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At least 1,009 records · Page 56

Development of a Method for Shape Optimization for a Gas Turbine Fuel Injector Design Using Metal-Additive Manufacturing

Adjoint shape optimization has enabled physics-based optimal designs for aerodynamic surfaces. Additive manufacturing (AM) makes it possible to manufacture complex shapes. However, there has been a gap between optimal and manufacturable surfaces due to the inherent limitations of commercial computational fluid dynamics (CFD) codes to implement geometric constraints during adjoint computation. In such cases, the design sensitivities are exported and used to perform constrained shape modifications using parametric information stored in computer aided design (CAD) files to satisfy manufacturability constraints. However, modifying the design using adjoint methods in CFD solvers and performing constrained shape modification in CAD can lead to inconsistencies due to different shape parameterization schemes. This paper describes a method to enable the simultaneous optimization of the fluid domain and impose AM manufacturability constraints, resolving one of the key issues of geometry definition for isogeometric analysis. Similar to a grid convergence study, the proposed method verifies the consistencies between shape parameterization techniques present within commercial CAD and CFD software during mesh movement as a part of the adjoint shape optimization routine. By identifying the appropriate parameters essential to a shape optimization study, the error metric between the different parameterization techniques converges to demonstrate sufficient consistencies for justifiable exchange of data between CAD and CFD. For the identified shape optimization parameters, the error metric to measure the deviation between the two parameterization schemes lies within the AM laser-powder bed fusion (L-PBF) process tolerance. Additionally, comparison for subsequent objective function calculations between iterations of the optimization loop showed acceptable differences within 1% variation between the modified geometries obtained using the two parameterization schemes. This method provides justification for the use of multiphysics guided adjoint design sensitivities computed in CFD software to perform shape modifications in CAD to incorporate AM manufacturability constraints during the shape optimization loop such that optimal designs are also additively manufacturable.

33 ADVANCED PROPULSION SYSTEMS↗

TEAMER: Twin Ocean Power Wave Energy Converter Comprehensive Overview

These files collectively provide a comprehensive overview of the testing process, data analysis, and validation for the Twin Ocean Power device tested at the O.H. Hinsdale Wave Research Laboratory, supported by TEAMER funding. This resource includes an overview of power results for a series of 7 trials. The files included in this comprehensive overview include a comprehensive log sheet for each trial, a summary of all trials, and processing scripts for the raw data. It includes all raw data in .tsv and MATLAB compatible formats, an average power chart, angular velocity charts for each trial, trial metrics, and power output files. This resource includes images of the Twin Ocean Power Wave Energy Converter device components and movement during testing and video recordings of each trial.

16 TIDAL AND WAVE POWER↗

Thermopile Energy Harvesting for Subsurface Wellbore Sensors (Final Report)

Robust in situ power harvesting underlies all efforts to enable downhole autonomous sensors for real-time and long-term monitoring of CO 2 plume movement and permeance, wellbore health, and induced seismicity. This project evaluated the potential use of downhole thermopile arrays, known as thermoelectric generators (TEGs), as power sources to charge sensors for in situ real-time, long-term data capture and transmission. Real-time downhole monitoring will enable “Big Data” techniques and machine learning, using massive amounts of continuous data from embedded sensors, to quantify short- and long-term stability and safety of enhanced oil recovery and/or commercial-scale geologic CO 2 storage. This project evaluated possible placement of the TEGs at two different wellbore locations: on the outside of the casing; or on the production tubing. TEGs convert heat flux to electrical power, and in the borehole environment, would convert heat flux into or out of the borehole into power for downhole sensors. Such heat flux would be driven by pumping of cold or hot fluids into the borehole—for instance, injecting supercritical CO 2 —creating a thermal pulse that could power the downhole sensors. Hence, wireless power generation could be accomplished with in situ TEG energy harvesting. This final report summarizes the project’s efforts that accomplished the creation of a fully operational thermopile field unit, including selection of materials, laboratory benchtop experiments and thermal-hydrologic modeling for design and optimization of the field-scale power generation test unit. Finally, the report describes the field unit that has been built and presents results of performance and survivability testing. The performance and survivability testing evaluated the following: 1) downhole power generation in response to a thermal gradient produced by pumping a heated fluid down a borehole and through the field unit; and 2) component survivability and operation at elevated temperature and pressure conditions representative of field conditions. The performance and survivability testing show that TEG arrays are viable for generating ample energy to power downhole sensors, although it is important to note that developing or connecting to sensors was beyond the scope of this project. This project’s accomplishments thus traversed from a low Technical Readiness Level (TRL) on fundamental concepts of the application and modeling to TRL-5 via testing of the fully integrated field unit for power generation in relevant environments. A fully issued United States Patent covers the wellbore power harvesting technology and applications developed by this project.

