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Phase 2 program on ground test of refanned JT8D turbofan engines and nacelles for the 727 airplane. Volume 1: Summary
The propulsion performance, acoustic, structural, and systems changes to a 727-200 airplane retrofitted with a refan modification of the JT8D turbofan engine are evaluated. Model tests, design of certifiable airplane retrofit kit hardware, manufacture of test hardware, ground test of a current production JT8D engine, followed by test of the same engine modified to the refan configuration, detailed analyses of the retrofit impact on airplane airworthiness, performance, and noise, and a preliminary analysis of retrofit costs are included. Results indicate that the refan retrofit of the 727-200 would be certifiable and would result in a 6-to 8 EPNdb reduction in effective perceived noise level (EPNL) at the FAR 36 measuring points and an annoyance-weighted footprint area reduction of 68% to 83%. The installed refan engine is estimated to provide 14% greater takeoff thrust at zero velocity and 10% greater thrust at 100 kn (51.4 m/s). There would be an approximate 0.6% increase in cruise specific fuel consumption (SFC). The refan engine performance in conjunction with the increase in stalled weight results in a range reduction of approximately 15% over the unmodified airplane at the same brake release gross weight (BRGW), with a block fuel increase of 1.5% to 3%. With the particular model 727 that was studied, however, it is possible to operate the airplane (with minor structural modifications) at a higher BRGW and increase the range up to approximately 15% relative to the nonrefanned airplane (with equal or slightly increased noise levels). The JT8D refan engine also improves the limited-field range of the airplane.
On the Basic Equations for the Second-order Modeling of Compressible Turbulence
Equations for the mean and the turbulence quantities of compressible turbulent flows are derived in this report. Both the conventional Reynolds average and the mass-weighted Favre average were employed to decompose the flow variable into mean and turbulent quantities. These equations are to be used later in developing second-order Reynolds stress models for high-speed compressible flows. A few recent advances in modeling some of the terms in the equation due to compressibility effects are also summarized.
Simulations of Spray Reacting Flows in a Single Element LDI Injector With and Without Invoking an Eulerian Scalar PDF Method
This paper presents the numerical simulations of the Jet-A spray reacting flow in a single element lean direct injection (LDI) injector by using the National Combustion Code (NCC) with and without invoking the Eulerian scalar probability density function (PDF) method. The flow field is calculated by using the Reynolds averaged Navier-Stokes equations (RANS and URANS) with nonlinear turbulence models, and when the scalar PDF method is invoked, the energy and compositions or species mass fractions are calculated by solving the equation of an ensemble averaged density-weighted fine-grained probability density function that is referred to here as the averaged probability density function (APDF). A nonlinear model for closing the convection term of the scalar APDF equation is used in the presented simulations and will be briefly described. Detailed comparisons between the results and available experimental data are carried out. Some positive findings of invoking the Eulerian scalar PDF method in both improving the simulation quality and reducing the computing cost are observed.
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.
Drude weights in one-dimensional systems with a single defect
Ballistic transport of a quantum system can be characterized by Drude weight, which quantifies the response of the system to a uniform electric field in the infinitely long timescale. The Drude weight is often discussed in terms of the Kohn formula, which gives the Drude weight by the derivative of the energy eigenvalue of a finite-size system with the periodic boundary condition in terms of the Aharonov-Bohm flux. Recently, the Kohn formula is generalized to nonlinear responses. However, the nonlinear Drude weight determined by the Kohn formula often diverges in the thermodynamic limit. In order to elucidate the issue, in this work we examine a simple example of a one-dimensional tight-binding model in the presence of a single defect at zero temperature. We find that its linear and nonlinear Drude weights given by the Kohn formula (i) depend on the Aharonov-Bohm flux and (ii) diverge proportionally to a power of the system size. Here, we argue that the problem can be attributed to different order of limits. The Drude weight according to the Kohn formula (“Kohn-Drude weight”) indicates the response of a finite-size system to an adiabatic insertion of the Aharonov-Bohm flux. While it is a well-defined physical quantity for a finite-size system, its thermodynamic limit does not always describe the ballistic transport of the bulk. The latter should be rather characterized by a “bulk Drude weight” defined by taking the thermodynamic limit first before the zero-frequency limit. While the potential issue of the order of limits has been sometimes discussed within the linear response, the discrepancy between the two limits is amplified in nonlinear Drude weights. We demonstrate the importance of the low-energy excitations of O(1/L), which are excluded from the Kohn-Drude weight, in regularizing the bulk Drude weight.
