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Development of Acoustic Mufflers for Cabin Noise Reduction in Orion Spacecraft

Controlling cabin acoustic noise levels in the Crew Module (CM) of the Orion spacecraft is critical to ensure adequate speech intelligibility, to avoid fatigue, and prevent any possibility of temporary and permanent hearing loss to the crew. The primary source of cabin noise for the on-orbit phase of the mission is from the Environmental Control and Life Support System (ECLSS) which recycles and conditions breathing air and maintains cabin pressurization through its duct network and components. Unfortunately, as a side effect, noise from the ECLSS fans propagates through theses ducts and emanate into the cabin habitable volume via the ECLSS inlet and outlets. To mitigate excessive duct-borne noise, two ECLSS mufflers have been designed to provide significant acoustic transmission loss (TL) so that the cabin noise requirements can be met. Each muffler is meant to be installed in the ducting of the ECLSS air inlet and outlet sides, respectively. Packaging constraints and tight volume requirements necessitated the mufflers to be of complex geometry and compatible with the bends of the ECLSS duct layout. To design and characterize the acoustic performance of the inlet and outlet mufflers, computational acoustic models were developed using the Finite Element Method (FEM) with 𝑤𝑎𝑣𝑒଺ vibroacoustic software. Characterization of the acoustic material and perforations in the mufflers were addressed with poroelastic theory. Once the mufflers were designed on paper and its TL predicted, prototypes of these mufflers were created using additive manufacturing. The muffler prototypes were subsequently tested for acoustic TL in the laboratory with various con-figurations of acoustic materials. Comparing the analytical predictions to the test performance yielded excellent correlation for acoustic TL and demonstrated significant broadband noise attenuation. The ECLSS mufflers are currently scheduled to be installed on the Artemis II Crew Module (CM) of the Orion spacecraft and will provide significant cabin comfort to crew during the mission.

Indranil Dandaroy↗

NASA NTRS1998

Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level

Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.

Rajbhandaril, Narayan↗

DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗

NASA NTRS2023

Wheat Crop Traits Conferring High Yield Potential May Also Improve Yield Stability Under Climate Change

Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.

Climate change↗

DOE OSTI2023

Strengthening model development and effects of low diffusing solutes to coarsening resistance in aluminum alloys

A modified Orowan strengthening model is proposed to account for finite rod-shaped precipitates with hemispherical caps in aluminum alloy systems. A combined computational and experimental approach is used to study the influences of anisotropic Orowan looping and solute-dislocation interaction on temperature-dependent yield strength. Here, the strengthening model is validated with a dataset containing 297 experimental precipitate geometries, chemistries, temperatures, and strength measurements, and achieves a strong predictive correlation of 0.8713 with experimentally measured yield strengths. Under conditions that are applicable to coarsening, constant particle volume fraction and aspect ratio, the model predicts that short rod precipitates provide far superior strengthening effects compared to plate precipitates. A cast Al-Si-Mg-Cu alloy with rod-shaped Q-phase (Al 3 Cu 2 Mg 9 Si 7 ) precipitates was developed with a novel chemistry exploring the use of low-diffusivity elements (Mn, Ni, V, Zr) to limit precipitate coarsening. The thermodynamic behavior of Mn, Ni, V, and Zr across the Q-phase interface is examined using transmission electron microscopy (TEM), first-principles density-functional theory (DFT) calculations. and atom-probe tomography (APT). DFT calculations utilizing TEM identified Q-phase/Al-matrix interfaces show that Mn, Ni, V, and Zr preferentially segregate to the Q-phase precipitate boundaries which suggests inhibition of precipitate coarsening and higher strengths after temperature exposure. Atom-probe tomography confirms solute atom partitioning/segregation at the Q-phase/Al-matrix interface, found in the modified commercial AS7GU alloy (A356 +0.5%Cu), which supports these observations.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Using Clustering to Establish Climate Regimes from PCM Output

A multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.

Oglesby, Robert↗

NASA NTRS1989

Fuel-air mixing and distribution in a direct-injection stratified-charge rotary engine

A three-dimensional model for flows and combustion in reciprocating and rotary engines is applied to a direct-injection stratified-charge rotary engine to identify the main parameters that control its burning rate. It is concluded that the orientation of the six sprays of the main injector with respect to the air stream is important to enhance vaporization and the production of flammable mixture. In particular, no spray should be in the wake of any other spray. It was predicted that if such a condition is respected, the indicated efficiency would increase by some 6 percent at higher loads and 2 percent at lower loads. The computations led to the design of a new injector tip that has since yielded slightly better efficiency gains than predicted.

Abraham, J.↗

DOE OSTI2024

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

58 GEOSCIENCES↗

DOE OSTI2026

Quantum critical collapse abhors a naked singularity

Classical critical collapse yields naked singularities from smooth initial data, challenging cosmic censorship, and shaping the spectrum of primordial black holes. We show that one-loop vacuum polarization near the threshold qualitatively changes this outcome by dressing the singularity with a horizon within a controlled semiclassical regime. In analytically tractable Einstein-scalar critical spacetimes, a one-loop $s$-wave treatment linearized around self-similar backgrounds shows that regularity uniquely selects an asymptotically Minkowskian, vacuum-polarization state. Its renormalized stress tensor carries a universal quantum growing mode that competes with the classical unstable mode, shifts the critical point, and generates a trapped surface along with a finite mass gap at the new threshold, thereby enforcing horizon formation even under arbitrary fine-tuning. In primordial collapse, the threshold shift enters exponentially into the formation fraction, while the mass gap truncates the low-mass tail, suggesting potentially important consequences for the predicted mass spectrum. Furthermore, these results provide a self-consistent semiclassical treatment of critical collapse and yield sharp predictions within the one-loop, near-critical, linearized regime.

