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NASA NTRS1992

A passive microwave algorithm for tropical oceanic rainfall

This study discusses a rainfall algorithm utilizing six channels of microwave radiance data from the Nimbus-7 Scanning Multifrequency Microwave Radiometer. The algorithm is intended for short-term climate studies over the ocean at low latitudes. To find a set of functional relationships, rain rates are regressed on brightness temperatures for each channel. Next, these functions are integrated over a class of rain-rate distributions to find relations between mean brightness temperatures and mean rain rates. This step accounts for beam filling. Finally, weights are obtained for combining the rain rates from the individual channels. The weights vary with the rain rates, so that the optimum combination of channels is always used. Results are stored in a database grid 1 deg latitude x 1 deg longitude by one month. To test the algorithm, three years (1979-81) of data from the Indian Ocean are processed.

Hinton, Barry B.↗

NASA NTRS1994

Integrated-Circuit Controller For Brushless dc Motor

Generic circuit performs commutation-logic and power-switching functions for control of brushless dc motor. Controller includes commutation-logic and associated control circuitry, power supply, and inverters containing power transistors. Major advantages of controller are size, weight, and power consumption can be made less than other brushless-dc-motor controllers.

Le, Dong Tuan↗

NASA NTRS1999

A Probabilistic Approach to Aeropropulsion System Assessment

A probabilistic approach is described for aeropropulsion system assessment. To demonstrate this approach, the technical performance of a wave rotor-enhanced gas turbine engine (i.e. engine net thrust, specific fuel consumption, and engine weight) is assessed. The assessment accounts for the uncertainties in component efficiencies/flows and mechanical design variables, using probability distributions. The results are presented in the form of cumulative distribution functions (CDFs) and sensitivity analyses, and are compared with those from the traditional deterministic approach. The comparison shows that the probabilistic approach provides a more realistic and systematic way to assess an aeropropulsion system.

Tong, Michael T.↗

NASA NTRS2000

A Probabilistic Approach to Aeropropulsion System Assessment

A probabilistic approach is described for aeropropulsion system assessment. To demonstrate this approach, the technical performance of a wave rotor-enhanced gas turbine engine (i.e. engine net thrust, specific fuel consumption, and engine weight) is assessed. The assessment accounts for the uncertainties in component efficiencies/flows and mechanical design variables, using probability distributions. The results are presented in the form of cumulative distribution functions (CDFS) and sensitivity analyses, and are compared with those from the traditional deterministic approach. The comparison shows that the probabilistic approach provides a more realistic and systematic way to assess an aeropropulsion system.

Tong, Michael T.↗

NASA NTRS2000

A Probabilistic Approach to Aeropropulsion System Assessment

Tong, Michael T.↗

NASA NTRS2001

Exoskeletal Engine Concept: Feasibility Studies for Medium and Small Thrust Engines

The exoskeletal engine concept is one in which the shafts and disks are eliminated and are replaced by rotating casings that support the blades in spanwise compression. Omission of the shafts and disks leads to an open channel at the engine centerline. This has immense potential for reduced jet noise and for the accomodation of an alternative form of thruster for use in a combined cycle. The use of ceramic composite materials has the potential for significantly reduced weight as well as higher working temperatures without cooling air. The exoskeletal configuration is also a natural stepping-stone to complete counter-rotating turbomachinery. Ultimately this will lead to reductions in weight, length, parts count and improved efficiency. The feasibility studies are in three parts. Part I-Systems and Component Requirements addressed the mechanical aspects of components from a functionality perspective. This effort laid the groundwork for preliminary design studies. Although important, it is not felt to be particularly original, and has therefore not been included in the current overview. Part 2-Preliminary Design Studies turned to some of the cycle and performance issues inherent in an exoskeletal configuration and some initial attempts at preliminary design of turbomachinery were described. Twin-spoon and single-spool 25.800-lbf-thrust turbofans were used as reference vehicles in a mid-size commercial subsonic category in addition to a single-spool 5,000-lbf-thrust turbofan that represented a general aviation application. The exoskeletal engine, with its open centerline, has tremendous potential for noise suppression and some preliminary analysis was done which began to quantify the benefits. Part 3-Additional Preliminary Design Studies revisited the design of single-spool 25,800-lbf-thrust turbofan configurations, but in addition to the original FPR = 1.6 and BPR = 5.1 reference engine, two additional configurations used FPR = 2.4 and BPR = 3.0 and FPR = 3.2 and BPR = 2.0 were investigated. The single-spool 5,000-lbf-thrust turbofan was refined and the small engine study was extended to include a 2,000-lbf-thrust turbojet. More attention was paid to optimizing the turbomachinery. Turbine cooling flows were eliminated, in keeping with the use of uncooled CMC material in exoskeletal engines. The turbine performance parameters moved much closer to the nominal target values, demonstrating the great benefits to the cycle of uncooled turbines.

