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NASA NTRS2000

Performance of the OVERFLOW-MLP and LAURA-MLP CFD Codes on the NASA Ames 512 CPU Origin System

The shared memory Multi-Level Parallelism (MLP) technique, developed last year at NASA Ames has been very successful in dramatically improving the performance of important NASA CFD codes. This new and very simple parallel programming technique was first inserted into the OVERFLOW production CFD code in FY 1998. The OVERFLOW-MLP code's parallel performance scaled linearly to 256 CPUs on the NASA Ames 256 CPU Origin 2000 system (steger). Overall performance exceeded 20.1 GFLOP/s, or about 4.5x the performance of a dedicated 16 CPU C90 system. All of this was achieved without any major modification to the original vector based code. The OVERFLOW-MLP code is now in production on the inhouse Origin systems as well as being used offsite at commercial aerospace companies. Partially as a result of this work, NASA Ames has purchased a new 512 CPU Origin 2000 system to further test the limits of parallel performance for NASA codes of interest. This paper presents the performance obtained from the latest optimization efforts on this machine for the LAURA-MLP and OVERFLOW-MLP codes. The Langley Aerothermodynamics Upwind Relaxation Algorithm (LAURA) code is a key simulation tool in the development of the next generation shuttle, interplanetary reentry vehicles, and nearly all "X" plane development. This code sustains about 4-5 GFLOP/s on a dedicated 16 CPU C90. At this rate, expected workloads would require over 100 C90 CPU years of computing over the next few calendar years. It is not feasible to expect that this would be affordable or available to the user community. Dramatic performance gains on cheaper systems are needed. This code is expected to be perhaps the largest consumer of NASA Ames compute cycles per run in the coming year.The OVERFLOW CFD code is extensively used in the government and commercial aerospace communities to evaluate new aircraft designs. It is one of the largest consumers of NASA supercomputing cycles and large simulations of highly resolved full aircraft are routinely undertaken. Typical large problems might require 100s of Cray C90 CPU hours to complete. The dramatic performance gains with the 256 CPU steger system are exciting. Obtaining results in hours instead of months is revolutionizing the way in which aircraft manufacturers are looking at future aircraft simulation work. Figure 2 below is a current state of the art plot of OVERFLOW-MLP performance on the 512 CPU Lomax system. As can be seen, the chart indicates that OVERFLOW-MLP continues to scale linearly with CPU count up to 512 CPUs on a large 35 million point full aircraft RANS simulation. At this point performance is such that a fully converged simulation of 2500 time steps is completed in less than 2 hours of elapsed time. Further work over the next few weeks will improve the performance of this code even further.The LAURA code has been converted to the MLP format as well. This code is currently being optimized for the 512 CPU system. Performance statistics indicate that the goal of 100 GFLOP/s will be achieved by year's end. This amounts to 20x the 16 CPU C90 result and strongly demonstrates the viability of the new parallel systems rapidly solving very large simulations in a production environment.

Taft, James R.↗

NASA NTRS2011

High-Performance 3D Articulated Robot Display

In the domain of telerobotic operations, the primary challenge facing the operator is to understand the state of the robotic platform. One key aspect of understanding the state is to visualize the physical location and configuration of the platform. As there is a wide variety of mobile robots, the requirements for visualizing their configurations vary diversely across different platforms. There can also be diversity in the mechanical mobility, such as wheeled, tracked, or legged mobility over surfaces. Adaptable 3D articulated robot visualization software can accommodate a wide variety of robotic platforms and environments. The visualization has been used for surface, aerial, space, and water robotic vehicle visualization during field testing. It has been used to enable operations of wheeled and legged surface vehicles, and can be readily adapted to facilitate other mechanical mobility solutions. The 3D visualization can render an articulated 3D model of a robotic platform for any environment. Given the model, the software receives real-time telemetry from the avionics system onboard the vehicle and animates the robot visualization to reflect the telemetered physical state. This is used to track the position and attitude in real time to monitor the progress of the vehicle as it traverses its environment. It is also used to monitor the state of any or all articulated elements of the vehicle, such as arms, legs, or control surfaces. The visualization can also render other sorts of telemetered states visually, such as stress or strains that are measured by the avionics. Such data can be used to color or annotate the virtual vehicle to indicate nominal or off-nominal states during operation. The visualization is also able to render the simulated environment where the vehicle is operating. For surface and aerial vehicles, it can render the terrain under the vehicle as the avionics sends it location information (GPS, odometry, or star tracking), and locate the vehicle over or on the terrain correctly. For long traverses over terrain, the visualization can stream in terrain piecewise in order to maintain the current area of interest for the operator without incurring unreasonable resource constraints on the computing platform. The visualization software is designed to run on laptops that can operate in field-testing environments without Internet access, which is a frequently encountered situation when testing in remote locations that simulate planetary environments such as Mars and other planetary bodies.

