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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

RNG in turbulence and modeling of bypass transition

Two projects are considered: the Renormalization Group (RNG) analysis of turbulence modeling, and the calculation of bypass transition through turbulence modeling. RNG is a process which eliminates small scales on the uneliminated large scales as the change in the transport properties. It is because of this property of RNG that it was previously suggested that RNG could be used as a model builder in turbulence modeling. The possibility is studied of constructing RNG based turbulence models, and to try to proceed to do the modeling through RNG in parallel with the classical approach. The numerical predictions made by RNG models and by classical models is compared against data from Direct Numerical Simulation. While in an environment with freestream turbulence, the transition initiated by the instability of the laminar boundary layer to Tollmien-Schlichting waves is found to be a bypass one in which turbulent spots are formed without T-S wave amplification. The formation is a random process, and flow within a turbulent spot is almost fully turbulent. This suggests the possibility of using turbulence modeling to describe and predict the bypass transition.

Yang, Zhigang↗
NASA NTRS1991

An analysis of RNG based turbulence models for homogeneous shear flow

In a recent paper, the authors compared the performance of a variety of turbulence models including the k-epsilon model and the second-order closure model based on Renormalization Group (RNG) Methods. The performance of these RNG models in homogeneous turbulent shear flow was found to be quite poor, apparently due to the value of the constant C(sub epsilon1) in the modeled dissipation rate equation which was substantially lower than its traditional value. However, recently a correction has been made in the RNG based calculation of C(sub epsilon1). It is shown that with the new value of C(sub epsilon1), the performance of the RNG k-epsilon model is substantially improved. On the other hand, while the predictions of the revised RNG second-order closure model are better, some lingering problems still remain which can be easily remedied by the addition of higher order terms.

Speziale, Charles G.↗
NASA NTRS1991

An analysis of RNG-based turbulence models for homogeneous shear flow

In a recent paper, the authors compared the performance of a variety of turbulence models including the k-epsilon model and the second-order closure model based on Renormalization Group (RNG) Methods. The performance of these RNG models in homogeneous turbulent shear flow was found to be quite poor, apparently due to the value of the constant C(sub epsilon 1) in the modeled dissipation rate equation which was substantially lower than its traditional value. However, recently a correction has been made in the RNG-based calculation of C(sub epsilon 1). It is shown that with the new value of C(sub epsilon 1), the performance of the RNG k-epsilon model is substantially improved. On the other hand, while the predictions of the revised RNG second-order closure model are better, some lingering problems still remain which can be easily remedied by the addition of higher order terms.

Speziale, Charles G.↗
NASA NTRS1992

Analysis of an RNG based turbulence model for separated flows

A two-equation turbulence model of the K-epsilon type was recently derived by using Renormalization Group (RNG) methods. It was later reported that this RNG based model yields substantially better predictions than the standard K-epsilon model for turbulent flow over a backward facing step - a standard test case used to benchmark the performance of turbulence models in separated flows. The improvements obtained from the RNG K-epsilon model were attributed to the better treatment of near wall turbulence effects. In contrast to these earlier claims, it is shown in this paper that the original version of the RNG K-epsilon model substantially underpredicts the reattachment point in the backstep problem. This is a deficiency that is traced to the modeling of the production of dissipation term. However, with the most recent improvements in the RNG K-epsilon model, excellent results for the backstep problem are now obtained.

Speziale, C. G.↗
NASA NTRS1991

An algebraic RNG-based turbulence model for three-dimensional turbomachinery flows

An algebraic eddy viscosity turbulence model based on Renormalization Group (RNG) theory for complex three-dimensional turbomachinery flows is presented. Modifications are made to the baseline RNG model for wakes and separated flows. The model has several advantages over popular algebraic models most notably its lack of empirically determined coefficients. The model is used to compute the mean flow in a low speed axial compressor rotor. The agreement with blade boundary layer and radial flow experimental data is very good and shows improvement over the Baldwin-Lomax model. The development of the tip leakage vortex is also well predicted. The computed wake decay also compares favorably with recent experimental data.

Kirtley, K. R.↗
NASA NTRS1992

On the RNG theory of turbulence

The Yakhot and Orszag (1986) renormalization group (RNG) theory of turbulence has generated a number of scaling law constants in reasonable quantitative agreement with experiments. The theory itself is highly mathematical, and its assumptions and approximations are not easily appreciated. The present paper reviews the RNG theory and recasts it in more conventional terms using a distinctly different viewpoint. A new formulation based on an alternative interpretation of the origin of the random force is presented, showing that the artificially introduced epsilon in the original theory is an adjustable parameter, thus offering a plausible explanation for the remarkable record of quantitative success of the so-called epsilon-expansion procedure.