Halliwell, Ian↗

NASA NTRS2010

Oxidation of SiC Fiber-Reinforced SiC Matrix Composites with a BN Interphase

SiC-fiber reinforced SiC matrix composites with a BN interphase were oxidized in reduced oxygen partial pressures of oxygen to simulate the environment for hypersonic vehicle leading edge applications. The constituent fibers as well as composite coupons were oxidized in oxygen partial pressures ranging from 1000 ppm O2 to 5% O2 balance argon. Exposure temperatures ranged from 816 C to 1353 C (1500 F to 2450 F). The oxidation kinetics of the coated fibers were monitored by thermogravimetric analysis (TGA). An initial rapid transient weight gain was observed followed by parabolic kinetics. Possible mechanisms for the transient oxidation are discussed. One edge of the composite coupon seal coat was ground off to simulate damage to the composite which allowed oxygen ingress to the interior of the composite. Oxidation kinetics of the coupons were characterized by scanning electron microscopy since the weight changes were minimal. It was found that sealing of the coupon edge by silica formation occurred. Differences in the amount and morphology of the sealing silica as a function of time, temperature and oxygen partial pressure are discussed. Implications for use of these materials for hypersonic vehicle leading edge materials are summarized.

Opila, Elizabeth↗

NASA NTRS2016

Development and Characterization of a Small Spacecraft Electro-Optic Scanner for Free-Space Laser Communications

Emergent data-intensive missions coupled with dramatic reductions in spacecraft size plus an increasing number of space-based missions necessitates new high performance, compact and low cost communications technology. Free space optical communications offer advantages including orders of magnitude increase for data rate performance, increased security, immunity to jamming and lack of frequency allocation requirements when compared with conventional radio frequency (RF) means. The spatial coherence and low divergence associated with the optical frequencies of laser communications lends themselves to superior performance, but this increased directionality also creates one of the primary technical challenges in establishing a laser communications link by repeatedly and reliably pointing the beam onto the receive aperture. Several solutions have emerged from wide angle (slow) mechanical articulation systems, fine (fast) steering mirrors and rotating prisms, inertial compensation gyros and vibration isolation cancellation systems, but each requires moving components and imparts a measured amount of burden on the host platform. The complexity, cost and size of current mechanically scanned solutions limits their platform applicability, and restricts the feasibility of deploying optical communications payloads on very compact spacecraft employing critical systems. A high speed, wide angle, non-mechanical solution is therefore desirable. The purpose of this work is to share the development, testing, and demonstration of a breadboard prototype electro-optic (EO) scanned laser-communication link (see Figure 1). This demonstration is a step toward realizing ultra-low Size, Weight and Power (SWaP) SmallSat/MicroSat EO non-mechanical laser beam steering modules for high bandwidth ( greater than Gbps) free-space data links operating in the 1550 nm wavelength bands. The elimination of all moving parts will dramatically reduce SWaP and cost, increase component lifetime and reliability, and simplify the system design of laser communication modules. This paper describes the target mission architectures and requirements (few cubic centimeters of volume, 10's of grams of weight with milliwatts of power) and design of the beam steering module. Laboratory metrology is used to determine the component performance including horizontal and vertical resolution (20urad) as a function of control voltage (see Figure 2), transition time (0.1-1ms), pointing repeatability and optic insertion loss. A test bed system demonstration, including a full laser communications link, is conducted. The capabilities of this new EO beam steerer provide an opportunity to dramatically improve space communications through increased utilization of laser technology on smaller platforms than were previously attainable.

