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At least 109 records · Page 6

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NASA NTRS2017

The Effectiveness of Shrouding on Reducing Meshed Spur Gear Power Loss - Test Results

Gearbox efficiency is reduced at high rotational speeds due to windage drag and viscous effects on rotating, meshed gear components. A goal of NASA aeronautics rotorcraft research is aimed at propulsion technologies that improve efficiency while minimizing vehicle weight. Specifically, reducing power losses to rotorcraft gearboxes would allow gains in areas such as vehicle payload, range, mission type, and fuel consumption. To that end, a gear windage rig has been commissioned at NASA Glenn Research Center to measure windage drag on gears and to test methodologies to mitigate windage power losses. One method used in rotorcraft gearbox design attempts to reduce gear windage power loss by utilizing close clearance walls to enclose the gears in both the axial and radial directions. The close clearance shrouds result in reduced drag on the gear teeth, and reduced power loss. For meshed spur gears, the shrouding takes the form of metal side plates and circumferential metal sectors. Variably positioned axial and radial shrouds are incorporated in the NASA rig to study the effect of shroud clearance on gearbox power loss. A number of researchers have given experimental and analytical results for single spur gears, with and without shrouding. Shrouded meshed spur gear test results are sparse in the literature. Windage tests were run at NASA Glenn using meshed spur gears at four shroud configurations: unshrouded, shrouded (max. axial, max radial), and two intermediate shrouding conditions. Results are compared to available meshed spur gear power loss data analyses as well as single spur gear data/analyses. Recommendations are made for future work.

power loss↗

NASA NTRS2017

Impact Delivery of Reduced Greenhouse Gases on Early Mars

Reducing greenhouse gases are once again the latest trend in finding solutions to the early Mars climate dilemma. In its current form - as proposed by Ramirez et al. [1], later refined by Wordsworth et al. [2], and confirmed by Ramirez [3] - collision induced absorptions between CO2-H2 or CO2-CH4 provide enough extra greenhouse power to raise global mean surface temperatures to the melting point of water provided the atmosphere is thick enough and the reduced gases are abundant enough. To raise surface temperatures significantly by this mechanism, surface pressures must be at least 500 mb and H2 and/or CH4 concentrations must be at or above the several percent level. Both Wordsworth et al. [2] and Ramirez [3] show that the melting point can be reached in atmospheres with 1-2 bars of CO2 and 2-10% H2; smaller concentrations of H2 will suffice if CH4 is also present. If thick weakly reducing atmospheres are the solution to the faint young Sun paradox, then plausible mechanisms must be found to generate and sustain the gases. Possible sources of reducing gases include volcanic outgassing, serpentinization, and impact delivery; sinks include photolyis, oxidation, and escape to space. The viability of the reduced greenhouse hypothesis depends, therefore, on the strength of these sources and sinks.

greenhose gases↗

NASA NTRS2017

The Effectiveness of Shrouding on Reducing Meshed Spur Gear Power Loss Test Results

Gearbox efficiency is reduced at high rotational speeds due to windage drag and viscous effects on rotating, meshed gear components. A goal of NASA aeronautics rotorcraft research is aimed at propulsion technologies that improve efficiency while minimizing vehicle weight. Specifically, reducing power losses to rotorcraft gearboxes would allow gains in areas such as vehicle payload, range, mission type, and fuel consumption. To that end, a gear windage rig has been commissioned at NASA Glenn Research Center to measure windage drag on gears and to test methodologies to mitigate windage power losses. One method used in rotorcraft gearbox design attempts to reduce gear windage power loss by utilizing close clearance walls to enclose the gears in both the axial and radial directions. The close clearance shrouds result in reduced drag on the gear teeth and reduced power loss. For meshed spur gears, the shrouding takes the form of metal side plates and circumferential metal sectors. Variably positioned axial and radial shrouds are incorporated in the NASA rig to study the effect of shroud clearance on gearbox power loss. A number of researchers have given experimental and analytical results for single spur gears, with and without shrouding. Shrouded meshed spur gear test results are sparse in the literature. Windage tests were run at NASA Glenn using meshed spur gears at four shroud configurations: unshrouded, shrouded (max. axial, max. radial), and two intermediate shrouding conditions. Results are compared to available meshed spur gear power loss data analyses as well as single spur gear data analyses.