Effect of Base Bleed and Terminal Fairings on the Performance of Exhaust-Nozzle-Afterbody Combinations at Mach Numbers of 1.93, 2.55, and 3.05
An investigation of four exhaust-nozzle-afterbody combinations has been conducted in the Langley 9- by 12-inch blowdown tunnel at Mach numbers of 1.93, 2.55, and 3.05. The models were tested on a pylon-mounted nacelle and the jet exhaust was simulated with cold air. Base bleed w a s varied from 0 to about 12 percent of the primary jet weight flow and was discharged in to the base region through either a sonic or supersonic bleed nozzle. The models were tested at zero degree angle of attack and the Reynolds number range was from 8 x 10(exp 6) to 9 x 10(exp 6) per foot. The results indicate that the base pressure and the performance of the exhaust-nozzle-afterbody combinations were little affected gy the high-velocity base bleed. The efficiency of the terminal-fairing model was only slightly less than that of the convergent-divergent nozzle-afterbody combinations; this difference indicates the loss associated with improved transonic efficiency at higher Mach numbers.
Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework
A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
Analyzing Responses of Chemical Sensor Arrays
NASA is developing a third-generation electronic nose (ENose) capable of continuous monitoring of the International Space Station s cabin atmosphere for specific, harmful airborne contaminants. Previous generations of the ENose have been described in prior NASA Tech Briefs issues. Sensor selection is critical in both (prefabrication) sensor material selection and (post-fabrication) data analysis of the ENose, which detects several analytes that are difficult to detect, or that are at very low concentration ranges. Existing sensor selection approaches usually include limited statistical measures, where selectivity is more important but reliability and sensitivity are not of concern. When reliability and sensitivity can be major limiting factors in detecting target compounds reliably, the existing approach is not able to provide meaningful selection that will actually improve data analysis results. The approach and software reported here consider more statistical measures (factors) than existing approaches for a similar purpose. The result is a more balanced and robust sensor selection from a less than ideal sensor array. The software offers quick, flexible, optimal sensor selection and weighting for a variety of purposes without a time-consuming, iterative search by performing sensor calibrations to a known linear or nonlinear model, evaluating the individual sensor s statistics, scoring the individual sensor s overall performance, finding the best sensor array size to maximize class separation, finding optimal weights for the remaining sensor array, estimating limits of detection for the target compounds, evaluating fingerprint distance between group pairs, and finding the best event-detecting sensors.
Quantitative and interpretable order parameters for phase transitions from persistent homology
Here, we apply modern methods in computational topology to the task of discovering and characterizing phase transitions. As illustrations, we apply our method to four two-dimensional lattice spin models: the Ising, square ice, XY, and fully frustrated XY models. In particular, we use persistent homology, which computes the births and deaths of individual topological features as a coarse-graining scale or sublevel threshold is increased, to summarize multiscale and high-point correlations in a spin configuration. We employ vector representations of this information called persistence images to formulate and perform the statistical task of distinguishing phases. For the models we consider, a simple logistic regression on these images is sufficient to identify the phase transition. Interpretable order parameters are then read from the weights of the regression. This method suffices to identify magnetization, frustration, and vortex-antivortex structure as relevant features for phase transitions in our models. We also define “persistence” critical exponents and study how they are related to those critical exponents usually considered.
Cold Nuclear Matter Effects on J/{psi} Yields as a Function of Rapidity and Nuclear Geometry in d+A Collisions at {radical}(s{sub NN})=200 GeV
We present measurements of J/{psi} yields in d+Au collisions at {radical}(s{sub NN})=200 GeV recorded by the PHENIX experiment and compare them with yields in p+p collisions at the same energy per nucleon-nucleon collision. The measurements cover a large kinematic range in J/{psi} rapidity (-2.2<y<2.4) with high statistical precision and are compared with two theoretical models: one with nuclear shadowing combined with final state breakup and one with coherent gluon saturation effects. In order to remove model dependent systematic uncertainties we also compare the data to a simple geometric model. The forward rapidity data are inconsistent with nuclear modifications that are linear or exponential in the density weighted longitudinal thickness, such as those from the final state breakup of the bound state.
Sparsity Prior Computed Tomography Reconstruction Using a Nonstandard Simultaneous X-ray Acquisition Model
In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which sparse approximation is performed. The simultaneous x-ray acquisition model ensures rapid capture of x-rays; however, it captures a significantly fewer number of attenuation measurements, and the projections are nonuniform. We propose a weighted average filter in the reconstruction framework to ensure better quality reconstruction by minimizing the effect of nonuniform projections. The performance of the state-of-the-art algorithms is analyzed with and without weighted averaging before sparse approximation, in simulated and real environments. Experiments in the simulated environment are conducted with and without the presence of noise. From the results, it is evident that sparsity prior algorithms are capable of producing cross-sectional reconstruction using the simultaneous x-ray acquisition model, and better reconstruction quality is achievable with the incorporation of weighted averaging in the reconstruction framework.
