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Results for “Structural performance prediction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Evaluation of capillary reinforced composites

Anti-icing of the inlet of jet engines is generally performed with high pressure heated air that is directed forward from the compressor through a series of pipes to various manifolds located near the structures to be anti-iced. From these manifolds, the air is directed to all flowpath surfaces that may be susceptible to ice formation. There the anti-icing function may be performed by either heat conduction or film heating. Unfortunately, the prospect of utilizing lighweight, high strength composites for inlet structures of jet engines has been frustrated by the low transverse thermal conductivity of such materials. It was the objective of this program to develop an advanced materials and design concept for anti-icing composite structures. The concept that was evaluated used capillary glass tubes embedded on the surface of a composite structure with heated air ducted through the tubes. An analytical computer program was developed to predict the anti-icing performance of such tubes and a test program was conducted to demonstrate actual performance of this system. Test data and analytical code results were in excellent agreement. Both indicate feasibility of using capillary tubes for surface heating as a means for composite engine structures to combat ice accumulation.

Cahill, J. E.↗
DOE OSTI2021

Budget Constrained Machine Learning for Early Prediction of Adverse Outcomes for COVID-19 Patients

Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan. Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features. Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin. Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Cadena Pico, JoseE.↗
NASA NTRS2024

Improved Mechanical Performance in FDM Cellular Frame Structures through Partial Incorporation of Faces

The utilization of lattice-type cellular architectures has seen a significant increase, owing to their predictable shape and the ability to fabricate templated porous materials through low-cost 3D-printing methods. Frames based on atomic lattice structures such as face-centered cubic (FCC), body-centered cubic (BCC), or simple cubic (SC) have been utilized. In FDM, the mechanical performance has been impeded by stress concentration at the nodes and melt-solidification interfaces arising from layer-by-layer deposition. Adding plates to the frames has resulted in improvements with a concurrent increase in weight and hot-pocket-induced dimensional impact in the closed cells formed. In this paper, we explore compressive performance from the partial addition of plates to the frames of a SC-BCC lattice. Compression testing of both single unit cells and 4 × 4 × 4 lattices in all three axial directions is conducted to examine stress transfer to the nearest neighbor and assess scale-up stress transfer. Our findings reveal that hybrid lattice structure unit cells exhibit significantly improved modulus in the range of 125% to 393%, specific modulus in the range of 13% to 120%, and energy absorption in the range of 17% to 395% over the open lattice. The scaled-up lattice modulus increased by 8% to 400%, specific modulus by 2% to 107%, and energy absorption by 37% to 553% over the lattice frame. Parameters that emerged as key to improved lightweighting.

FDM↗
NASA NTRS2001

Dynamic Analysis of a Building Under Rocket Engine Plume Acoustic Load

Studies have been performed to develop finite-element modeling and simulation techniques to predict the dynamic structural response of Building 4010 to the acoustic load from the plume of high-thrust rocket motors. The building is the Test Control Center and general office space for the E-complex at Stennis Space Center. It is a large single span; light-structured building located approximately 1,000 feet from the E-1 test stand. A three-dimensional shell/beam combined model of the building was built using Pro/Engineer platform and imported into Pro/Mechanica for analysis. An Equivalent Shell technique was developed to simplify the highly complex building structure so that the calculation is more efficient and accurate. A deterministic approach was used for the dynamic analysis. A pre-stressed modal analysis was performed to simulate the weight stiffening of the structure, through which about 200 modes ranging from 0 to 35 Hz were identified. In an initial dynamic frequency analysis, the maximum response over the model was found. Then the complete 3-D distributions of the displacement, as well as the stresses, were calculated through a final frequency analysis. The results were compared to a strain gage and accelerometer recordings from rocket engine tests and showed reasonable agreement.

