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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗
NASA NTRS2019

Modeling of an Atmospheric-Boundary-Layer Profile in Support of Experiments in the NASA Langley Transonic Dynamics Tunnel

This paper presents the results of the Computational Fluid Dynamics (CFD) analyses to model two atmospheric-boundary-layer (ABL) profiles inside the NASA Langley Research Center Transonic Dynamics Tunnel (TDT). The CFD models include tunnel walls and all additional hardware needed to simulate the ABL profiles. This hardware includes Irwin spires and floor-mounted roughness elements. The numerical simulations show that the application of higher-fidelity numerical methods is necessary to compute boundary-layer and turbulenceintensity profiles that match experimental data. The ABL profiles are computed both inside the numerical model of the TDT and in a classical free-air model. Both Unsteady Reynolds Averaged Navier-Stokes (URANS) with Spalart-Allmaras (SA) turbulence model and Modified Delayed Detached Eddy (MDDES) simulation methods are used. The results show that the MDDES-simulation results match the experimental data very well while URANS-SA does not. The results also show that the wind-tunnel walls have a significant effect on ABL prediction.

Chwalowski, Pawel↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗
NASA NTRS2015

Comparison of Instantaneous Frequency Scaling from Rain Attenuation and Optical Disdrometer Measurements at K/Q bands

Rain attenuation is strongly dependent on the rain rate, but also on the rain drop size distribution (DSD). Typically, models utilize an average drop size distribution, such as those developed by Laws and Parsons, or Marshall and Palmer. However, individual rain events may possess drop size distributions which could be significantly different from the average and will impact, for example, fade mitigation techniques which utilize channel performance estimates from a signal at a different frequency. Therefore, a good understanding of the characteristics and variability of the raindrop size distribution is extremely important in predicting rain attenuation and instantaneous frequency scaling parameters on an event-toevent basis. Since June 2014, NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have measured the attenuation due to rain in Milan, Italy, on the 20/40 GHz beacon signal broadcast from the Alphasat TDP#5 Aldo Paraboni Q/V-band Payload. Concomitant with these measurements are the measurements of drop size distribution and rain rate utilizing a Thies Clima laser precipitation monitor (disdrometer). In this paper, we discuss the comparison of the predicted rain attenuation at 20 and 40 GHz derived from the drop size distribution data with the measured rain attenuation. The results are compared on statistical and real-time bases. We will investigate the performance of the rain attenuation model, instantaneous frequency scaling, and the distribution of the scaling factor. Further, seasonal rain characteristics will be analysed.

millimeter wave↗
NASA NTRS2023

Implementation, Realization and an Effective Solver of Two-Equation Turbulence Models

Currently, when the Reynolds-Averaged Navier-Stokes (RANS) equations are solved using turbulence modeling, most often the one-equation model of Spalart and Allmaras is used. Then, it is only necessary to solve the RANS equations in conjunction with a single transport equation for modeling turbulence. For this model, considerable assessment and analysis has been performed, allowing the possibility of a reliable solution method for an eddy viscosity required to compute the Reynolds stresses in the RANS equations. Such evaluation along with analysis has not been achieved to realize similar performance with two-equation models of the k-w type. The primary objective of this paper is to present and discuss the components of an effective numerical algorithm for solving the RANS equations and the two transport equations of k-w type turbulence models. All the important details of the turbulence model as actually implemented are given, which is sometimes not done in various papers considering such modeling. The viability and effectiveness of this solution algorithm are demonstrated by solving both two-dimensional and three-dimensional aerodynamic flows. In all applications, a linear rate of convergence without oscillations or other evidence of unstable behavior is observed. This behavior is also particularly true when the proposed algorithm is applied to systematically renewed mesh sequences, which is generally not observed with algorithms solving more than one transport equation. Thus, numerical integration errors are systematically reduced, allowing for a significantly more reliable assessment of the effectiveness of the turbulence model. Additionally, in this paper, analysis of the solution algorithm, including linear stability, is also performed for a particular flow problem.