Powell, Mark W.↗

DOE OSTI2018

The multichannel $n$-propyl + O 2 reaction surface: Definitive theory on a model hydrocarbon oxidation mechanism

The n-propyl + O 2 reaction is an important model of chain branching reactions in larger combustion systems. In this work, focal point analyses (FPAs) extrapolating to the ab initio limit were performed on the n-propyl + O 2 system based on explicit quantum chemical computations with electron correlation treatments through coupled cluster single, double, triple, and perturbative quadruple excitations [CCSDT(Q)] and basis sets up to cc-pV5Z. All reaction species and transition states were fully optimized at the rigorous CCSD(T)/cc-pVTZ level of theory, revealing some substantial differences in comparison to the density functional theory geometries existing in the literature. A mixed Hessian methodology was implemented and benchmarked that essentially makes the computations of CCSD(T)/cc-pVTZ vibrational frequencies feasible and thus provides critical improvements to zero-point vibrational energies for the n-propyl + O 2 system. Two key stationary points, n-propylperoxy radical (MIN1) and its concerted elimination transition state (TS1), were located 32.7 kcal mol -1 and 2.4 kcal mol -1 below the reactants, respectively. Two competitive $β$-hydrogen transfer transition states (TS2 and TS2') were found separated by only 0.16 kcal mol -1 , a fact unrecognized in the current combustion literature. Incorporating TS2' in master equation (ME) kinetic models might reduce the large discrepancy of 2.5 kcal mol -1 between FPA and ME barrier heights for TS2. TS2 exhibits an anomalously large diagonal Born-Oppenheimer correction ($Δ$ DBOC = 1.71 kcal mol -1 ), which is indicative of a nearby surface crossing and possible nonadiabatic reaction dynamics. The first systematic conformational search of three hydroperoxypropyl (QOOH) intermediates was completed, uncovering a total of 32 rotamers lying within 1.6 kcal mol -1 of their respective lowest-energy minima. Our definitive energetics for stationary points on the $n$-propyl + O 2 potential energy surface provide key benchmarks for future studies of hydrocarbon oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Land-Based Wind Market Report: 2021 Edition

