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At least 487 records · Page 27

Cooperative Education

M-3 Focused Experiments is a group at Los Alamos National Lab (LANL) and is under the Dynamic Experiments Division. Their work surrounds the diagnostics and study of explosives and their interaction with materials. The data collected is used to better understand the erratic nature of explosions and high velocity projectiles. Through better comprehension of this science, the overall national security will be improved while innovations are made along the way. Despite the strong drive for research and development, the lab holds safety above all else and constantly updates procedures to protect LANL employees and their environment. At any time, there are multiple projects going on at once within the team and I am tasked with assisting the Firing Site Leaders and various research diagnosticians in any way possible with the setup, testing, and execution of data collection for each experiment. Such data includes velocity, fragmentation characteristics, and the spread of energy across the material to name a few. During my summer employment with the lab, my main goal is to maintain a questioning attitude and learn as much possible about the research and development side of science and engineering. Throughout the duration of the Co-op, many experiments will take place at specially designed firing sites which is where I will be spending most of my time.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Coupling Remote Sensing and Sonar Measurements to Estimate Availability of Suitable Backwater Nursery Habitat for Colorado Pikeminnow

Low-velocity channel-margin habitats serve as important nursery habitats for the endangered Colorado pikeminnow (Ptychocheilus lucius) in the middle Green River between Jensen and Ouray, Utah. These habitats, known as backwaters, are associated with emergent sandbars and are shaped and reformed annually by peak flows. Recent knowledge about backwater characteristics and dynamics was summarized in a synthesis report (Grippo et al. 2015) that was based on detailed annual survey data collected from a relatively small sample of backwaters between 2003 to 2014 and reach-wide evaluations of backwater surface area based on manual interpretation of aerial and satellite imagery. Methods that bridge the gap between the detailed surveys from a small number of backwaters and reach-wide assessments of their surface area would enable an improved assessment of the availability of backwater habitats that provide suitable depths for young-of-the-year (YOY) Colorado pikeminnow.

13 HYDRO ENERGY↗

Development of capabilities for stall/spin research

Apparatus and techniques were developed for measuring in a low-speed wind tunnel the static and dynamic (rotary balance) aerodynamic data pertinent to spin behavior of a general aviation aircraft. The main results were: (1) collection of static force and moment data for several airplane configurations at angles of attack from 0 to 90 degrees and angles of sideslip from 0 to 40 degrees; and (2) difficulties, shortcomings, and unsuitability of some aspects of the rotary balance mount as constructed were discovered and identified for avoidance in a new design for a mount.

Craig, A.↗

Programmable, automated transistor test system

A programmable, automated transistor test system was built to supply experimental data on new and advanced power semiconductors. The data will be used for analytical models and by engineers in designing space and aircraft electric power systems. A pulsed power technique was used at low duty cycles in a nondestructive test to examine the dynamic switching characteristic curves of power transistors in the 500 to 1000 V, 10 to 100 A range. Data collection, manipulation, storage, and output are operator interactive but are guided and controlled by the system software.

Truong, L. V.↗

Acoustic testing of high temperature panels

Results are presented of a series of thermal-acoustic tests conducted on the NASA Langley Research Center Thermal-Acoustic Test Apparatus to (1) investigate techniques for obtaining strain measurements on metallic and carbon-carbon materials at elevated temperature; (2) document the dynamic strain response characteristics of several superalloy honeycomb thermal protection system panels at elevated temperatures of up to 1200 F; and (3) determine the strain response and sonic fatigue behavior of four carbon-carbon panels at both ambient and elevated temperatures. A second study tested four carbon-carbon panels to document panel dynamic response characteristics at ambient and elevated temperature, determine time to failure and faliure modes, and collect continuous strain data up to panel failure. Strain data are presented from both types of panels, and problems encountered in obtaining reliable strain data on the carbon-carbon panels are described. The failure modes of the carbon-carbon panels are examined.

