respectively. Finally, it was observed that low temperature extrusion at 341 °C and 40 rpm gave superior material properties in the T6 condition compared to high temperature extrusion at 441 °C and 120 rpm due to variances in nanoscale second phase size and distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Wave-powered water pump for upwelling in aquaculture: Numerical model and ocean test

Wave-powered upwelling can increase the productivity and survivability of several aquaculture species. This enhancement is due to transporting cold, nutrient-rich ocean water, typically found lower in the water column, to the surface. Macroalgaes, like kelp, exhibit increased growth from these altered conditions. The University of New Hampshire’s (UNH) wave-powered water pump (wave pump) is a point absorber wave energy converter (WEC) that uses ocean waves to create relative motion between a spar buoy and a concentric float which drives an internal pump. A numerical model of the wave pump was developed using WEC-Sim to predict device performance in the ocean. Wave pump performance was evaluated during a five day ocean test near Appledore Island in Maine in March 2023, where volumetric flow rate, relative distance between spar and float, and wave conditions were measured. These data were then used for numerical model validation. The ocean deployment recorded the device’s performance in a variety of sea states, with average significant wave heights up to 0.7 m. The ocean test data were compared to the WEC-Sim numerical model of the device with favorable results. Average values of device stroke period, stroke height, and flow rate agreed between the ocean test and model data to within approximately 16 to 22%. Furthermore, the validated numerical model provides a valuable tool for improving the design and developing a commercial-scale, wave-powered water pump for use in aquaculture.

16 TIDAL AND WAVE POWER↗