The U.S. Department of Energy's 2021 edition of its land-based wind market report provides an overview of key trends in the U.S. wind power market, with a focus on 2020. You can find a report, data file and presentation on the Files tab, below. Additionally, several data visualizations are available on the Visualizations tab. Highlights of this year’s update include: -Wind comprises a growing share of electricity supply: U.S. wind power capacity grew at a record pace in 2020, with 25 billion dollars invested in 16.8 GW of capacity. Wind energy output rose to account for more than 8% of the entire nation’s electricity supply, and is more than 20% in 10 states. At least 209 GW of wind are seeking transmission interconnection; 61 GW of this capacity are offshore wind and 13 GW are hybrid plants that pair wind with storage or PV. -Wind project performance has increased over time: The average capacity factor among recently built projects was over 40%, considerably higher than projects built earlier. The highest capacity factors are seen in the interior ‘wind belt’ of the country. -Turbines continue to get larger: Improved plant performance has been driven by larger turbines mounted on taller towers and featuring longer blades. In 2010, no turbines employed blades that were 115 meters in diameter or larger, but in 2020, 91% of newly installed turbines featured such rotors. Proposed projects indicate that total turbine height will continue to rise. -Low wind turbine pricing has pushed down installed project costs over the last decade: Wind turbine prices are averaging 775–850 dollars/kW. The average installed cost of wind projects in 2020 was 1,460 dollars/kW, down more than 40% since the peak in 2010, though stable for the last three years. The lowest costs were found in Texas and the (non-ISO) West. -Wind energy prices remain low, around 20 dollars/MWh in the interior of the country: After topping out at 70 dollars/MWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped. In the interior ‘wind belt’ of the country, recent pricing is around 20 dollars/MWh. In the West and East, prices tend to average 30 dollars/MWh or more. These prices, which are possible in part due to federal tax support, fall below the projected future fuel costs of gas-fired generation. -Wind PPA prices are often attractive compared to wind’s grid-system market value: The value of wind in wholesale power markets is affected by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind declined in 2020, following natural gas prices lower and averaging under 15 dolalrs/MWh in ERCOT, MISO, NYISO and SPP; higher values were seen in CAISO, ISO-NE and PJM. -The average levelized cost of wind energy is down to 33 dollars/MWh: Levelized costs, which exclude the impacts of federal tax incentives, vary across time and geography, but the national average stood at 33 dollars/MWh in 2020—down substantially historically, though consistent with the previous two years. Levelized costs were lowest in ERCOT, SPP, and the (non-ISO) West. -The health and climate benefits of wind in 2020 were larger than its grid-system value, and the combination of all three far exceeds the current levelized cost of wind: Wind generation reduces power-sector emissions of carbon dioxide, nitrogen oxides, and sulfur dioxide. These reductions, in turn, provide public health and climate benefits that vary regionally, but together are economically valued at an average of 76 dollars/MWh-wind nationwide in 2020. -The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>85%), towers (60-75%), and blades and hubs (30-50%), but is much lower for most components internal to the nacelle.

17 WIND ENERGY↗

NASA NTRS2014

Improved Algorithms for Accurate Retrieval of UV - Visible Diffuse Attenuation Coefficients in Optically Complex, Inshore Waters

Photochemical processes driven by high-energy ultraviolet radiation (UVR) in inshore, estuarine, and coastal waters play an important role in global bio geochemical cycles and biological systems. A key to modeling photochemical processes in these optically complex waters is an accurate description of the vertical distribution of UVR in the water column which can be obtained using the diffuse attenuation coefficients of down welling irradiance (Kd()). The Sea UV Sea UVc algorithms (Fichot et al., 2008) can accurately retrieve Kd ( 320, 340, 380,412, 443 and 490 nm) in oceanic and coastal waters using multispectral remote sensing reflectances (Rrs(), Sea WiFS bands). However, SeaUVSeaUVc algorithms are currently not optimized for use in optically complex, inshore waters, where they tend to severely underestimate Kd(). Here, a new training data set of optical properties collected in optically complex, inshore waters was used to re-parameterize the published SeaUVSeaUVc algorithms, resulting in improved Kd() retrievals for turbid, estuarine waters. Although the updated SeaUVSeaUVc algorithms perform best in optically complex waters, the published SeaUVSeaUVc models still perform well in most coastal and oceanic waters. Therefore, we propose a composite set of SeaUVSeaUVc algorithms, optimized for Kd() retrieval in almost all marine systems, ranging from oceanic to inshore waters. The composite algorithm set can retrieve Kd from ocean color with good accuracy across this wide range of water types (e.g., within 13 mean relative error for Kd(340)). A validation step using three independent, in situ data sets indicates that the composite SeaUVSeaUVc can generate accurate Kd values from 320 490 nm using satellite imagery on a global scale. Taking advantage of the inherent benefits of our statistical methods, we pooled the validation data with the training set, obtaining an optimized composite model for estimating Kd() in UV wavelengths for almost all marine waters. This optimized composite set of SeaUVSeaUVc algorithms will provide the optical community with improved ability to quantify the role of solar UV radiation in photochemical and photobiological processes in the ocean.