Turbulence↗
DOE OSTI2022

Modeling Yield, Biogenic Emissions, and Carbon Sequestration in Southeastern Cropping Systems With Winter Carinata

Sustainable aviation fuel (SAF) production from lipids is a technologically mature approach for replacing conventional fossil fuel use in the aviation sector, and there is increasing demand for such feedstocks. The oilseed Brassica carinata (known as Ethiopian mustard or simply carinata) is a promising SAF feedstock that can be grown as a supplemental cash crop over the winter fallow season of various annual crop rotations in the Southeast US, avoiding land use changes and potentially achieving some of the soil carbon sequestration and ecosystem service benefits of winter cover crops. However, carinata may require more intensive management than traditional cover crops, potentially leading to additional soil greenhouse gas (GHG) emissions through increased carbon losses from soil tillage and nitrous oxide (N 2 O) emissions from nitrogen fertilizer application. In this work, the 2017 version of the process-based DayCent ecosystem model was used to establish initial expectations for the total regional SAF production potential and associated soil GHG emissions when carinata is integrated as a winter crop into the existing crop rotations across its current suitability range in southern Alabama, southern Georgia, and northern Florida. Using data from academic and industry carinata field trials in the region, DayCent was calibrated to reproduce carinata yield, nitrogen response, harvest index, and biomass carbon-to-nitrogen ratio. The resulting model was then used to simulate the integration of carinata every third winter across all 2.1 Mha of actively cultivated cropland in the study area. The model predicted regional average yields of 2.9–3.0 Mg carinata seed per hectare depending on crop management assumptions. That results in the production of more than two million Mg of carinata seed annually across the study area, enough to supply approximately one billion liters of SAF. Conventional management of carinata led to only modest increases in soil carbon storage that were largely offset by additional N2O emissions. Climate-smart management via adopting no-till carinata establishment or using poultry litter as a nitrogen source resulted in a substantial net soil GHG sink (0.23–0.31 Mg CO 2 e ha -1 y -1 , or 0.24–0.32 Mg CO2e per Mg of seed produced) at the farms where carinata is cultivated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Accuracy of the time-averaged ponderomotive approximation for laser-plasma accelerator modeling

Reliable modeling of laser-plasma accelerators, where a short and intense laser pulse propagates in an underdense plasma over long distances, is a computationally challenging task. This is due to the great disparity among the scales involved in the modeling, ranging from the micrometer scale of the laser wavelength to, for instance, the meter scale of the laser-plasma interaction length for a multi-GeV-class laser-plasma accelerator. To reduce such imbalance, the time-averaged ponderomotive approximation may be used, where the plasma particle dynamics is analytically averaged over the laser frequency, and only spatiotemporal scales associated with the laser envelope are retained in the calculations, resulting in significant computational savings. Here, we characterize the accuracy and robustness of the time-averaged ponderomotive approximation for a range of laser parameters of interest for present and future laser-plasma accelerators, and we show that the error introduced by the averaging process is small in all relevant cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Distance‐based reconstruction of protein quaternary structures from inter‐chain contacts

Abstract Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Non-singlet quark helicity $\mathrm{PDF}$s of the nucleon from pseudo-distributions

The non-singlet helicity quark parton distribution functions (PDFs) of the nucleon are determined from lattice QCD, by jointly leveraging pseudo-distributions and the distillation spatial smearing paradigm. A Lorentz decomposition of appropriately isolated space-like matrix elements reveals pseudo-distributions that contain information on the leading-twist helicity PDFs, as well as an invariant amplitude that induces an additional z 2 contamination of the leading-twist signal. An analysis of the short-distance behavior of the space-like matrix elements using matching coefficients computed to next-to-leading order (NLO) exposes the desired PDF up to this additional z 2 contamination. Due to the non-conservation of the axial current, we elect to isolate the helicity PDFs normalized by the nucleon axial charge at the same scale μ 2 . The leading-twist helicity PDFs as well as several sources of systematic error, including higher-twist effects, discretization errors, and the aforementioned z 2 contaminating amplitude are jointly determined by characterizing the computed pseudo-distribution in a basis of Jacobi polynomials. The Akaike Information Criterion is exploited to effectively average over distinct model parameterizations and cuts on the pseudo-distribution. Encouraging agreement is observed with recent global analyses of each non-singlet quark helicity PDF, notably a rather small non-singlet anti-quark helicity PDF for all quark momentum fractions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Assessing free tropospheric quasi-equilibrium for different GCM resolutions using a cloud-resolving model simulation of tropical convection