Laser Optical Communications↗

NASA NTRS2016

Low Force Penetration of Icy Regolith

A percussive cone penetrometer measures the strength of granular material by using percussion to deliver mechanical energy into the material. A percussive cone penetrometer was used in this study to penetrate a regolith ice mixture by breaking up ice and decompacting the regolith. As compared to a static cone penetrometer, percussion allows low reaction forces to push a penetrometer probe tip more easily into dry regolith in a low gravity environment from a planetary surface rover or a landed spacecraft. A percussive cone penetrates icy regolith at ice concentrations that a static cone cannot penetrate. In this study, the percussive penetrator was able to penetrate material under 65 N of down-force which could not be penetrated using a static cone under full body weight. This paper discusses using a percussive cone penetrometer to discern changes in the concentration of water-ice in a mixture of lunar regolith simulant and ice to a depth of one meter. The rate of penetration was found to be a function of the ice content and was not significantly affected by the down-force. The test results demonstrate that this method may be ideal for a small platform in a reduced gravity environment. However, there are some cases where the system may not be able to penetrate the icy regolith, and there is some risk of the probe tip becoming stuck so that it cannot be retracted. It is also shown that a percussive cone penetrometer could be used to prospect for water ice in regolith at concentrations as high as 8 by weight.

surface stabilization↗

DOE OSTI2024

GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

We present the Galaxy Assembly and Interaction Neural Networks (Gainn), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 < z < 15. The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies. The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M). We compare the performance of the neural networks using different combinations of these filters, as well as different activation functions and numbers of layers. The best neural network predicted redshift with a normalized rms error of $0.010^{+0.003}_{-0.001}$, stellar mass with rms = $0.089^{+0.044}_{-0.022}$, halo mass with a mean-squared error of $0.022^{+0.014}_{-0.008}$, and mass-weighted age with rms = $12.466^{+5.065}_{-2.408}$. We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias $\langle$Δz$\rangle$ < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Data-driven approach to parameterize SCAN + U for an accurate description of 3 d transition metal oxide thermochemistry

Semilocal density-functional theory (DFT) methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on the DFT + U method can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. Here we develop a systematic methodology to determine the parameters and to statistically assess the results by considering interlinked thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT + U compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach for 3d transition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the strongly constrained and appropriately normed (SCAN) density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. A simplified correction scheme that does not involve SCAN/SCAN + U compatibility terms still achieves an error reduction of 30% (25%). The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimize DFT + U performance for other target physical properties.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Integrated aerodynamic/dynamic optimization of helicopter rotor blades

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4 per rev vertical hub shear for a rotor blade in forward flight. The coupling of aerodynamics and dynamics is accomplished through the inclusion of airloads which vary with the design variables during the optimization process. Both single and multiple objective functions are used in the optimization formulation. The Global Criteria Approach is used to formulate the multiple objective optimization and results are compared with those obtained by using single objective function formulations. Constraints are imposed on natural frequencies, autorotational inertia, and centrifugal stress. The program CAMRAD is used for the blade aerodynamic and dynamic analyses, and the program CONMIN is used for the optimization. Since the spanwise and the azimuthal variations of loading are responsible for most rotor vibration and noise, the vertical airload distributions on the blade, before and after optimization, are compared. The total power required by the rotor to produce the same amount of thrust for a given area is also calculated before and after optimization. Results indicate that integrated optimization can significantly reduce the blade weight, the hub shear and the amplitude of the vertical airload distributions on the blade and the total power required by the rotor.