UV↗

NASA NTRS2003

Investigation of the Surface Stress in SiC and Diamond Nanocrystals by In-situ High Pressure Powder Diffraction Technique

The real atomic structure of nanocrystals determines key properties of the materials. For such materials the serious experimental problem lies in obtaining sufficiently accurate measurements of the structural parameters of the crystals, since very small crystals constitute rather a two-phase than a uniform crystallographic phase system. As a result, elastic properties of nanograins may be expected to reflect a dual nature of their structure, with a corresponding set of different elastic property parameters. We studied those properties by in-situ high-pressure powder diffraction technique. For nanocrystalline, even one-phase materials such measurements are particularly difficult to make since determination of the lattice parameters of very small crystals presents a challenge due to inherent limitations of standard elaboration of powder diffractograms. In this investigation we used our methodology of the structural analysis, the 'apparent lattice parameter' (alp) concept. The methodology allowed us to avoid the traps (if applied to nanocrystals) of standard powder diffraction evaluation techniques. The experiments were performed for nanocrystalline Sic and GaN powders using synchrotron sources. We applied both hydrostatic and isostatic pressures in the range of up to 40 GPa. Elastic properties of the samples were examined based on the measurements of a change of the lattice parameters with pressure. The results show a dual nature of the mechanical properties (compressibilities) of the materials, indicating a complex, core-shell structure of the grains.

Palosz, B.↗

NASA NTRS2008

Commercial Product Activation Using RFID

Radio-frequency identification (RFID) would be used for commercial product activation, according to a proposal. What is new here is the concept of combining RFID with activation - more specifically, using RFID for activating commercial products (principally, electronic ones) and for performing such ancillary functions as tracking individual product units on production lines, tracking shipments, and updating inventories. According to the proposal, an RFID chip would be embedded in each product. The information encoded in the chip would include a unique number for identifying the product. An RFID reader at the point of sale would record the number of the product and would write digital information to the RFID chip for either immediate activation of the product or for later interrogation and processing. To be practical, an RFID product-activation system should satisfy a number of key requirements: the system should be designed to be integrable into the inventory-tracking and the data-processing and -communication infrastructures of businesses along the entire supply chain from manufacture to retail; the system should be resistant to sophisticated hacking; activation codes should be made sufficiently complexity to minimize the probability of activating stolen products; RFID activation equipment at points of sale must be capable to two-way RF communication for the purposes of reading information from, and writing information to, embedded RFID chips; the equipment at points of sale should be easily operable by sales clerks with little or no training; the point-of-sale equipment should verify activation and provide visible and/or audible signals indicating verification or lack thereof; and, the system should be able to handle millions of products per year with minimal human intervention, among other requirements.

Jedrey, Thomas↗

DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2020

Investigation of a Helicopter Individual Blade Control (IBC) System in Two Full-Scale Wind Tunnel Tests: Volume I

This report presents the data acquired during the testing of an individual blade control (IBC) system on a full-scale helicopter rotor in two test entries in the NASA Ames 40- by 80-Foot Wind Tunnel. The objective of these investigations was to evaluate the potential benefits of using IBC to improve rotor performance, reduce blade vortex interaction (BVI) noise, and alleviate helicopter vibrations. The wind tunnel tests were an international, collaborative effort between NASA, the U.S. Army Aeroflightdynamics Directorate, ZF Luftfahrttechnik GmbH, Eurocopter Deutschland GmbH, and the German Aerospace Laboratory (DLR). They were conducted as a task of the U.S./German Memorandum of Understanding (MOU) on Helicopter Aeromechanics. The IBC tests were performed using a full-scale BO-105 helicopter rotor mounted to the NASA/U.S. Army Rotor Test Apparatus (RTA). The first test, performed in 1993, was the first full-scale wind tunnel test to explore the effects of an IBC system on rotor vibration, noise, and performance. In this test, the pitch links of the rotor were replaced by servo-actuators. The servo-actuators and IBC control system were designed and manufactured by ZF Luftfahrttechnik GmbH. This control system allowed the pitch of each rotor blade to be changed independently of the other blades. The IBC inputs had large effects on the hub vibrations and BVI noise. However, the rotor hub moments were not retrimmed with each new IBC input. This resulted in an out-of-trim rotor configuration. The same IBC system was used in the second IBC test performed in 1994. This test more carefully investigated the potential of IBC to simultaneously reduce noise and vibration and also investigated the effect of IBC on rotor performance in high-speed-cruise flight. In the 1994 test, the hub moment and rotor thrust were readjusted to maintain rotor trim as the IBC inputs were applied. For this reason, the data taken from the second test are considered to be more accurate, except for some unique IBC input combinations not repeated in the second test. IBC controls that were evaluated were single-frequency inputs from 2/rev to 6/rev and multi-harmonic combinations of these frequencies to form pulses, wavelets, and doublets. Extensive data were acquired for each IBC data point. These data included rotor performance, average and time-varying hub loads, rotor blade bending loads, control system loads, inboard and outboard blade pitch motions, and BVI noise data. The rotor balance hub force and moment data included the mean values, half-peak-to-peak values, and sine/cosine harmonics up to the 20th rotor harmonic. The time history and averaged Fourier spectrum for each measurement are available electronically from NASA Ames Research Center. The data indicate that significant reduction in both BVI noise and hub vibration can be obtained using IBC. The 2/rev input produced the best single-frequency results. At a typical descent flight condition, 2/rev IBC combined with other IBC harmonics reduced the BVI noise up to 12 dB (85 percent) at some microphone locations. At the same time, this input could also reduce the dominant 4/rev vibratory hub loads by up to 75 percent. The data also show that performance improvements of up to 7 percent were obtained using 2/rev IBC at high-speed forward flight conditions. An analysis of the hydraulic power requirements for BVI noise suppression, vibration reduction, and rotor performance improvement is included in this report. This analysis shows that the power required by the IBC system is negligible at low-speed flight conditions, and that a net gain of 3 percent of rotor horsepower can be achieved at high-speed flight conditions.