Abstract This study examines the free-tropospheric quasi-equilibrium at different global climate model (GCM) resolutions using the simulation of tropical convection by a cloud-resolving model during the Tropical Western Pacific International Cloud Experiment. The simulated dynamic and thermodynamic fields within the model domain are averaged over subdomains of different sizes equivalent to different GCM resolutions. These coarse-grained fields are then used to compute CAPE and its change with time, and their relationships with simulated convection. Results show that CAPE change with time is controlled predominantly by variations of thermodynamic properties in the planetary boundary layer for all subdomain sizes ranging from 64 to 4 km. Lag correlation analysis shows that CAPE generation by the free-tropospheric dynamical advection (dCAPE ls ) leads convective precipitation but is in phase with convective mass flux at 600 mb and 500 mb vertical velocity for all subdomain sizes. However, the correlation coefficients and regression slopes decrease as the subdomain size decreases for subdomain sizes smaller than 16 km. This is probably due to increased randomness of convection and more scale-dependence of the relationships when the subdomain size reaches the grey zone. By examining the sensitivity of the relationships of convection with dCAPE ls to temporal scales in different subdomain size, it shows that the quasi-equilibrium between dCAPE ls and convection holds well for timescales of 30 min or longer at all subdomain sizes. These results suggest that the free tropospheric quasi-equilibrium assumption may still be useable even for GCM resolutions in the grey zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Divergent responses of spring phenology to daytime and nighttime warming

Spring phenology (i.e., start of season, SOS) has shifted earlier in response to warmer temperatures. However, the respective influences of daytime and nighttime temperatures on SOS changes remain poorly understood. Here we characterized the responses of satellite-derived SOS to minimum temperature (Tmin) and maximum temperature (Tmax) across Appalachian Trail regions in the Eastern United States during 2000-2013. We found SOS responded differently to Tmin and Tmax at 81.5% of the study area. Furthermore, the SOS responses to Tmin and Tmax both changed across space, and specifically, these two responses showed significant divergent trends from cold to warm regions (P<0.001). We propose a new framework utilizing both Tmin and Tmax, instead of daily average temperature, in modeling phenology. This study, for the first time, disentangled phenological responses to daytime temperature from nighttime temperature across a wide range of temperatures. The findings suggest that model projections of future phenological changes should incorporate the divergent phenology responses to daytime and nighttime temperatures, in order to improve the representation of land-atmosphere interactions in Earth system models in light of asymmetric diurnal warming.

asymmetric warming, temperature sensitivity, Appal↗
DOE OSTI2020

Multimodal imaging and machine learning to enhance microscope images of shale

A machine learning based image processing workflow is presented to enhance shale source rock microscopic images obtained using diverse imaging platforms. Images were acquired from a 30 μm diameter cylindrical Vaca Muerta shale sample using both nondestructive Transmission X-Ray Microscopy (TXM, alternately referred to as nano computed tomography) and destructive Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM). Output cross-sectional images from each modality were aligned using a combination of manual and automated registration techniques to create a registered image dataset. We then apply this dataset for two image processing tasks: prediction of image cross sections with SEM-like resolution from nondestructive TXM data and repair of charged region artifacts (localized accumulation of electrons) within SEM images. The image processing algorithms for both tasks use deep learning models, specifically image-to-image Convolutional Neural Networks (CNNs) and conditional Generative Adversarial Networks (cGANs). In the image enhancement tasks, we are able to achieve significant qualitative and quantitative improvement in TXM images. Here, the best model reaches an average Peak Signal to Noise Ratio (PSNR) of 15.8 dB. Conditioning on TXM data is also shown to reduce artifacts from SEM charging, achieving an average PSNR of 25.8 dB. Furthermore, our results suggest that properly trained and validated networks are capable of significant enhancement of images obtained using nondestructive techniques, thereby improving interpretation of two- and three-dimensional images while preserving samples for future use.