windage↗

NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

The spread of flames over the surface of solid combustible materials is affected by the environmental conditions. Variables such as flow velocity, oxygen concentration, ambient pressure, partial gravity or microgravity, may change the material flammability and influence the fire dynamics. This is an important fire safety issue for space exploration vehicles and space habitats which will very likely have different environments than those currently used on the International Space Station, such as reduced pressure and enriched oxygen concentration (SEA). However, testing experimentally the materials to be used and qualified for space exploration under these conditions is a difficult and expensive task. Here, numerical modeling is used to understand the dominant physico-chemical processes on the concurrent flame spread over thin fabrics under reduced ambient pressure (and in turn, buoyancy) and variable gravitational conditions. The numerical model uses the Fire Dynamics Simulator (FDS6) code with a single-step Arrhenius reaction rate for the solid phase decomposition. Different models are tested for the gas phase combustion kinetics. The model results are validated with experimental results obtained at similar reduced ambient pressure and flow conditions at 1 g. It is shown that as ambient pressure is reduced the flame spread rate over a thin fabric is also reduced, both experimentally and numerically. Numerical results are compared to an analytical approach previously developed to explain the experimental trends. Further interpretation of the model results provides information regarding the physics of the process and how they are affected by the lower pressure environments and gravity conditions. The results of this work provide guidance for potential on-earth testing for fire safety design in spacecraft and space habitats.

Fire Safety↗

NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

microgravity↗

DOE OSTI2025

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗

DOE OSTI2024

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models

While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce the points selection algorithm developed by Shin and Xiu as a hyper-reduction method. The selection algorithm was originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity $\mathcal{S}$ that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with a gappy proper orthogonal decomposition (POD) algorithm. Here, we found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy than gappy POD especially when the number of sampling points is small, although we note that S-OPT shows slow asymptotic convergence with respect to the number of samples for some applications, e.g., Lagrangian hydrodynamics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, projection-based methods fail to adequately reduce the computational complexity. Devising alternative reduced order models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models. In this work, we develop a timestepping procedure for dynamical parameter-dependent problems, in which a neural-network is trained to propagate the coefficients of a reduced basis expansion. This results in an online stage with a computational cost independent of the size of the underlying problem. Here, we demonstrate our method on several parabolic partial differential equations, including a problem that is not parametrically separable.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Characterization of a new thermophilic sulfate-reducing bacterium Thermodesulfovibrio yellowstonii, gen. nov. and sp. nov.: its phylogenetic relationship to Thermodesulfobacterium commune and their origins deep within the bacterial domain

A thermophilic sulfate-reducing vibrio isolated from thermal vent water in Yellowstone Lake, Wyoming, USA is described. The gram-negative, curved rod-shaped cells averaged 0.3 micrometer wide and 1.5 micrometers long. They were motile by means of a single polar flagellum. Growth was observed between 40 degrees and 70 degrees C with optimal growth at 65 degrees C. Cultures remained viable for one year at 27 degrees C although spore-formation was not observed. Sulfate, thiosulfate and sulfite were used as electron acceptors. Sulfur, fumarate and nitrate were not reduced. In the presence of sulfate, growth was observed only with lactate, pyruvate, hydrogen plus acetate, or formate plus acetate. Pyruvate was the only compound observed to support fermentative growth. Pyruvate and lactate were oxidized to acetate. Desulfofuscidin and c-type cytochromes were present. The G + C content was 29.5 mol%. The divergence in the 16 S ribosomal RNA sequences between the new isolate and Thermodesulfobacterium commune suggests that these two thermophilic sulfate-reducing bacteria represent different genera. These two bacteria depict a lineage that branches deeply within the Bacteria domain and which is clearly distinct from previously defined phylogenetic lines of sulfate-reducing bacteria. Strain YP87 is described as the type strain of the new genus and species Thermodesulfovibrio yellowstonii.