Investigation↗

DOE OSTI2019

Experimental Evidence that Stochasticity Contributes to Bacterial Composition and Functioning in a Decomposer Community

ABSTRACT Stochasticity emerging from random differences in replication, death, mutation, and dispersal is thought to alter the composition of ecological communities. However, the importance of stochastic effects remains somewhat speculative because stochasticity is not directly measured but is instead inferred from unexplained variations in beta-diversity. Here, we performed a field experiment to more directly disentangle the role of stochastic processes, environmental selection, and dispersal in the composition and functioning of a natural bacterial decomposer community in the field. To increase our ability to detect these effects, we reduced initial biological and environmental heterogeneity using replicate nylon litterbags in the field. We then applied two treatments: ambient/added precipitation and bacterial and fungal dispersal using “open” litterbags (made from 18.0-μm-pore-size mesh) (“open bacterial dispersal”) versus bacterial and fungal dispersal using “closed” litterbags (made from 22.0-μm-pore-size mesh) (“closed bacterial dispersal”). After 5 months, we assayed composition and functioning by the use of three subsamples from each litterbag to disentangle stochastic effects from residual variation. Our results indicate that stochasticity via ecological drift can contribute to beta-diversity in bacterial communities. However, residual variation, which had previously been included in stochasticity estimates, accounted for more than four times as much variability. At the same time, stochastic effects on beta-diversity were not attenuated at the functional level, as measured by genetic functional potential and extracellular enzyme activity. Finally, dispersal was found to interact with precipitation availability to influence the degree to which stochasticity contributed to functional variation. Together, our results demonstrate that the ability to quantify stochastic processes is key to understanding microbial diversity and its role in ecosystem functioning. IMPORTANCE Randomness can alter the diversity and composition of ecological communities. Such stochasticity may therefore obscure the relationship between the environment and community composition and hinder our ability to predict the relationship between biodiversity and ecosystem functioning. This study investigated the role of stochastic processes, environmental selection, and dispersal in microbial composition and its functioning on an intact field community. To do this, we used a controlled and replicated experiment that was similar to that used to study population genetics in the laboratory. Our study showed that, while the stochastic effects on taxonomic composition are smaller than expected, the degree to which stochasticity contributes to variability in ecosystem processes may be much higher than previously assumed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Methane, ethane, and propane production in Greenland ice core samples and a first isotopic characterization of excess methane