47 OTHER INSTRUMENTATION↗

MgADP Promotes Myosin Head Movement toward Actin at Low [Ca 2+ ] to Increase Force Production and Ca 2+ -Sensitivity of Contraction in Permeabilized Porcine Myocardial Strips

Myosin cross-bridges dissociate from actin following Mg 2+ -adenosine triphosphate (MgATP) binding. Myosin hydrolyses MgATP into inorganic phosphate (P i ) and Mg 2+ -adenosine diphosphate (ADP), and release of these hydrolysis products drives chemo-mechanical energy transitions within the cross-bridge cycle to power muscle contraction. Some forms of heart disease are associated with metabolic or enzymatic dysregulation of the MgATP-MgADP nucleotide pool, resulting in elevated cytosolic [MgADP] and impaired muscle relaxation. We investigated the mechanical and structural effects of increasing [MgADP] in permeabilized myocardial strips from porcine left ventricle samples. Sarcomere length was set to 2.0 µm at 28 °C, and all solutions contained 3% dextran T-500 to compress myofilament lattice spacing to near-physiological values. Under relaxing low [Ca 2+ ] conditions (pCa 8.0, where pCa = -log 10 [Ca 2+ ]), tension increased as [MgADP] increased from 0-5 mM. Complementary small-angle X-ray diffraction measurements show that the equatorial intensity ratio, I 1,1 /I 1,0 , also increased as [MgADP] increased from 0 to 5 mM, indicating myosin head movement away from the thick-filament backbone towards the thin-filament. Ca 2+ -activated force-pCa measurements show that Ca 2+ -sensitivity of contraction increased with 5 mM MgADP, compared to 0 mM MgADP. These data show that MgADP augments tension at low [Ca 2+ ] and Ca 2+ -sensitivity of contraction, suggesting that MgADP destabilizes the quasi-helically ordered myosin OFF state, thereby shifting the cross-bridge population towards the disordered myosin ON state. Together, these results indicate that MgADP enhances the probability of cross-bridge binding to actin due to enhancement of both thick and thin filament-based activation mechanisms.

60 APPLIED LIFE SCIENCES↗

Admissible Powertrain Alternatives for Heavy-Duty Fleets: A Case Study on Resiliency and Efficiency

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H 2 ), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H 2 and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H 2 , and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H 2 and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping.

BEV↗

Lidar observations of wave-like structure in the atmospheric sodium layer

The University of Illinois (Urbana) lidar system has been developed to study the atmospheric sodium layer near 90 km altitude through the mechanism of resonance scattering. The photocount data are processed using digital smoothing filters to obtain continuous estimates of the sodium density versus altitude. The filter cutoff frequency is related to the height resolution and accuracy of the estimated profile. Lidar photocount data processed using this filtering technique show wave-like structures in the sodium layer which move downward with time. The waves have typical wavelengths of 3-15 km and phase velocities of less than 1 m/sec. The movement of these structures seems to be independent of the motion of the bottomside of the layer, which also has been observed to move up or down by as much as 2 km over a period of a few hours.

Rowlett, J. R.↗

Preliminary investigation of motion requirements for the simulation of helicopter hover tasks

Data from a preliminary experiment are presented which attempted to define a helicopter hover task that would allow the detection of objectively-measured differences in fixed base/moving base simulator performance. The addition of heave, pitch, and roll movement of a ship at sea to the hover task, by means of an adaption of a simulator g-seat, potentially fulfills the desired definition. The feasibility of g-seat substitution for platform motion can be investigated utilizing this task.

