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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analysis of Vector Particle-In-Cell (VPIC) memory usage optimizations on cutting-edge computer architectures

Vector Particle-In-Cell (VPIC) is one of the fastest plasma simulation codes in the world, with particle numbers ranging from one trillion on the first petascale system, Roadrunner, to ten trillion particles on the more recent Blue Waters supercomputer. As supercomputers continue to grow rapidly in size, so too does the gap between computing capability and memory capability. Current memory systems limit VPIC simulations greatly as the maximum number of particles that can be simulated directly depends on the available memory. In this study, we present a suite of VPIC memory optimizations (i.e., particle weight, half-precision, and fixed-point optimizations) that enable a significant increase in the number of particles in VPIC simulations. Here, we assess the optimizations’ impact on memory and runtime performance for a suite of cutting-edge computer architectures such has the NVIDIA V100 GPU, the IBM Power9, and the Fujitsu A64FX architectures. Our optimizations enable a 31.25% reduction in memory usage and up to 40% increase in the number of particles. This paper extends our work on developing particle storage format optimizations Tan et al.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core

In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin-spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on the graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. Here we show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS that can improve the performance by 26% ~ 33% compared to implementation based on cuBLAS. Furthermore, we perform the on-the-fly calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes the large-scale atomistic spin dynamics simulation feasible and affordable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO 2 , and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO 2 , and soil moisture) on canopy processes (i.e. net CO 2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO 2 assimilation and increased air temperature. Net CO 2 assimilation increased at higher atmospheric CO 2 , while decreasing soil moisture resulted in lower leaf water potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2020

A discrete integral transform for rapid spectral synthesis

Accurate synthetic spectra that rely on large Line-By-Line (LBL)-databases are used in a wide range of applications such as high temperature combustion, atmospheric re-entry, planetary surveillance and laboratory plasmas. Conventionally synthetic spectra are calculated by computing a lineshape for every spectral line in the database and adding those together, which may take multiple hours for large databases. In this paper we propose a new approach for spectral synthesis based on an integral transform: the synthetic spectrum is calculated as the integral over the product of a Voigt profile and a newly proposed three-dimensional “lineshape distribution function”, which is a function of spectral position and Gaussian- & Lorentzian width coordinates. A fast discrete version of this transform based on the Fast Fourier Transform (FFT) is proposed, which improves performance compared to the conventional approach by several orders of magnitude while maintaining accuracy. Strategies that minimize the discretization error are discussed. A Python implementation of the method is compared against state-of-the-art spectral code RADIS, and is since adopted as RADIS's default synthesis method. The synthesis of a benchmark CO2 spectrum consisting of 1.8 M spectral lines and 200k spectral points took only 3.1 s using the proposed method (1011 lines × spectral points/s), a factor ~300 improvement over the state-of-the-art, with the relative improvement generally increasing for higher number of lines and/or number of spectral points. Finally, an experimental GPU-implementation of the method was also benchmarked, which demonstrated another 2~3 orders performance increase, achieving up to 5 ∙ 10 14 lines × spectral points/s.

42 ENGINEERING↗
DOE OSTI2025

Direct sensitivity analysis on the parameterization of crystal plasticity models

Various methods for calibrating crystal plasticity finite element (CPFE) models lead to non-unique input parameter values, which subsequently introduce uncertainty in the predicted mechanical response. Sensitivity analysis (SA) conducted on crystal plasticity models is used to identify how variability in these parameters contribute to output uncertainty. Traditional SA on CPFE parameters uses simplified surrogate models to save computational time. However, the accuracy of the surrogate models depends on the quantity of training data used, and any modeling error can propagate into the SA results, potentially affecting their reliability. In this work, the elementary effects test (EET) method, a global SA technique using direct CPFE simulations was employed, and the results obtained were compared with the First Order Second Moment (FOSM) method. ExaConstit, an open-source GPU-enabled CPFE code, was used to perform the simulations and direct SA. The EET method was accurately able to capture the non-linear effects of all the input parameters on the output and is a valuable approach for reliably attributing parameter sensitivities in CPFE models. Based on the results, efficient strategies to perform future parameter calibration and SA are discussed. Additionally, the SA trends observed in different single crystal orientations closely mirrored the activity of the slip systems.