Air trapped in polar ice provides unique records of the past atmospheric composition ranging from key greenhouse gases such as methane (CH 4 ) to short-lived trace gases like ethane (C 2 H 6 ) and propane (C 3 H 8 ). Recently, the comparison of CH 4 records obtained using different extraction methods revealed disagreements in the CH 4 concentration for the last glacial in Greenland ice. Elevated methane levels were detected in dust-rich ice core sections measured discretely, pointing to a process sensitive to the melt extraction technique. To shed light on the underlying mechanism, we performed targeted experiments and analyzed samples for methane and the short-chain alkanes ethane and propane covering the time interval from 12 to 42 kyr. Here, we report our findings of these elevated alkane concentrations, which scale linearly with the amount of mineral dust within the ice samples. The alkane production happens during the melt extraction step of the classic wet-extraction technique and reaches 14 to 91 ppb of CH 4 excess in dusty ice samples. We document for the first time a co-production of excess methane, ethane, and propane, with the observed concentrations for ethane and propane exceeding their past atmospheric background at least by a factor of 10. Independent of the produced amounts, excess alkanes were produced in a fixed molar ratio of approximately 14 : 2 : 1, indicating a shared origin. The measured carbon isotopic signature of excess methane is (-47.0 ± 2.9) ‰ and its deuterium isotopic signature is (-326 ± 57) ‰. With the co-production ratios of excess alkanes and the isotopic composition of excess methane we established a fingerprint that allows us to constrain potential formation processes. This fingerprint is not in line with a microbial origin. Moreover, an adsorption–desorption process of thermogenic gas on dust particles transported to Greenland does not appear very likely. Instead, the alkane pattern appears to be indicative of abiotic decomposition of organic matter as found in soils and plant leaves.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗

NASA NTRS2003

Main Chamber Injectors for Advanced Hydrocarbon Booster Engines

Achieving the highest possible specific impulse has long been a key driver for space launch systems. Recently, more importance has been placed on the need for increased reliability and streamlined launch operations. These general factors along with more specific mission requirements have provided a new focus that is centered on the oxidizer rich staged combustion (ORSC) cycle. Despite a history of use in Russia that extends back to the 1960's, a proven design methodology for ORSC cycle engines does not exist in the West. This lack of design expertise extends to the main chamber injector, a critical subcomponent that largely determines the engine performance and main chamber life. The goals of the effort described here are to establish an empirical knowledge base to provide a fundamental understanding of main chamber injectors and for verification of an injector design methodology for the ORSC cycle. The design of a baseline injector element, derived from information on Russian engines in the open literature, is presented. The baseline injector comprises a gaseous oxidizer core flow and an annular swirling fuel flow. Sets of equations describing the steady-state and the dynamic characteristics of the injector are presented; these equations, which form the basis of the design analysis methodology, will be verified in tests later this year. On-going cold flow studies, using nitrogen and water as simulants, are described which indicate highly atomized and symmetric sprays.

Long, Matthew R.↗

NASA NTRS2014

Exploration of the Trade Space Between Unmanned Aircraft Systems Descent Maneuver Performance and Sense-and-Avoid System Performance Requirements

A need exists to safely integrate Unmanned Aircraft Systems (UAS) into the United States' National Airspace System. Replacing manned aircraft's see-and-avoid capability in the absence of an onboard pilot is one of the key challenges associated with safe integration. Sense-and-avoid (SAA) systems will have to achieve yet-to-be-determined required separation distances for a wide range of encounters. They will also need to account for the maneuver performance of the UAS they are paired with. The work described in this paper is aimed at developing an understanding of the trade space between UAS maneuver performance and SAA system performance requirements, focusing on a descent avoidance maneuver. An assessment of current manned and unmanned aircraft performance was used to establish potential UAS performance test matrix bounds. Then, near-term UAS integration work was used to narrow down the scope. A simulator was developed with sufficient fidelity to assess SAA system performance requirements. The simulator generates closest-point-of-approach (CPA) data from the wide range of UAS performance models maneuvering against a single intruder with various encounter geometries. Initial attempts to model the results made it clear that developing maneuver performance groups is required. Discussion of the performance groups developed and how to know in which group an aircraft belongs for a given flight condition and encounter is included. The groups are airplane, flight condition, and encounter specific, rather than airplane-only specific. Results and methodology for developing UAS maneuver performance requirements are presented for a descent avoidance maneuver. Results for the descent maneuver indicate that a minimum specific excess power magnitude can assure a minimum CPA for a given time-to-go prediction. However, smaller amounts of specific excess power may achieve or exceed the same CPA if the UAS has sufficient speed to trade for altitude. The results of this study will support UAS maneuver performance requirements development for integrating UAS in the NAS. The methods described are being used to help RTCA Special Committee 228 develop requirements.