Parrish, R. V.↗

Comparison of manually produced and automated cross country movement maps using digital image processing techniques

The Image-Based Information System (IBIS) was used to automate the cross country movement (CCM) mapping model developed by the Defense Mapping Agency (DMA). Existing terrain factor overlays and a CCM map, produced by DMA for the Fort Lewis, Washington area, were digitized and reformatted into geometrically registered images. Terrain factor data from Slope, Soils, and Vegetation overlays were entered into IBIS, and were then combined utilizing IBIS-programmed equations to implement the DMA CCM model. The resulting IBIS-generated CCM map was then compared with the digitized manually produced map to test similarity. The numbers of pixels comprising each CCM region were compared between the two map images, and percent agreement between each two regional counts was computed. The mean percent agreement equalled 86.21%, with an areally weighted standard deviation of 11.11%. Calculation of Pearson's correlation coefficient yielded +9.997. In some cases, the IBIS-calculated map code differed from the DMA codes: analysis revealed that IBIS had calculated the codes correctly. These highly positive results demonstrate the power and accuracy of IBIS in automating models which synthesize a variety of thematic geographic data.

Wynn, L. K.↗

Data Presentation of Force Characteristics of Several Engine-strut-body Configurations at Mach Numbers of 1.8 and 2.0 / Robert T. Madden and Emil J. Kremzier

Investigation to determine lift, drag, and pitching-moment characteristics of several engine-strut-body combinations was conducted over range of angles of attack from 0 degrees to 10 degrees at Mach numbers of 1.8 and 2.0. The average Reynolds number based on body length was 28x106. Data are presented without analysis and indicate decreases in minimum drag and lift curve slope with decreasing in minimum drag and lift curve slope with decreasing strut length. Decreases in minimum drag also noted with rear-ward movement of engines.

PROTUBERANCES - BODIES↗

Wind-tunnel research comparing lateral control devices, particularly at high angles of attack I : ordinary ailerons on rectangular wings

This report is the first in a series in which it is intended to compare the relative merits of all ordinary and some special forms of ailerons and other lateral control devices in regard to their effect on lateral controllability, lateral stability, and airplane performance. The comparisons are based on wind-tunnel test data, all the control devices being fitted to model wings having the same span, area, and airfoil section, and being subjected to the same series of force and rotation tests. The results are given for five different aileron movements: one with equal up-and-down deflection, one with average and one with extreme differential motion, one with upward deflection only, and one with the ailerons arranged to float with respect to the wing.

Weick, Fred E↗

Satellite-Respondent Buoys Identify Ocean Debris

NASA operates a series of Earth-observing satellites, which help scientists learn more about our home planet. Through partnerships with universities and other government agencies, like the National Oceanic and Atmospheric Administration (NOAA), the Space Agency helps scientists around the world capture precise movements of the Earth s crust to learn more about the underground processes related to earthquakes and volcanic eruptions, create accurate assessments of wind resources for future energy use, and preserve endangered species by generating much-needed data about their environments. This work, done primarily from space with satellites using a variety of complex instruments to take readings of the surface below, generates leagues of valuable data that aid scientists on the ground - or in some cases on the water. As much of the Earth is covered in water liquid, frozen, saltwater, or fresh much of NASA s remote sensing work focuses on the oceans and their health. This valuable, mammoth (yet fragile) resource provides insight into the overall health of our planet, as water, in addition to being abundant, is a key ingredient to all known life on Earth. As part of its ocean-observing work, NASA partnered with NOAA and private industry to develop remote sensing technologies for protecting the seas of the North Pacific from a nefarious and pervasive problem: derelict fishing gear.

Source record↗

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗

Coal face measurement system for underground use

A measurement system was developed for the Eickhoff longwall shearer to determine the contour of the coal face as it mines coal. Contour data are obtained by an indirect measurement technique based on evaluating the motion of the shearer during mining. Starting from a known location, points along the coal face are established through a knowledge of the machines' positions and yaw movements as it moves past the coal face. The hardware and system operation procedures are described. The tests of system performance and their results are reported.