Ionospheric calibration for single frequency altimeter measurements
This study is a preliminary analysis of the effectiveness (in terms of altimeter calibration accuracy) of various ionosphere models and the Global Positioning System (GPS) to calibrate single frequency altimeter height measurements for ionospheric path delay. In particular, the research focused on ingesting GPS Total Electron Content (TEC) data into the physical Parameterized Real-Time Ionospheric Specification Model (PRISM), which estimates the composition of the ionosphere using independent empirical and physical models and has the capability of adjusting to additional ionospheric measurements. Two types of GPS data were used to adjust the PRISM model: GPS receiver station data mapped from line-of-sight observations to the vertical at the point of interest and a grid map (generated at the Jet Propulsion Laboratory) of GPS derived TEC in a sun-fixed longitude frame. The adjusted PRISM TEC values, as well as predictions by the International Reference Ionosphere (IRI-90), a climatological (monthly mean) model of the ionosphere, were compared to TOPEX dual-frequency TEC measurements (considered as truth) for a number of TOPEX sub-satellite tracks. For a 13.6 GHz altimeter, a Total Electron Content (TEC) of 1 TECU 10(exp 16) electrons/sq m corresponds to approximately 0.218 centimeters of range delay. A maximum expected TEC (at solar maximum or during solar storms) of 10(exp 18) electrons/sq m will create 22 centimeters of range delay. Compared with the TOPEX data, the PRISM predictions were generally accurate within the TECU when the sub-satellite track of interest passed within 300 to 400 km of the GPS TEC data or when the track passed through a night-time ionosphere. If neither was the case, in particular if the track passed through a local noon ionosphere, the PRISM values differed by more than 10 TECU and by as much as 40 TECU. The IRI-90 model, with no current ability to unseat GPS data, predicted TEC to a slightly higher error of 12 TECU. The performance of PRISM is very promising for predicting TEC and will prove useful for calibrating single frequency altimeter height measurements for ionospheric path delay. When adjusted to the GPS line-of-sight data the PRISM URSI empirical model predicted TEC over a day's period to within a global error of 8.60 TECU rms during a nighttime ionosphere and 9.74 TECU rms during the day. When adjusted to the GPS derived TEC grid, the PRISM parametrized model predicted TEC to within an error of 8.47 TECU rms for a nighttime ionosphere and 12.83 TECU rms during the day. However, the grid cannot be considered globally due to the lack of sufficient numbers of GPS stations and large latitude gaps in GPS data. It is the opinion of the authors that using the PRISM model and adjusting to the global sun-fixed TEC grid regenerated with a localized weighted interpolation offers the best possibility of meeting the 10 TECU global rms (or 2 cm at 13.6 GHz) ionosphere range correction accuracy requirement of TOPEX/Poseidon and should be the subject of further study. However, it is clear that the anticipated requirement of 34 TECU global rms for TOPEX/Poseidon Follow-On (corresponding to the TOPEX/Poseidon performance) can not be met with any realizable combination of existing models and data assimilation schemes.
The storage performance of automotive cryo-compressed hydrogen vessels
Cryo-compressed hydrogen storage promises to deliver the highest system storage density leading to practical vehicles with range comparable to today's gasoline vehicles and fundamental cost and safety advantages. Yet, cryogenic vessels are complex systems, continuously drifting in thermodynamic space depending on use patterns, insulation performance, vessel characteristics, liquid hydrogen pump performance, and para-H 2 to ortho-H 2 conversion. In this paper, cryogenic vessel fill density results from a previous publication are extended to calculate system storage performance, including volumetric (gH 2 /L), gravimetric (H 2 weight fraction), and vent losses over a broad range of conditions. The results validate previous experiments and models indicating that cryogenic pressure vessels have maximum system density of all available storage technologies while avoiding vent losses in all but the most extreme situations. Design pressures in the range 250–350 bar seem most advantageous due to high system density and low weight and cost, although determining an optimum pressure demands a complete economic and functional analysis. Future insulation, vessel, and liquid hydrogen pump improvements are finally analyzed that, while not experimentally exhibited to date, show promise of being feasible in the future as their level of technical maturity increases, leading to maximum H 2 storage performance for cryo-compressed storage. If proven feasible and incorporated into future cryogenic vessels, these improvements will enable 50 + gH 2 /L system density at 10+% H 2 weight fraction.
High-Resolution Forest Canopy Height Estimation in an African Blue Carbon Ecosystem
Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereophotogrammetric techniques on high-resolution spaceborne imagery (HRSI) for southern Mozambique. A mean-weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18-1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three-dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.