Lam, S. H.↗
NASA NTRS1993

Development of a recursion RNG-based turbulence model

Reynolds stress closure models based on the recursion renormalization group theory are developed for the prediction of turbulent separated flows. The proposed model uses a finite wavenumber truncation scheme to account for the spectral distribution of energy. In particular, the model incorporates effects of both local and nonlocal interactions. The nonlocal interactions are shown to yield a contribution identical to that from the epsilon-renormalization group (RNG), while the local interactions introduce higher order dispersive effects. A formal analysis of the model is presented and its ability to accurately predict separated flows is analyzed from a combined theoretical and computational stand point. Turbulent flow past a backward facing step is chosen as a test case and the results obtained based on detailed computations demonstrate that the proposed recursion -RNG model with finite cut-off wavenumber can yield very good predictions for the backstep problem.

Zhou, YE↗
NASA NTRS1995

Classical closure theory and Lam's interpretation of epsilon-RNG

Lam's phenomenological epsilon-renormalization group (RNG) model is quite different from the other members of that group. It does not make use of the correspondence principle and the epsilon-expansion procedure. We demonstrate that Lam's epsilon-RNG model is essentially the physical space version of the classical closure theory in spectral space and consider the corresponding treatment of the eddy viscosity and energy backscatter.

Zhou, YE↗
NASA NTRS1990

Application of the algebraic RNG model for transition simulation

The algebraic form of the RNG model of Yakhot and Orszag (1986) is investigated as a transition model for the Reynolds averaged boundary layer equations. It is found that the cubic equation for the eddy viscosity contains both a jump discontinuity and one spurious root. A yet unpublished transformation to a quartic equation is shown to remove the numerical difficulties associated with the discontinuity, but only at the expense of merging both the physical and spurious root of the cubic. Jumps between the branches of the resulting multiple-valued solution are found to lead to oscillations in flat plate transition calculations. Aside from the oscillations, the transition behavior is qualitatively correct.

Lund, Thomas S.↗
NASA NTRS1992

Application of algebraic-RNG eddy viscosity model to simulation of transitional boundary layer flow

An algebraic eddy-viscosity model is derived from the renormalization group (RNG) theory of turbulence. A new length scale, based on boundary layer characteristics (displacement thickness, shape factor), is proposed. The model was applied to transitional boundary layer flow over a flat plate. Integral characteristics, such as the total skin friction coefficient, and mean velocity profile across the boundary layer, are found to be in good agreement with experimental data.

Yakhot, Alexander↗
DOE OSTI2025

Accelerating Neutrino Event Generation in MARLEY Using CUDA-Based RNG and GPU Parallelization

MARLEY is a simulation tool that helps scientists study how low-energy neutrinos interact with matter. To work properly, MARLEY uses random numbers thousands of times in each simulation. These random numbers are important for modeling things like how neutrinos collide with atoms and what particles they produce. Right now, MARLEY runs on a regular computer processor (CPU) and uses a built-in random number generator called the Mersenne Twister. This setup works, but it can be slow, especially when trying to simulate many events. This research focuses on making MARLEY run faster by moving the random number generation and some of the repetitive calculations from the CPU to a graphics processing unit (GPU), which can handle many tasks at the same time. We use CUDA (a tool for programming NVIDIA GPUs) and cuRAND (a GPU-based random number library) to test faster alternatives to the current random number system. We compare different GPU-based generators, like curand_mtgp32, xorwow, and philox, to see which ones are the quickest and still give reliable results. Early tests show that using the GPU can make MARLEY simulations much faster. This project not only helps improve current simulation performance but also moves closer to a full simulation chain where all stages can run on modern GPU hardware.

Dunkley, Kimieka [Florida A-M]↗
NASA NTRS1992

Analysis of an RNG based turbulence model for separated flows

It is shown in this paper that the original version of the renormalization group K-epsilon model substantially underpredicts the reattachment point in the backstep problem - a deficiency that is traced to the modeling of the production of dissipation term. However, with recent improvements in the renormalization group K-epsilon model proposed by Yakhot et al. (1986) improved results for the backstep problem are obtained. These results are not that sensitive to the details of the near wall treatment.

Speziale, C. G.↗
DOE OSTI2025

Policy support and technology development trajectory for renewable natural gas in the U.S.