Qian, Z.↗
NASA NTRS1987

On the effect of leading edge blowing on circulation control airfoil aerodynamics

In the present context the term circulation control is used to denote a method of lift generation that utilizes tangential jet blowing over the upper surface of a rounded trailing edge airfoil to determine the location of the boundary layer separation points, thus setting an effective Kutta condition. At present little information exists on the flow structure generated by circulation control airfoils under leading edge blowing. Consequently, no theoretical methods exist to predict airfoil performance under such conditions. An experimental study of the flow field generated by a two dimensional circulation control airfoil under steady leading and trailing edge blowing was undertaken. The objective was to fundamentally understand the overall flow structure generated and its relation to airfoil performance. Flow visualization was performed to define the overall flow field structure. Measurements of the airfoil forces were also made to provide a correlation of the observed flow field structure to airfoil performance. Preliminary results are presented, specifically on the effect on the flow field structure of leading edge blowing, alone and in conjunction with trailing edge blowing.

Mclachlan, B. G.↗
NASA NTRSDate not supplied

Assessment of Artemis-1 Pogo Flight Instrumentation System

As the space industry continues to strive for more efficient launch vehicles, it must relyon increasingly accurate predictive models. Verification of models typically requires physical testing. Flight data measurements offer the most real and therefore the most accurate data for model correlation. As NASA prepares for the inaugural launch of Space Launch System(SLS), Artemis-1, they must rely heavily on predictive system models to ensure flight safety.NASA has implemented a Development Flight Instrumentation (DFI) system in hopes of recovering useful flight data to aid in model correlation. Historically, some of the most important flight measurements are those that monitor the potentially destructive dynamic interaction of the structural and propellant modes – a phenomenon known as pogo. Pogo is a dynamic instability that can occur on a launch vehicle during any phase of ascent. During this investigation an end-to-end assessment of the Artemis-I Pogo-related DFI was performed to identify any obstacles inherent in the current instrumentation system which may prevent successful measurement of the data necessary to validate the current predictive fluid-structural models. Input drive signals were approximated and applied to a state-space system model to derive predicted pressure and acceleration responses. These predicted responses were then fed through a simulation of the data acquisition process in order to recover predicted measurements. Finally, a mock Flight Data Analysis (FDA) was performed to assess the ability of these measurements to meet the Flight Test Objectives(FTO).

Adam Johnson↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Novel Si(1-x)Ge(x)/Si heterojunction internal photoemission long wavelength infrared detectors

There is a major need for long-wavelength-infrared (LWIR) detector arrays in the range of 8 to 16 microns which operate with close-cycle cryocoolers above 65 K. In addition, it would be very attractive to have Si-based infrared (IR) detectors that can be easily integrated with Si readout circuitry and have good pixel-to-pixel uniformity, which is critical for focal plane array (FPA) applications. Here, researchers report a novel Si(1-x)Ge(x)/Si heterojunction internal photoemission (HIP) detector approach with a tailorable long wavelength infrared cutoff wavelength, based on internal photoemission over the Si(1-x)Ge(x)/Si heterojunction. The HIP detectors were grown by molecular beam epitaxy (MBE), which allows one to optimize the device structure with precise control of doping profiles, layer thickness and composition. The feasibility of a novel Si(1-x)Ge(x)/Si HIP detector has been demonstrated with tailorable cutoff wavelength in the LWIR region. Photoresponse at wavelengths 2 to 10 microns are obtained with quantum efficiency (QE) above approx. 1 percent in these non-optimized device structures. It should be possible to significantly improve the QE of the HIP detectors by optimizing the thickness, composition, and doping concentration of the Si(1-x)Ge(x) layers and by configuring the detector for maximum absorption such as the use of a cavity structure. With optimization of the QE and by matching the barrier energy to the desired wavelength cutoff to minimize the thermionic current, researchers predict near background limited performance in the LWIR region with operating temperatures above 65K. Finally, with mature Si processing, the relatively simple device structure offers potential for low-cost producible arrays with excellent uniformity.