Leatherwood, Jack D.↗

Characterizing and Modeling the Cost of Rework in a Library of Reusable Software Components

In this paper we characterize and model the cost of rework in a Component Factory (CF) organization. A CF is responsible for developing and packaging reusable software components. Data was collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC. We then constructed a predictive model of the cost of rework using the C4.5 system for generating a logical classification model. The predictor variables for the model are measures of internal software product attributes. The model demonstrates good prediction accuracy, and can be used by managers to allocate resources for corrective maintenance activities. Furthermore, we used the model to generate proscriptive coding guidelines to improve programming, practices so that the cost of rework can be reduced in the future. The general approach we have used is applicable to other environments.

Basili, Victor R.↗

Comparison of NTF Experimental Data with CFD Predictions from the Third AIAA CFD Drag Prediction Workshop

Recently acquired experimental data for the DLR-F6 wing-body transonic transport con figuration from the National Transonic Facility (NTF) are compared with the database of computational fluid dynamics (CFD) predictions generated for the Third AIAA CFD Drag Prediction Workshop (DPW-III). The NTF data were collected after the DPW-III, which was conducted with blind test cases. These data include both absolute drag levels and increments associated with this wing-body geometry. The baseline DLR-F6 wing-body geometry is also augmented with a side-of-body fairing which eliminates the flow separation in this juncture region. A comparison between computed and experimentally observed sizes of the side-of-body flow-separation bubble is included. The CFD results for the drag polars and separation bubble sizes are computed on grids which represent current engineering best practices for drag predictions. In addition to these data, a more rigorous attempt to predict absolute drag at the design point is provided. Here, a series of three grid densities are utilized to establish an asymptotic trend of computed drag with respect to grid convergence. This trend is then extrapolated to estimate a grid-converged absolute drag level.

Vassberg, John C.↗

Flutter Prediction Report in Support of the High Angle Working Group at the Third Aeroelastic Prediction Workshop

This paper presents a summary of the computational flutter results associated with the AIAA third Aeroelastic Prediction Workshop (AePW-3) High Angle Working Group (HAWG). The computational results are compared against the experimental data collected during the Pitch and Plunge Apparatus (PAPA) Benchmark Supercritical Wing (BSCW) test campaign conducted in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center thirty years ago. During that test several flutter points were identified at transonic conditions. One of these points, specifically near Mach 0.8 and 5° angle of attack, became a focal point of the computational challenge with in HAWG. Various fidelity time-domain, Reduced Order Model (ROM) and Linearized Frequency Domain (LFD) methods were used by seven participating teams. While there are encouraging trends in the computational results, the range of the predicted flutter dynamic pressure is still quite large due to the stall flutter mechanism. A description of each participating team’s software and methods is included.

Pawel Chwalowski↗

Spacecraft Fire Safety Predictions using Verified Saffire Model

A model developed using Fire Dynamics Simulator (FDS) that aimed to determine the effect of a fire in a spacecraft was validated by data collected during the Saffire campaign. The model used inlet and outlet temperatures and CO 2 concentrations of the Saffire payload where fire spread was taking place to determine the amount of heat and combustion products that made it into Northrop Grumman’s Cygnus vehicle. The model was then validated using six remote sensors in various places in the vehicle, as well as a far field device (FFD) in the open zenith section that was representative of average vehicle values. The current work focuses on using the model to predict fire safety scenarios. One simulation aimed to determine the fate of HCl, which sticks to surfaces. The model prediction showed that the HCl was removed from the atmosphere rapidly. This compared well against the FFD data in the Saffire VI campaign event where a bottle of 5% HCl was released into the vehicle. An additional simulation where the Environmental Control and Life Support System (ECLSS) was shut off once the FFD reached 5 ppm of HCl showed that HCl stayed in the atmosphere considerably longer. Continuing the simulation with the ECLSS activated and after temperatures returned to their initial conditions, resulted in a rapid removal of HCL similar to that observed in the original HCl release scenario model. Finally, a simulation that used the heat release rate from a lithium-ion battery test to determine the effect it would have on a spacecraft was performed. This simulation used a heat addition rate that is considerably higher than what was determined from the burning of solid fuels in the Saffire campaign and hence produced a non-trivial temperature increase in more locations within the vehicle.