58 GEOSCIENCES↗
DOE OSTI2023

Emergent Constrained Projections of Mean and Extreme Warming in China

Reliable regional temperature projections including heat extremes are essential for climate change adaptation and mitigation. Taking China as an example, simple averages from Coupled Model Intercomparison Project Phase 6 (CMIP6) models project high warming due to sampling many high climate sensitivities in the ensemble. Here, we develop an emergent constraint (EC) framework to obtain constrained mean and daily maximum temperature (TXx) warming over China by using observed global warming and local residual warming. The constrained annual mean and TXx warming over China (2.33°C [1.61–3.05°C] and 2.31°C [1.21–2.99°C]) are 0.65°C [0.04–1.76°C] and 0.63°C [–0.50–2.39°C], respectively, lower than raw projections (2.98°C [1.85–4.22°C] and 2.94°C [2.04–4.39°C]) for 2080–2099 under the intermediate-emission scenario. Approximately half model uncertainty is reduced after constraint. The land area (population) experiencing temperature extremes in our metric is 78% (85%) of the raw projections. Our results imply a lower impact of extreme heat than implied by current raw CMIP6 projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Alternating Conditional Expectations: Introducing a Non‐Parametric Statistical Method to Interpret Long‐Term Greenhouse Gas Flux Measurements Over Semi‐Arid and Wetland Ecosystems

Abstract We explore the potential of using a non‐parametric statistical method called Alternating Conditional Expectations, ACE, to quantify functional relationships in biogeosciences. Here, ACE is used to quantify the non‐linear and multi‐faceted responses of greenhouse gas fluxes to a set of biophysical forcings, when the shapes of those response surfaces are unknown. We evaluated the statistical method over two contrasting ecosystems and two contrasting time steps. One case involved quantifying the biophysical controls of water vapor and carbon dioxide (CO 2 ) fluxes over a semi‐arid oak savanna using daily integrated fluxes. The other case evaluated the responses of CO 2 and methane (CH 4 ) flux measurements to a set of biophysical forcings at a restored tidal wetland using thirty‐minute averages. The statistical model, based on 4 independent variables, explained up over 90% of the variation in daily integrated flux densities of water vapor and net carbon dioxide exchange at the savanna site. This fit was defined by distinct non‐linear responses to such drivers as gross primary production, photosynthetically active radiation, air temperature, vapor pressure deficit and soil moisture. At the tidal wetland site, we evaluated net carbon dioxide and methane fluxes with short‐term measurements to capture the influence of rising and falling tides and seasonality in biological activity. The statistical model defined the shape of the forcing of fluxes due to the roles of carbon exudates, water table depth, oxygen level in the water column, temperature and vegetation status. The statistical fits of the greenhouse gas fluxes were less precise than the savanna case. The fetch varies on a run‐to‐run basis as it is comprised of a heterogeneous mosaic of open water and vegetation. Furthermore, it is difficult to monitor the environmental conditions of the archaea and bacteria in the sediments that produce methane and carbon dioxide.

Environmental Sciences & Ecology↗
DOE OSTI2025

The polarized-signal density matrix: A practical way to recover molecular frame information from isotropic samples

We present a novel approach to model ultrafast time-dependent nonlinear optical polarization sensitive signals emitted from randomly oriented molecules. By projecting the laboratory-frame analyzer polarization axis into the molecular-frame and linking that axis with the density matrix through a tensor product, we demonstrate an approach to find a specific molecular orientation that yields a good approximation to simulated four-wave mixing signals produced by the same model but with averaging over molecular orientation.