Chattopadhyay, Aditi↗

DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗

DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗

DOE OSTI2020

Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development

Global expansion of hydropower resources has increased in recent years to meet growing energy demands and fill worldwide gaps in electricity supply. However, hydropower induces significant environmental impacts on river ecosystems - impacts that are addressed through environmental impact assessment (EIA) processes. The need for effective EIA processes is increasing as environmental regulations are either stressed in developing countries undertaking rapid expansion of hydropower capacity or time- and resource-intensive in developed countries. Part of the challenge in implementing EIAs lies in reaching a consensus among stakeholders regarding the most important environmental factors as the focus of impact studies. To help address this gap, we developed a weight-of-evidence approach (and toolkit) as a preliminary and coarse assessment of the most relevant impacts of hydropower on primary components of the river ecosystem, as identified using river function indicators. Through a science-based questionnaire and predictive model, users identify which environmental indicators may be impacted during hydropower development as well as those indicators that have the highest levels of uncertainty and require further investigation. Furthermore, an assessment tool visualizes inter-dependent indicator relationships, which help formulate hypotheses about causal relationships explored through environmental studies. We apply these tools to four existing hydropower projects and one hypothetical new hydropower project of varying sizes and environmental contexts. We observed consistencies between the output of our tools and the Federal Energy Regulatory Commission licensing process (inclusive of EIAs) but also important differences arising from holistic scientific evaluations (our toolkit) versus regulatory policies. The tools presented herein are aimed at increasing the efficiency of the EIA processes that engender environmental studies without loss of rigor or transparency of rationale necessary for understanding, considering, and mitigating the environmental consequences of hydropower.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Harmonic analysis of discrete tracers of large-scale structure

It is commonplace in cosmology to analyze fields projected onto the celestial sphere, and in particular density fields that are defined by a set of points e.g. galaxies. When performing an harmonic-space analysis of such data (e.g. an angular power spectrum) using a pixelized map one has to deal with aliasing of small-scale power and pixel window functions. We compare and contrast the approaches to this problem taken in the cosmic microwave background and large-scale structure communities, and advocate for a direct approach that avoids pixelization. We describe a method for performing a pseudo-spectrum analysis of a galaxy data set and show that it can be implemented efficiently using well-known algorithms for special functions that are suited to acceleration by graphics processing units (GPUs). The method returns the same spectra as the more traditional map-based approach if in the latter the number of pixels is taken to be sufficiently large and the mask is well sampled. The method is readily generalizable to cross-spectra and higher-order functions. It also provides a convenient route for distributing the information in a galaxy catalog directly in harmonic space, as a complement to releasing the configuration-space positions and weights, and a route to spectral apodization. Finally, we make public a code enabling the application of our method to existing and upcoming datasets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

GenomeFace v1.0

GenomeFace is meta-genome binning software. Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

Lettich, Richard [Lawrence Berkeley National Labor↗

DOE OSTI2023

Calibration of Single-Crystal TATB EOS

An equation of state (EOS) for the single-crystal TATB (C 6 H 6 N 6 O 6 , molar mass 258.15 g/mol) is calibrated using the experimental/computational calibration data uploaded to the caldata repository. The thermodynamically complete EOS is given by the Helmholtz free energy as a single-valued almost analytical function of the specific volume and temperature. The cold curve is taken to be of the MACAW form. The vibrational contribution to the Helmholtz free energy is described by a weighted sum of three Debye oscillators in the quasi-harmonic approximation. The calibration data suggests the range of reliable applicability of the EOS from the ambient conditions to temperatures of ∼ 3000 K and pressures of ∼ 50-70 GPa.

36 MATERIALS SCIENCE↗