Elementary Effects Test↗
DOE OSTI2021

Ammonia separation from N 2 and H 2 over LTA zeolitic imidazolate framework membranes

LTA type zeolitic imidazolate framework membranes denoted as ZIF-21 were synthesized and evaluated for the separation of a commodity chemical, ammonia, from nitrogen and hydrogen. Membranes were grown using secondary seeded growth. Ammonia permeances were as high as 1727 GPU, with NH 3 /N 2 , and NH 3 /H 2 ideal selectivities of 35, and 12 respectively. ZIF-21 membranes were also evaluated for gas mixtures composed of 13.8:86.2 NH 3 /N 2 and NH 3 /H 2 , with mixture separation selectivities as high as 7.6 for NH 3 /N 2 . Differences in adsorption, and diffusivities were identified as the key separation mechanisms. Specifically, for both gas pairs the polar-polar interaction between ammonia and the linker polar channels, led to the preferential adsorption of ammonia, and therefore favored thermodynamically the separation of ammonia from nitrogen and hydrogen. In addition, for NH 3 /N 2 gas pair, differences in diffusivities was a contributing separation mechanism. For NH 3 /H 2 gas pair, due to the faster hydrogen diffusivity, the kinetic contribution was a strong competing separation mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Enhancing membrane performance for CO 2 capture from flue gas with ultrahigh MW polyvinylamine

Membranes for post-combustion CO 2 capture are required to have a high CO 2 permeance due to the limited driving force. For facilitated transport membranes synthesized with polyvinylamine (PVAm), a defect-free selective layer of <200 nm is usually required to render sufficient permeance with high CO 2 /N 2 selectivity. In order to meet such a demand through the knife-coating process, the coating solution needs to have a high viscosity at a relatively low concentration to minimize its penetration into the substrate. The demand was met by synthesizing PVAm with an ultrahigh molecular weight (MW) via inverse emulsion polymerization (IEP) in this study. Compared to solution polymerization, IEP isolates the reaction in inverse micelles suspended in a continuous organic phase, which allows excellent dissipation of the heat generated by the reaction and reduces gel formation drastically. The polymerization parameters, including monomer concentration, initiator concentration, and reaction temperature, were investigated to obtain the optimal MW of PVAm for membrane performance. As compared to solution polymerization, IEP enhanced the MW of PVAm from 1.2 to 12.7 MDa, which minimized the penetration of the coating solution into the substrate and hence the extra mass transfer resistance. The effect of MW and degree of hydrolysis of PVAm on the membrane transport properties were studied. Furthermore, by employing PVAm with a MW of 12.7 MDa to strengthen the polymer matrix, the loading of piperazine glycinate in the membrane was increased up to 85 wt.%. In conclusion, the resultant membrane achieved a CO 2 permeance of 839 GPU and a CO 2 /N 2 selectivity of 161 at the typical flue gas temperature of 57°C.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Preparation of defect-free asymmetric gas separation membranes with dihydrolevoglucosenone (Cyrene TM ) as a greener polar aprotic solvent

Nonsolvent-induced phase separation (NIPS) is widely used to prepare asymmetric gas separation membranes. Most industrial NIPS casting solution formulations are limited to a small group of glassy polymers and, importantly, require toxic polar aprotic solvents such as N,N-dimethylacetamide (DMAc), N,N-dimethylformamide (DMF), or N-methyl-2-pyrrolidone (NMP). Growing restrictions on the use of such solvents are spurring the search for more benign casting solution formulations that do not compromise membrane performance. Herein this study reports high-flux, defect-free asymmetric polysulfone (PSf) gas separation membranes prepared using dihydrolevoglucosenone (Cyrene TM ), a polar aprotic solvent that is believed to be safer than DMAc, DMF, and NMP, as the majority casting solution component. Optimized formulations and casting conditions produce membranes with hydrogen permeances exceeding 100 gas permeance units (GPU) and selectivities at or above those of dense PSf films. Dry/wet NIPS membrane performance improved with shorter dry step times and increased Cyrene TM loadings relative to the volatile solvent, tetrahydrofuran (THF), in the casting solution. The high water-Cyrene TM Flory-Huggins interaction parameter, $\mathcal{X}12$ , and high casting solution viscosities help suppress the formation of skin layer defects and sublayer macrovoids. In some cases, membrane selectivities were influenced by substructure resistance, providing insight into the relationship between sublayer morphology and membrane performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Thin-film composite membranes based on hyperbranched poly(ethylene oxide) for CO 2 /N 2 separation