Source record↗

Oceanographic and meteorological research based on the data products of SEASAT

Reservations were expressed concerning the sum of squares wind recovery algorithm and the power law model function. The SAS sum of squares (SOS) method for recovering winds from backscatter data leads to inconsistent results when V pol and H pol winds are compared. A model function that does not use a power law and that accounts for sea surface temperature is needed and is under study both theoretically and by means of the SASS mode 4 data. Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts are elaborated. The operational aspect of an intermittent assimilation scheme currently utilized for the specification of the initial value field is considered with focus on quantifying the absolute 12-hour linear displacement error of the movement of low centers.

Pierson, W. J., Jr.↗

Three-dimensional ballistocardiography and respiratory motion in sustained microgravity

BACKGROUND: We measured the three-dimensional ballistocardiogram (BCG) in a free-floating subject in sustained microgravity during spaceflight to test the usefulness of such measurements for future non-invasive monitoring of cardiac function, and to examine the effects of respiratory movement on the BCG in three axes. METHODS: Acceleration was measured using a three-axis accelerometer fastened to the lumbar region of the subject while simultaneous recordings of ECG, and respiratory motion via impedance plethysmography were also made. Data were recorded during a 146-s period of inactivity on the part of the subject during which time there was no contact with the spacecraft. RESULTS: Total body motion due to respiratory activity was consistent with that calculated from the known action of the diaphragm and conservation of momentum. The accelerations due to cardiac activity, ensemble averaged over the R-R interval, were greatest along the head-to-foot axis. Maximum amplitude of the HIJK complex of the BCG generated by ventricular ejection was greatest in the head to foot axis (approximately 70 x 10(-3) m x s(-2)), but there were also substantial accelerations along the dorsoventral axis of up to 43 10(-3) m x s(-2), that are not measured interrestrial two-dimensional studies. The amplitude of the BCG was strongly affected by lung volume, with accelerations being reduced 50 to 70% between end-inspiration and end-expiration. CONCLUSIONS: These data suggest a greatly reduced transmission of the cardiac motion to the body at end-expiration (FRC) than at higher lung volumes. The BCG might be further developed as a non-invasive means of monitoring parameters such as stroke volume in microgravity.

Flight Experiment↗

A role for cyclin-dependent kinase(s) in the modulation of fast anterograde axonal transport: effects defined by olomoucine and the APC tumor suppressor protein

Proteins that interact with both cytoskeletal and membrane components are candidates to modulate membrane trafficking. The tumor suppressor proteins neurofibromin (NF1) and adenomatous polyposis coli (APC) both bind to microtubules and interact with membrane-associated proteins. The effects of recombinant NF1 and APC fragments on vesicle motility were evaluated by measuring fast axonal transport along microtubules in axoplasm from squid giant axons. APC4 (amino acids 1034-2844) reduced only anterograde movements, whereas APC2 (aa 1034-2130) or APC3 (aa 2130-2844) reduced both anterograde and retrograde transport. NF1 had no effect on organelle movement in either direction. Because APC contains multiple cyclin-dependent kinase (CDK) consensus phosphorylation motifs, the kinase inhibitor olomoucine was examined. At concentrations in which olomoucine is specific for cyclin-dependent kinases (5 microM), it reduced only anterograde transport, whereas anterograde and retrograde movement were both affected at concentrations at which other kinases are inhibited as well (50 microM). Both anterograde and retrograde transport also were inhibited by histone H1 and KSPXK peptides, substrates for proline-directed kinases, including CDKs. Our data suggest that CDK-like axonal kinases modulate fast anterograde transport and that other axonal kinases may be involved in modulating retrograde transport. The specific effect of APC4 on anterograde transport suggests a model in which the binding of APC to microtubules may limit the activity of axonal CDK kinase or kinases in restricted domains, thereby affecting organelle transport.

Non-NASA Center↗