Fire Safety↗

Analytical Identification Method of Generalized Short‐Circuit Ratio Using Phasor Measurement Units

This paper introduces a novel analytical approach for the identification of the admittance matrix and the generalized short-circuit ratio (gSCR) in power systems integrated with renewable energy sources. The proposed method leverages voltage and current measurements from phasor measurement units (PMUs) to construct a least squares objective function, which is then solved using matrix calculus and partial derivatives. Unlike conventional optimization algorithms, this approach provides an analytical solution that substantially reduces data requirements, enabling the efficient and accurate identification of the gSCR with smaller datasets. Additionally, its fixed computational complexity allows for real-time updates as new data are collected, ensuring continuous refinement of the system of equations and enabling rapid, precise gSCR calculations. The method also exhibits strong robustness against measurement noise, making it well-suited for practical applications in dynamic power systems. The combination of reduced data requirements, real-time adaptability, noise robustness and fixed computational load establishes this method as a highly efficient and reliable tool for real-time power system stability analysis. Case studies on an EPRI 36-bus system demonstrate the method's effectiveness, highlighting its accuracy in closely matching true gSCR values, even under diverse disturbances and noisy conditions.

Han, Zelei [Hohai University, Nanjing (China)] (OR↗

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Navier-Stokes and Comprehensive Analysis Performance Predictions of the NREL Phase VI Experiment

A vortex lattice code, CAMRAD II, and a Reynolds-Averaged Navier-Stoke code, OVERFLOW-D2, were used to predict the aerodynamic performance of a two-bladed horizontal axis wind turbine. All computations were compared with experimental data that was collected at the NASA Ames Research Center 80- by 120-Foot Wind Tunnel. Computations were performed for both axial as well as yawed operating conditions. Various stall delay models and dynamics stall models were used by the CAMRAD II code. Comparisons between the experimental data and computed aerodynamic loads show that the OVERFLOW-D2 code can accurately predict the power and spanwise loading of a wind turbine rotor.

NREL (NATIONAL RENEWABLE ENERGY LABORATORY)↗

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

13 HYDRO ENERGY↗

A Bayesian statistical approach to inferring particle dynamics from in-situ pump POC and chloropigment data from the Mediterranean Sea

Concentrations of chloropigments and particulate organic carbon (POC) in large-volume in-situ pump samples from the Mediterranean Sea were used to estimate rate constants of processes that control the fate of particles, and specifically chloropigments, in the water column. In this paper we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and pheopigment degradation, and particle aggregation and disaggregation. We use first-order kinetics to model disaggregation, and use both first-order and second-order kinetics to model aggregation. Using these methods, the estimated small-particle (1–70 μm) POC respiration rate constant was 2.44 -1.00 +1.69 yr -1 (0.41 yr). The estimated disaggregation and second-order aggregation rate constants were 85.6 -36.4 +63.4 yr -1 (1.17×10 -2 yr) and 2.78 -1.17 +2.01 μM -1 yr -1 , respectively. Using the optimal rate constants and the corresponding particle concentrations, disaggregation is ~4.2 times faster than the small-size POC dissolution rate, which indicates that disaggregation is a dominant processes at the time of sampling. More importantly, by comparing our results with those of previous studies, we conclude that sampling methods have less influence than tracers themselves on inferring particle dynamic rate constants. We previously introduced a somewhat similar approach to modeling SV sediment trap data, but large volume pumps are a much more common sample collection method in oceanographic surveys than SV sediment traps, and thus our new model should have a wider applicability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Analysis of dynamic radiation level changes using surface networks [Book Chapter]