Non-NASA Center↗

DOE OSTI2024

Use of Lignin-Based Admixture as Water Reducer (WRA) for Tailoring the Rheological Properties of Mortars for 3D-Printing

Efforts towards decarbonizing construction materials and industrial processes related to cement and concrete can be aided via multifaceted approaches that target alternative admixtures as well as precision control of fabrication. Chemical admixtures for water reduction have played a crucial role in the development of advanced mortar and concrete mixtures. Newer biomass processing techniques developed for aviation fuel production from corn stover biomass produce a highly reactive lignin byproduct that is suitable for chemical modifications to mimic the properties of commonly used polycarboxylate ethers (PCEs) with a smaller carbon footprint. These lignin-based plasticizers developed at NREL can be used in place of petrochemical-derived superplasticizers to tailor the rheological properties of cement-based systems for applications such as additive manufacturing while reducing the carbon intensity of the concrete mix. Lignosulfonates derived from paper pulping were historically used as water reducers only to be displaced with the rise of PCEs. The present study examines the use of a NREL produced lignin-based water reducing admixture (WRA), in cement pastes and mortar mixtures for 3D-printing at small- (mm) scale. The experimental program consisted of different formulations of oxidized lignin-based WRAs added in variable dosages to cement pastes with a fixed water-to-cement ratio. The objective is to achieve an appropriate workability, extrudability and buildability of mortar mixtures to produce 3D-printed specimens. The rheological characterization was performed to compare the initial yield stress and viscosity of the different mixtures with lignin-based admixture with respect to conventional PCE superplasticizers. The rheological characterization shows that the proposed material act as an effective water-reducer in cement systems, affecting the yield stress, plastic viscosity, and structuration rate of the mixtures evaluated. On the other hand, the effect on early hydration process of the cement pastes containing lignin-based admixtures is comparable to conventional superplasticizers.

3D-printing↗

DOE OSTI2025

Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks

The computational cost of high-fidelity numerical models makes outer-loop analysis, which requires repeated interrogation of the model such as uncertainty quantification, computationally demanding. Multi-fidelity methods, which construct a surrogate model using data from an ensemble of models of varying cost and accuracy, can substantially reduce the cost of outer-loop analysis. However, these methods can be difficult to apply when the model ensemble does not admit a clear hierarchy a priori and the correlations between models are low. Consequently, in this paper, we present a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models. Our method utilizes basis adaptation to build low-dimensional polynomial chaos expansions of each model and employs Multi-fidelity Networks to encode the relationships among models. We show that the resulting method exhibit two notable advantages over its counterpart: (1) enhanced accuracy (both reduced bias and variance); and (2) reduced dependency on the graph structure encoding relationships among models. We demonstrate the approach on an analytical test problem and a challenging finite element model for a spent nuclear fuel. Our method produces a surrogate model that is significantly more accurate than either a single-fidelity surrogate or a multi-fidelity surrogate constructed without basis adaptation.

42 ENGINEERING↗

DOE OSTI2026

Improving energy efficiency while reducing anthropogenic heat from buildings: how retrofits influence the building stock and urban microclimate in Los Angeles

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Anthropogenic heat↗

DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗

DOE OSTI2024

Determining the N -Representability of a Reduced Density Matrix via Unitary Evolution and Stochastic Sampling