Jack, Devin P.↗

NASA NTRSDate not supplied

Current Test Capabilities of NASA Langley’s Arc-Heated Scramjet Test Facility

The Arc-Heated Scramjet Test Facility at NASA Langley Research Center has demonstrated itself repeatedly as a key contributor to fundamental hypersonic research and scramjet engine technology development in the Mach 4.7 to 8 range. Since its inception in the 1970’s, the facility has hosted numerous scramjet engine configurations that explored the performance and operability of airframe-integrated hydrogen-fueled scramjet flow path concepts, successfully maturing both analysis tools and design technique and methodology for hypersonic propulsion. Contributions to major programs include extensive tests of the hydrogen-fueled NASP and Hyper-X scramjet engines, and the hydrocarbon-fueled HIFiRE Flight 2 scramjet flow path, in addition to fundamental in-house research. The facility capabilities have expanded to include direct-connect flow path support, gaseous hydrocarbon fuels, dynamic trajectory simulation and extension of flight simulation down to Mach 2.2. The low-Mach upgrades include increased air flow rate and controls capability and two new low-Mach nozzles (Mach 2.2 and 3.5). The facility recently completed verification of the new operational range through successful envelope expansion tests with the Mach 2.2 nozzle. With these new modifications, the operating envelope for the facility is now Mach 2.2 – 8.0(+), with a demonstrated dynamic pressure simulation of 2000 psf at the low end, and margin available for even more. Lastly, a continuous run time of over 5 minutes has been demonstrated, indicating that run time is limited only by test hardware or back-pressure requirements. This paper will provide a detailed description of these previously unreported upgrades and expanded simulation capabilities.

Hypersonic↗

DOE OSTI2023

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this paper, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them with their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find that models with less-complex architectures see the greatest performance improvement with the addition of random wiring (up to 30.4% for multilayer perceptrons). Furthermore, of 24 different model architecture, parameter count, and prediction task combinations, only one had a statistically significant performance deficit in randomly wired networks relative to their standard counterparts, with 14 cases showing statistically significant improvement. We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Neutron irradiation-induced microstructure damage in ultra-high temperature ceramic TiC

Titanium carbide (TiC) is an ultra-high temperature ceramic with potential as a structural material candidate for advanced reactor concepts. However, the irradiation tolerance of TiC is not well understood. Here, we reveal the key irradiation damage microstructure degradation processes in TiC using mixed spectrum neutron irradiations at dose of ~2 displacements per atom (dpa) at temperatures of ~220, 620, and 1115 °C, combined with state-of-art microstructure characterization using transmission electron microscopy (TEM) and high-resolution TEM (HRTEM). The helium injection (~65 atomic parts per million) produced by neutron transmutation also occurred in the sample. TiC was observed to form irradiation-induced interstitial-type dislocation loops and He-stabilized cavities. At 220 and 620 °C, the analysis of the electron diffraction patterns, rel-rod imaging and HRTEM revealed that the dislocation loops were faulted Frank loops with Burgers vector b Frank = 1/3<111> lying on {111} planes. A detailed Burgers vector identification performed by the g.b technique revealed that the dislocation loops forming at 1115 °C were unfaulted, edge-type, with Burgers vectors b = a<100> and a/2<110> with corresponding {100} and {110} habit planes. Using continuum mechanics, we estimated the critical radius at which a faulted dislocation loop transitions to a perfect loop to be 9 nm. Further, no amorphization occurred in TiC under-investigated irradiation conditions while macroscopic swelling under point defect swelling regime was observed. He-stabilized cavities were detected at 1115 °C, but not at lower temperature irradiation. These observations indicate the onset of long-range vacancy migration in TiC occurs between 620 and 1115 °C.

36 MATERIALS SCIENCE↗