The Fukushima nuclear accident reminds us of severe risk of radioactive substances. Citizen scientists voluntarily collect and share radiation data using geo-tagged sensors for radiation preparedness. However, radiation levels are affected by a number of factors including for example weather conditions, naturally occurring radioactive materials (NORM), and large marble structures. It is therefore difficult to determine whether a higher radiation level comes from a normal variation in background or not. This research analyzes the radiation changes using surface networks that can be used to characterize complex surfaces. A new algorithm has been developed to identify salient peaks and their hills by merging insignificant peaks recur-sively. Salient peaks and their hills are converted into graphs. The structural similarities of graphs were compared over time. The radiation measurements in the city of Koriyama, Japan were analyzed as a case study. The results demonstrated that structural analysis of dynamic radiation levels revealed stable changes, while numeric analysis of radiation levels presented statistically significant differences. This method is able to detect radiation level changes irrespective of background variations.

97 MATHEMATICS AND COMPUTING↗

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗

High Speed Inlet Distortion Test for the X 59 Low Boom Flight Demonstrator in the NASA Glenn 8-by 6-Foot Supersonic Wind Tunnel

The Low Boom Flight Demonstrator (LBFD) High Speed Inlet Distortion Test (HSIDT) was completed in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel (8x6 SWT) using a 9.5 percent-scale propulsion model of the X–59 to evaluate the compatibility and operability of the inlet and engine integration, focusing on the inlet dynamic effects at the engine face. The model was instrumented with steady-state and high-response total pressure probes in the conventional 40-point, equal-area array at the aerodynamic interface plane (AIP). The HSIDT successfully collected high response distortion data points for the X–59 from Mach = 0 to 1.55 for a wide range of angle-of-attack, angle-of-sideslip, and inlet flow. Analyses showed that inlet dynamic distortion and inlet planar wave (or inlet buzz) were within engine limits through most of the flight envelope. The test data showed that inlet planar wave was present at Mach 1.35 and above, at airflows between idle and maximum engine airflow. Engine throttle back will need to be limited when decelerating from supersonic flight Mach to avoid these regions of inlet planar wave. The HSIDT data was also used to compare the metric SUM40, computed from the 40 high-response AIP total pressure probes, with the metric SUM4, computed from four high-response inlet static pressure probes. The data showed that SUM4 can be used as a surrogate for SUM40 during the X–59 flight test to detect inlet planar wave.

X-59↗

Air Traffic Management Technology Demonstration - 3 (ATD-3) Dynamic Weather Routes Domestic En Route Concept of Operations Synopsis Version 1.0

NASA conducted a survey in 2015 of stakeholders in industry, flight operators, and others to collect data on the most pressing air traffic management (ATM) issues impacting en route operations. Weather related delays and interruptions were identified as the most significant and costly problem impacting en route operations in today's system. Additionally, future traffic demands will place greater demands on the FAA's air traffic control system, necessitate a more efficient and integrated traffic management system and provide controller tools to leverage human productivity. Consequently, NASA is assembling a suite of integrated ground and aircraft-based technologies and decision-making aids under the Airspace Technology Demonstrations 3 (ATD-3) project to continuously search for more efficient weather-avoidance routes and to rapidly respond to weather changes and associated traffic management initiatives. These route efficiency enhancements are targeted at en route airspace and will enable continued use of en route and arrival metering in the presence of weather. NASA's Dynamic Weather Routes (DWR) concept represents a subset of those technologies and focuses primarily on the cruise phase of flight. It leverages existing air traffic weather, airspace, and traffic data, as well as improvements in navigation, surveillance, communication and digital network technologies, and builds upon existing ATM automation to address the shortcomings associated with strategic traffic flow management initiatives and weather forecasting uncertainties. The potential benefits in the form of time, fuel, and cost savings are significant. This concept of operations synopsis provides a detailed description of DWR, its potential benefits and notional implementation paths.

DWR↗