Thurston, Richard L↗
DOE OSTI2026

Extended Rice–Thomson analysis and atomistic simulations revealing grain boundary effects on fracture in refractory high-entropy alloys

Significance This work serves to extend the fundamental ductile vs. brittle fracture theory, specifically the Rice–Thomson criterion, by introducing a grain boundary ahead of an initiating crack which propagates at an oblique angle to impinge the boundary. Atomistic fracture simulations on two refractory complex concentrated alloys, the brittle NbMoTaW and the ductile Nb 45 Ta 25 Ti 15 Hf 15 , demonstrate qualitative correspondence with the extended Rice–Thomson criterion and experimental observations. Abstract Understanding how grain boundaries mediate fracture remains a critical challenge in designing ductile, high-performance refractory alloys. Here, we extend the Rice–Thomson criterion to account for the angle between cracks and the impinging grain boundaries (GBs), capturing the competition between intergranular fracture and dislocation-mediated plasticity. Using machine learning interatomic potentials, we performed molecular statics simulations to probe fracture mechanisms in nanocrystalline NbMoTaW and Nb 45 Ta 25 Ti 15 Hf 15 , each with two different grain sizes, revealing trends consistent with experimental observations and the extended Rice model. Comparison with averaged R-curves for bulk samples demonstrates that GBs enhance ductility in Nb 45 Ta 25 Ti 15 Hf 15 in both grain sizes investigated. In contrast, GBs only locally improve fracture resistance in NbMoTaW when cracks are temporarily pinned at GBs inclined at high angles from the crack, but generally promote brittle intergranular fracture. These contrasting behaviors are attributed to differences in GB cohesion, reflecting clear alloying trends that align with ab-initio calculations and trends observed experimentally. Our results bridge classical fracture theory, atomistic simulations, and experimental observations, providing a comprehensive understanding of the fracture mechanisms in nanocrystalline refractory complex concentrated alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Coupled Monte Carlo Transport and Conjugate Heat Transfer for Wire-Wrapped Bundles Within the MOOSE Framework

Cardinal is an open-source application that couples OpenMC Monte Carlo transport and NekRS computational fluid dynamics (CFD) to the Multiphysics Object-Oriented Simulation Environment (MOOSE), closing neutronics and thermal-fluid gaps in conducting high-resolution multiscale and multiphysics analyses of nuclear systems. We first provide a brief introduction to Cardinal's software design, data mapping, and coupling strategy to highlight our approach to overcoming common challenges in high-fidelity multiphysics simulations. Here we then present two Cardinal simulations for hexagonal pin bundles. The first is a validation of Cardinal's conjugate heat transfer coupling of NekRS's Reynolds-Averaged Navier Stokes model with MOOSE's heat conduction physics for a bare seven-pin Freon-12 bundle flow experiment. Predictions for pin surface temperatures under three different heating modes agree reasonably well with experimental data and similar CFD modeling from the literature. The second simulation is a multiphysics coupling of OpenMC, NekRS, and BISON for a reduced-scale, seven-pin wire-wrapped version of an Advanced Burner Reactor bundle. Wire wraps are approximated using a momentum source model, and coupled predictions are provided for velocity, temperature, and power distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Data-driven profile prediction for DIII-D

A new, fully data-driven algorithm has been developed that uses neural networks to predict plasma pro les on a scale of τ E into the future given actuators and the present plasma state. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model is accurate on average, with q predictions the worst and pressure predictions the best. The model can run in milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It also is a candidate for doing phase-space exploration without going through the DIIID database or complicated and computationally expensive simulation codes. Here, a reduced model using only realtime diagnostics has also been developed and formed the basis for a model-predictive control algorithm implemented and successfully tested on DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