Cross-linked amorphous poly(ethylene oxide) (XLPEO) is one of the leading membrane materials for post-combustion CO 2 capture. For example, XLPEO prepared from poly(ethylene glycol) methyl ether acrylate (PEGMEA) exhibited CO 2 permeability of 570 Barrer and CO 2 /N 2 selectivity of 41 at 35 °C. However, these XLPEOs cannot be dissolved in coating solutions, making it impossible to be fabricated into thin-film composite (TFC) membranes using state-of-the-art manufacturing processes. In this study, we synthesized high molecular weight yet soluble HPEO via atom transfer radical polymerization (ATRP). These polymers were thoroughly characterized and compared with XLPEO, including thermal transitions, free volumes, and pure-gas sorption and permeation properties. A polymer with the best combination of CO 2 permeability (540 Barrer) and CO 2 /N 2 selectivity (43) was fabricated into defect-free TFC membranes with a thickness as thin as 506 ± 44 nm. When challenged with simulated flue gas containing water vapor at 35 °C for over 100 h, the membrane shows stable CO 2 permeance of 850 GPU and CO 2 /N 2 selectivity of 37, comparable to the leading commercial membranes for carbon capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bicontinuous substrates with reduced pore restriction for CO 2 -selective composite membranes

A substrate with bicontinuous structure is attractive for thin-film composite (TFC) membrane synthesis due to its high surface porosity and well-interconnected bulk morphology. In this study, the vapor-induced phase separation (VIPS) of the casting solution comprising water, 2-pyrrolidone, and polyethersulfone (PES) was studied. By tuning the water addition in the casting solution and water vapor exposure time, the optimized substrate exhibited a bicontinuous surface with a high porosity of 24.2% (or 216/µm 2 pore density), thereby mitigating pore restriction and showing a high CO 2 permeance of 2.94×10 5 GPU. Compared to the benchmark PES substrate with cellular surface pores and lower gas permeance, the use of the bicontinuous substrate led to a 12% increase in the CO 2 permeance of the TFC membrane. The mitigated pore restriction was further rationalized by a resistance-in-series model. The analysis of the lateral diffusion and substrate resistances indicates that an even higher surface pore density of the substrate is required to fully realize the potential of ultra-permeable, CO 2 -selective polymers in the TFC configuration. As a result, the scalability of the VIPS process was demonstrated by the roll-to-roll fabrication of the optimized bicontinuous substrate with a width of 21" and a length of >100'.

42 ENGINEERING↗
DOE OSTI2022

Polystyrene sulfonate (PSS) stabilized polyethylenimine (PEI) membranes fabricated by spray coating for highly effective CO 2 /N 2 separation

Greenhouse effect, largely caused by CO 2 emission, has become a major concern for global climate change. Post-combustion CO 2 capture is one of the critical strategies to mitigate this issue. Membrane technology for carbon capture has drawn significant attention because of the cost and energy efficiency and scalability. Many membranes for CO 2 capture are limited by the trade-off between CO 2 permeability and CO 2 /N 2 selectivity and the long-term stability under practical operating conditions. Facilitated transport membranes with efficient CO 2 carriers have demonstrated potential to surpass the permeability-selectivity trade-off, but these carriers are often lost under operational conditions. In this work, we designed and fabricated a polystyrene sulfonate (PSS) stabilized polyethylenimine (PEI) membrane by a facile and scalable spray-coating method. The deposited defect-free selective layer, in which the amine carriers in PEI can be stabilized electrostatically by PSS, exhibited superior CO 2 separation performance with good long-term stability under practical operating conditions. The separation performance was optimized by spray-coating cycles, CNT network loading, and PSS loading. Our membrane showed CO 2 permeance ranging from 820 to 1,770 GPU and CO 2 /N 2 selectivity varying from 395 to 460 under vacuum operation mode in the temperature range between 80 and 90 °C. Furthermore, the membrane was successfully scaled up to 200 cm 2 with good uniformity. These results might suggest a novel membrane structure and a scalable approach for fabrication of highly efficient CO 2 separation membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ultrathin polyorganosilica membranes synthesized by oxygen-plasma treatment of polysiloxanes for H 2 /CO 2 separation

Oxygen plasma treatment of polydimethylsiloxane (PDMS) induces an ultrathin polyorganosilica (POSi) layer (<10 nm) on top of a PDMS membrane, leading to excellent H 2 /gas separation properties and providing a rapid and scalable way to fabricate robust silica membranes compared with conventional high-temperature and time-consuming sol-gel methods. Here, we thoroughly investigate POSi membranes derived from poly (dimethylsiloxane-co-methylhydroxidesiloxane) (poly (DMS-co-MHOS)) containing -SiOH groups that can be more easily converted to silica networks than the -SiCH 3 in PDMS. Further, the effect of the polysiloxane structure and plasma treatment conditions (including plasma generating powers, oxygen flowrate, chamber pressure, and treatment time) on the silica chemistry, structure, and H 2 /CO 2 separation properties are systematically determined to derive structure/property relationships. An optimized membrane exhibits H 2 permeance of 880 GPU and H 2 /CO 2 selectivity of 67 at 150 °C, superior to state-of-the-art polymeric membranes. The membrane retains H 2 /CO 2 selectivity as high as 46 when challenged with simulated syngas containing 2.8 mol% water vapor at 150 °C, demonstrating the potential of these POSi membranes for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Disentangling the gap between pure and mixed-gas performance of thin film composite membranes through improved cell design and testing methods