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-representable allows the constrained minimization of a many-body Hamiltonian expectation value with respect to the p-body density matrix and, thus, the determination of its exact ground state. However, the number of constraints that complete the N-representability conditions grows exponentially with system size, and hence, the procedure quickly becomes intractable for practical applications. This work introduces a hybrid quantum-stochastic algorithm to effectively replace the N-representability conditions. The algorithm consists of applying to an initial N-body density matrix a sequence of unitary evolution operators constructed from a stochastic process that successively approaches the reduced state of the density matrix on a p-body subsystem, represented by a p-RDM, to a target p-body matrix, potentially a p-RDM. The generators of the evolution operators follow the well-known adaptive derivative-assembled pseudo-Trotter method (ADAPT), while the stochastic component is implemented by using a simulated annealing process. The resulting algorithm is independent of any underlying Hamiltonian, and it can be used to decide whether a given p-body matrix is N-representable, establishing a criterion to determine its quality and correcting it. We apply the proposed hybrid ADAPT algorithm to alleged reduced density matrices from a quantum chemistry electronic Hamiltonian, from the reduced Bardeen–Cooper–Schrieffer model with constant pairing, and from the Heisenberg XXZ spin model. In all cases, the proposed method behaves as expected for 1-RDMs and 2-RDMs, evolving the initial matrices toward different targets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Greater aperture counteracts effects of reduced stomatal density on WUE: a case study on sugarcane and meta-analysis

Stomata regulate CO 2 and water vapor exchange between leaves and the atmosphere. Stomata are a target for engineering to improve crop intrinsic water use efficiency (iWUE). One example is by expressing genes that lower stomatal density (SD) and reduce stomatal conductance (g sw ). However, the quantitative relationship between reduced SD, g sw , and the mechanisms underlying it is poorly understood. We addressed this knowledge gap using low-SD sugarcane (Saccharum spp. hybrid) as a case study alongside a meta-analysis of data from 10 species. Transgenic expression of EPIDERMAL PATTERNING FACTOR 2 from Sorghum bicolor (SbEFP2) in sugarcane reduced SD by 26-38% but did not affect gsw compared to wildtype. Further, no changes occurred in stomatal complex size or proxies for photosynthetic capacity. Measurements of gas exchange at low CO 2 concentrations that promote complete stomatal opening to normalize aperture size between genotypes were combined with modeling of maximum gsw from anatomical data. These data suggest that increased stomatal aperture is the only possible explanation for maintaining gsw when SD is reduced. Meta-analysis across C 3 dicots, C 3 monocots, and C 4 monocots revealed engineered reductions in SD are strongly correlated with lower gsw (r 2 =0.60-0.98), but this response is damped relative to the change in anatomy.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1975

Evaluation of methods of reducing community noise impact around San Jose municipal airport

A computer simulation of the airport noise impact on the surrounding communities was used to evaluate alternate operational procedures, improved technology, and land use conversion as methods of reducing community noise impact in the airport vicinity. In addition, a constant density population distribution was analyzed for possible application to other airport communities with fairly uniform population densities and similar aircraft operational patterns. The introduction of sound absorption material (SAM) was found to reduce community noise annoyance by over 25 percent, and the introduction of refan was found to reduce community annoyance by over 60 percent. Replacing the present aircraft was found to reduce the noise problem to very small proportions, and the introduction of an advanced technology twin was found to essentially eliminate the community noise problem.

Glick, J. M.↗

NASA NTRS1977

Noise of fan designed to reduce stator lift fluctuations

An existing fan stage was redesigned to reduce stator lift fluctuations and was acoustically tested at three nozzle sizes for reduced noise generation. The lift fluctuations on the stator were reduced by increasing the stator cord, adjusting incidence angles, and adjusting the rotor velocity diagrams. Broadband noise levels were signficantly reduced in the middle to high frequencies. Blade passage tone sound power was not lessened, but decreases in the harmonics were observed. Aerodynamic improvements in both performance and efficiency were obtained.

Dittmar, J. H.↗

NASA NTRS1980

Grooves reduce aircraft drag

Aerodynamic drag can be reduced by many small longitudinal grooves machined in aircraft skin. Experiments show that grooves parallel to airflow reduce drag by 4 to 7 percent. Reduced drag translates into reduced engine power required to overcome drag and ultimately to lower fuel consumption.

Walsh, M. J.↗