Impact of Active Climate Control Seats on Energy Use, Fuel Use, and CO2 Emissions
A project was developed through collaboration between Gentherm and NREL to determine the impact of climate control seats for light-duty vehicles in the United States. The project used a combination of experimentation and analysis, with experimental results providing critical input to the analysis process. First, outdoor stationary vehicle testing was performed at NREL's facility in Golden, CO using multiple occupants. Two pre-production Ford Focus electric vehicles were used for testing; one containing a standard inactive seat and the second vehicle containing a Gentherm climate control seat. Multiple maximum cool-down and steady-state cooling tests were performed in late summer conditions. The two vehicles were used to determine the increase in cabin temperature when using the climate control seat in comparison to the baseline vehicle cabin temperature with a standard seat at the equivalent occupant whole-body sensation. The experiments estimated that on average, the climate control seats allowed for a 2.61 degrees Celsius increase in vehicle cabin temperature at equivalent occupant body sensation compared to the baseline vehicle. The increased cabin air temperature along with their measured energy usage were then used as inputs to the national analysis process. The national analysis process was constructed from full vehicle cabin, HVAC, and propulsion models previously developed by NREL. In addition, three representative vehicle platforms, vehicle usage patterns, and vehicle registration weighted environmental data were integrated into the analysis process. Both the baseline vehicle and the vehicle with climate control seats were simulated, using the experimentally determined cabin temperature offset of 2.61degrees Celsius and added seat energy as inputs to the climate control seat vehicle model. The U.S. composite annual fuel use savings for the climate control seats over the baseline A/C system was determined to be 5.1 gallons of gasoline per year per vehicle, corresponding to 4.0 grams of CO2/mile savings. Finally, the potential impact of 100 percent adoption of climate control seats on U.S. light-duty fleet A/C fuel use was calculated to be 1.3 billion gallons of gasoline annually with a corresponding CO2 emissions reduction of 12.7 million tons. Direct comparison of the impact of the CCS to the ventilated seat off-cycle credit was not possible because the NREL analysis calculated a combined car/truck savings and the baseline A/C CO2 emissions were higher than EPA. To enable comparison, the CCS national A/C CO2 emissions were split into car/truck components and the ventilated seat credit was scaled up. The split CO2 emissions savings due to the CCS were 3.5 g/mi for a car and 4.4 g/mi for a truck. The CCS saved an additional 2.0 g/mi and 2.5 g/mi over the adjusted ventilated seat credit for a car and truck, respectively.
Modeling CO 2 emissions from A rctic lakes: Model development and site‐level study
Abstract Recent studies indicated that Arctic lakes play an important role in receiving, processing, and storing organic carbon exported from terrestrial ecosystems. To quantify the contribution of Arctic lakes to the global carbon cycle, we developed a one‐dimensional process‐based Arctic Lake Biogeochemistry Model (ALBM) that explicitly simulates the dynamics of organic and inorganic carbon in Arctic lakes. By realistically modeling water mixing, carbon biogeochemistry, and permafrost carbon loading, the model can reproduce the seasonal variability of CO 2 fluxes from the study Arctic lakes. The simulated area‐weighted CO 2 fluxes from yedoma thermokarst lakes, nonyedoma thermokarst lakes, and glacial lakes are 29.5, 13.0, and 21.4 g C m −2 yr −1 , respectively, close to the observed values (31.2, 17.2, and 16.5 ± 7.7 g C m −2 yr −1 , respectively). The simulations show that the high CO 2 fluxes from yedoma thermokarst lakes are stimulated by the biomineralization of mobilized labile organic carbon from thawing yedoma permafrost. The simulations also imply that the relative contribution of glacial lakes to the global carbon cycle could be the largest because of their much larger surface area and high biomineralization and carbon loading. According to the model, sunlight‐induced organic carbon degradation is more important for shallow nonyedoma thermokarst lakes but its overall contribution to the global carbon cycle could be limited. Overall, the ALBM can simulate the whole‐lake carbon balance of Arctic lakes, a difficult task for field and laboratory experiments and other biogeochemistry models.
Optimization of Type 4 composite pressure vessels using genetic algorithms and simulated annealing
We propose and demonstrate two methodologies for weight minimization of Type 4 (plastic lined, fiber wrapped) compressed hydrogen pressure vessels: genetic algorithms and simulated annealing. We consider 70 MPa vessels with a safety factor of 2.25, and analyze the vessels with classical laminate theory. We propose an objective function based on Tsai-Wu criterion, composite thickness, a safety factor, and a penalization factor. Here, the optimum results are analyzed and compared by a high resolution finite element model. Computer simulations show that the proposed methodology produces more efficient designs by reducing the weight by up to 9.8% and 11.2% when compared to previously published vessel optimization research.