Anomalies↗

NASA NTRS1972

Two-dimensional finite-element temperature variance analysis

The finite element method is extended to thermal analysis by forming a variance analysis of temperature results so that the sensitivity of predicted temperatures to uncertainties in input variables is determined. The temperature fields within a finite number of elements are described in terms of the temperatures of vertices and the variational principle is used to minimize the integral equation describing thermal potential energy. A computer calculation yields the desired solution matrix of predicted temperatures and provides information about initial thermal parameters and their associated errors. Sample calculations show that all predicted temperatures are most effected by temperature values along fixed boundaries; more accurate specifications of these temperatures reduce errors in thermal calculations.

Heuser, J. S.↗

DOE OSTI2023

On High-Fluence Irradiation Hardening of Nine RPV Surveillance Steels in the UCSB ATR-2 Experiment: Implications for Extended-Life Embrittlement Predictions

Nine archival reactor pressure vessel (RPV) surveillance steels from commercial nuclear power plants were irradiated in the University of California, Santa Barbara Advanced Test Reactor 2 (ATR-2) experiment to evaluate irradiation embrittlement under low-flux surveillance capsule versus higher flux test reactor (ATR-2) conditions. The postirradiation measurements of irradiation hardening, measured as increases in yield stress (Δσy), and corresponding conversions of Δσy to Charpy V-notch 41-J transition temperature shifts (ΔTc) are compared with various embrittlement trend curve (ETC) model predictions for the nine steels. Tensile and converted shear punch and microhardness measurements of Δσy generally show a continuing increase between intermediate and high ATR-2 fluences. The Eason-Odette-Nanstad-Yamamoto and ASTM E900 ETC models underpredict embrittlement at the ATR-2 irradiation condition: irradiation temperature (Ti) of 292°C, neutron fluence (ϕt) of 1.4 × 1020 n/cm2 (E > 1 MeV), and neutron flux (ϕ) of 3.68 × 1012 n/cm2-s. On average, the French FIS and Japanese JAEC ETCs slightly overpredict the ATR-2 data. The increase in Δσy with higher fluence is primarily due to Ni-Mn-Si precipitates, which slowly evolve in both nearly copper-free and copper-bearing steels. Finally, a new Odette-Wells-Almirall-Yamamoto embrittlement model is shown that yields good predictions for the nine steels at high fluences (ϕt > 5.5 × 1019 n/cm2).

Nanstad, Randy K.↗

DOE OSTI2023

On High-Fluence Irradiation Hardening of Nine RPV Surveillance Steels in the UCSB ATR-2 Experiment: Implications for Extended-Life Embrittlement Predictions

Nanstad, Randy↗

DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗

DOE OSTI2022

Rapid Allylic 1,6 H-Atom Transfer in an Unsaturated Criegee Intermediate

Here, a novel allylic 1,6 hydrogen-atom-transfer mechanism is established through infrared activation of the 2-butenal oxide Criegee intermediate, resulting in very rapid unimolecular decay to hydroxyl (OH) radical products. A new precursor, Z/E -1,3-diiodobut-1-ene, is synthesized and photolyzed in the presence of oxygen to generate anew four-carbon Criegee intermediate with extended conjugation across the vinyl and carbonyl oxide groups that facilitates rapid allylic 1,6 H-atom transfer. A low-energy reaction pathway involving isomerization of 2-butenal oxide from a lower-energy ( tZZ ) conformer to a higher-energy ( cZZ ) conformer followed by 1,6 hydrogen transfer via a seven-membered ring transition state is predicted theoretically and shown experimentally to yield OH products. The low-lying ( tZZ ) conformer of 2-butenaloxide is identified based on computed an harmonic frequencies and intensities of its conformers. Experimental IR action spectra recorded in the fundamental CH stretch region with OH product detection by UV laser-induced fluorescence reveal a distinctive IR transition of the low-lying ( tZZ ) conformer at 2996 cm -1 that results in rapid unimolecular decay to OH products. Statistical RRKM calculations involving a combination of conformational isomerization and unimolecular decay via 1,6 H-transfer yield an effective decay rate k eff ( E ) on the order of 10 8 s -1 at ca. 3000 cm -1 in good accord with the experiment. Unimolecular decay proceeds with significant enhancement due to quantum mechanical tunneling. A rapid thermal decay rate of ca. 10 6 s -1 is predicted by master-equation modeling of 2-butenal oxide at 298 K, 1 bar. This novel unimolecular decay pathway is expected to increase the nonphotolytic production of OH radicals upon alkene ozonolysis in the troposphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1976

Study leading to the refinement of the SRB structural analysis program

The constructed SRB math model was used for the analysis of the 85/32/5(VV/VH/theta) cavity collapse load condition for the aft end of the SRB structure. Linear, nonlinear collapse and bifurcation buckling analyses were performed. The linear and bifurcation buckling analyses showed that structural integrity and positive margins of safety are maintained for the applied maximum load case using a design factor of 1.25. The nonlinear collapse analysis predicts stresses in the skirt above the yield for a load factor of 1.0. Applying the 1.25 design factor gives a negative margin of safety against the material ultimate. The predicted collapse occurs at a load factor higher than 1.25.

Jones, W. E.↗

DOE OSTI2024

A physics-informed deep learning description of Knudsen layer reactivity reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