Testing thin film composite (TFC) membrane coupons at low stage-cuts (≤5%) in a sweep-gas permeation system is a common practice to obtain mixed-gas separation properties for benchmarking performance and making scale-up decisions. However, even under these idealized conditions, mixed-gas permeance and selectivity can be more than 30% lower than their pure-gas values, partially due to concentration polarization, an effect that typically intensifies with increased membrane permeance. This study investigates the effect of cell design on mixed-gas testing using PolyActive TM TFC membranes with pure-gas CO 2 permeance of 1700 – 3100 gas permeance unit (GPU), covering the permeance range of most state-of-the-art CO 2 /N 2 separation membranes. Here, we designed and 3D-printed a counter-current permeation cell with enhanced feed and sweep flow efficiency, resulting in a 33 – 41% increase in mixed-gas CO 2 permeance compared to traditional permeation cells. Furthermore, we compared sweep-gas and vacuum permeation methods using traditional permeation cells, revealing that the latter delivers 41% higher mixed-gas CO 2 permeance, because vacuuming effectively minimizes the downstream concentration polarization. These findings highlight the importance of cell design and permeation apparatus selection in lab-scale mixed-gas testing, with strong implications for module design and process optimization at the industrial scale.

mixed gas performance↗
DOE OSTI2023

SPH modeling of biomass granular flow: Theoretical implementation and experimental validation

The commercialization of biomass-derived energy is impeded by flowability challenges arising from the feeding and handling of granular biomass materials in full-scale biorefineries. To overcome these obstacles, a robust and accurate model to simulate the flow of granular biomass is indispensable. However, conventional mesh-based numerical codes are limited by inherent mesh distortion in simulating large deformation that commonly occurs in granular biomass handling. Here, in this study, we propose a graphics processing unit (GPU)-accelerated meshless Smoothed Particle Hydrodynamics (SPH) code to model the flow of granular biomass materials. A modified void ratio-based mass conversation, a hybrid particle-to-particle/surface frictional boundary treatment, and a hypoplastic constitutive model are implemented. Four numerical examples, an elastic block sliding on inclined planes, sand column collapse, Angle of Repose, and axial compression tests for pine chips, were simulated using the developed SPH code. The results demonstrate good agreement between numerical predictions and analytical and experimental data for all four examples, validating the SPH code and increasing confidence that it can be applied to simulate more complex granular biomass handling processes, such as hopper feeding or auger conveyance.

09 BIOMASS FUELS↗
DOE OSTI2024

A High-Performance Discrete-Element Framework for Simulating Flow and Jamming of Moisture Bearing Biomass Feedstocks

We developed and verified a high-performance open-source discrete element method (DEM) solver with simultaneously-supported feedstock-specific interaction models, including bonded-sphere, liquid bridge, cohesion, and non-linear contact models. Our solver uses parallel data structures on hybrid central and graphics processing unit (CPU/GPU) architectures, with favorable strong scaling performance observed for large problem sizes comprised of (100 M particles), and 4X single-node GPU speedup. The particles for corn stover feedstock were conceptualized and calibrated based on experimental measurements and results. Sensitivity analyses demonstrate that the mass flow rate from a wedge hopper is governed primarily by moisture content, friction coefficient, and cohesion energy density. The model is used to reproduce experimentally observed hopper jamming results, highlighting that the experimental no-flow trends can only be achieved by using non-spherical particles, liquid bridge and cohesion models, highlighting the importance of using concurrent feedstock specialized models for the effective representation of biomass material handling problems.

bioenergy↗
DOE OSTI2021

Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

In this paper, we developed a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) simulations for bubbly flows. The proposed approach is supported by high-resolution two-phase flow measurements, including those by double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distributions of key physical quantities of interest (QoIs), including the void fraction and phasic velocities, are obtained to support the Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified while the aleatory uncertainties from stochastic fluctuations of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of the QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of an open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainties of MCFD simulations can be significantly reduced. The proposed approach has the potential for other applications that involve numerical models with empirical parameters.

42 ENGINEERING↗
DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