Renewable natural gas (RNG) is a clean alternative to fossil natural gas, which can be used as transportation fuel, among other applications. This study projects the development trajectory of RNG and evaluates its impacts on the future U.S. transportation market using a hybrid computable general equilibrium model. This analysis considers various factors and uncertainties affecting RNG production, such as technology development, market conditions, competition with other advanced biofuels, and national and state policies. In 2050, RNG production will grow to 2.7 billion gallons (10 billion liters), mostly from swine manure, under current policy provisions. This will lead to a reduction in greenhouse gas (GHG) emissions by 58.56 million metric tonne of CO 2e in 2050. Analysis of different technology cases finds RNG from animal manure to be predominant, while RNG from corn stover and cellulosic ethanol are less competitive. Furthermore, a high mandatory target of 1 billion gallons will drive RNG production higher by 8–18 %, while an extended 2 nd -generation biofuel production tax credit will mostly increase cellulosic ethanol production. The model also finds RNG production being affected by uncertainties in market conditions, such as GDP growth, fossil fuel prices, and oil and gas supply.

Biomethane↗
DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗
DOE OSTI2025

Life Cycle Greenhouse Gas Emissions of Biogas Upgrading for Fuel Production

Waste-to-Renewable Natural Gas (RNG) offers a promising solution to alleviating waste management challenges by converting waste into renewable fuels. Here, this process can significantly reduce greenhouse gas (GHG) emissions, as demonstrated through a comprehensive life cycle analysis. Biogas upgrading is essential to enhance the methane concentration, though it could be energy-intensive and susceptible to methane slippage. Four commonly adopted biogas upgrading technologies, including pressure swing adsorption, membrane separation, chemical absorption, and water scrubbing, are considered. Our study evaluates the life cycle GHG emissions of RNG production from major sources of waste in the U.S. including wastewater sludge, food waste, landfill gas, dairy cow manure, and swine manure. Meta-analysis was conducted to assess methane slippage and energy consumption of biogas upgrading and associated GHG emissions, while accounting for potential avoided emissions from conventional waste management, which vary widely (ranging from −481.0 to 101.8 g CO 2 -eq/MJ). Under default upstream assumptions, representative carbon intensity of RNG varies from about −125 g of CO 2 -eq/MJ (dairy cow manure) to about 41 g of CO 2 -eq/MJ (wastewater sludge). We also explored RNG applications in producing hydrogen, ammonia, and compressed/liquefied forms. These findings highlight the potential of RNG and RNG-derived fuels to reduce GHG emissions and bolster the U.S. energy supply.

Biogas upgrades↗
DOE OSTI2025

Techno-economic analysis and life cycle analysis of renewable natural gas production from brewery wastewater via ex-situ methanation processes

As sustainability becomes increasingly critical, the brewing industry encounters challenges in managing high-organic-content wastewater, a promising renewable energy source. This study evaluates the economic feasibility and environmental impacts of converting brewery wastewater into renewable natural gas (RNG) through anaerobic digestion and biogas upgrading, with an emphasis on different hydrogen sources for biomethanation. Four scenarios were analyzed: Scenario 1 uses purchased hydrogen, while Scenarios 2–4 use renewable hydrogen from microbial electrolysis cells and water electrolyzers. Results show that capital investments for processing 139 MT/h of wastewater, yielding 208 m 3 /h of RNG, range from 10.6 M to 31.9M USD, with estimated minimum selling prices of RNG between 2.25 and 4.37 USD/m 3 . Life cycle greenhouse gas (GHG) emissions span from -21 to 32g CO 2 -equivalent per MJ of RNG. Further, this study presents key economic and environmental metrics for RNG production from brewery wastewater, offering insights to enhance sustainability in brewery wastewater management.

03 NATURAL GAS↗
NASA NTRS1976

The autocorrelation structure of Tausworthe pseudorandom number generators

An algorithm for determining the autocorrelation structure of a new sequence generated from an original sequence by proper decimation (a shift with each q-th term of the original sequence used) is presented, once autocorrelations for any repeating number sequence are known. The autocorrelation structure of the Tausworthe random number generator (RNG), based on proper decimation by q, is accepted as a RNG structure in which a proper decimation generates a long interval over which autocorrelations are small for all shifts. It is shown that if L bits of an N-bit Tausworthe RNG are L-bit binary numbers, L + 1 levels of correlation will exist, and L levels of correlation will each occur twice per RNG cycle length.

Neuman, F.↗