Lin, T. L.↗
DOE OSTI2022

Integrating phytoplankton pigment and DNA meta-barcoding observations to determine phytoplankton composition in the coastal ocean

Quantifying phytoplankton composition is critical to predicting marine ecosystem structure and function. DNA meta-barcoding and high-performance liquid chromatography (HPLC) pigment analysis are two widely used methods for assessing phytoplankton composition; however, comparing their performance has been done only rarely. Here, we integrate DNA meta-barcoding and HPLC pigment observations to determine eukaryotic phytoplankton composition in the Santa Barbara Channel, California. We find that both methods identify the same four dominant eukaryotic phytoplankton taxa (diatoms, dinoflagellates, chlorophytes, and prymnesiophytes), but inter- and intra-lineage variability in biomarker pigmentation (associated with both a lack of taxonomic specificity of biomarker pigments and intrinsic differences in accessory pigmentation) drives substantial disagreement between the methods. Covariation network analysis circumvents this disagreement and reveals that diverse assemblages of phytoplankton and other protists covary with distinct suites of biomarker pigments. Our results highlight the strengths and weaknesses of each method in characterizing phytoplankton composition and reveal novel insights into phytoplankton physiology that could only be gained by integrating the two methods. Finally, we suggest a path to monitor eukaryotic plankton communities on unprecedented spatiotemporal scales based on the covariation of unique phytoplankton and protistan assemblages with remotely sensible phytoplankton pigment concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Physics-aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower-dimensional feature space. We build upon our previous work on physics-aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure-aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower-dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data-driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale.

Mathematics and Computing↗
DOE OSTI2026

Validation Data for Benchmarking Wire Arc Additive Manufacturing Process Simulations

Residual stresses cause geometric distortion and affect mechanical performance of additively manufactured structures, yet they are notoriously difficult to assess and predict. Distortion (warpage) can drive parts outside dimensional tolerance limits, leading to part rejection or rework. For parts that meet tolerance, locked-in residual stress fields can affect structural integrity during operation, particularly subcritical cracking by fatigue, creep, or corrosion. This work develops benchmark data for a common additive manufacturing process (Wire Arc Additive Manufacturing) that can be applied for calibration and validation of physical process models that predict residual stress fields. The work includes design of two different samples of differing geometry, detailed manufacturing records for a set of physical samples, and an extensive set of residual stress measurement data developed using two diverse techniques (the contour method and neutron diffraction). An initial application of the work is also reported, where a modeling challenge was issued to secure residual stress model predictions from two independent laboratories that were blind to residual stress measurement data. These initial blind residual stress predictions show significant discrepancies relative to the measurement data, illustrating the potential value of the underlying validation data. An open repository for this work, including the sample designs, manufacturing process records, and the residual stress data, is also provided for future application in non-blind validation efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mutual information prediction for strongly correlated systems

We have trained a new machine-learning (ML) model which predicts mutual information (MI) for strongly correlated systems. This is a complex quantity, which is much more difficult to predict than one-site entropies, but carries important information about the correlation structure inside electronic systems. In this work, we replaced the expensive density matrix renormalization group (DMRG) calculations by newly trained ML model for prediction of the mutual information. We show the performance of the model on two important tasks: (a) to determine the correlation structure and (b) to determine ordering of orbitals for accurate DMRG calculations. In conclusion, the results are compared with the MI obtained from accurate DMRG calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Calculations versus measurements for residual dose rates from SNS spent structures

Here, residual dose rate measurements were conducted on target vessel #13 and proton beam window #5 after extraction from their service locations. These measurements are used to verify calculation methods of radionuclide inventory assessment that are typically performed for nuclear waste characterization and transportation of these structures. Neutronics analyses for predicting residual dose rates are carried out using the transport code MCNPX and the transmutation code CINDER90. For transport analyses a complex and rigorous geometry model of the structures and their surroundings are applied. The neutronics analyses are carried out using the Bertini and CEM high energy physics models for simulating particles interactions above the table-based cross section range. Finally, obtained calculational results are analyzed and compared to the measured dose rates and overall show good agreement within 25%, which shows applicability of the methods used in analyses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Exceptional Radiation Absorption in a Pentagon-Based Si Allotrope

Excellent photovoltaic performance is predicted in a pentagonal covalent network of Si in a hollow structure exhibiting both thermal and dynamical stability. Consisting of a combination of sp 2 and sp 3 hybridized Si atomic orbitals, the GW0 computed band structure shows an indirect band gap near the zone edge and also a manifold of directly absorbing transitions at frequencies in the window of visible light, in distinction with conventional Si. Hydrogenation of a single sp 2 site is predicted to lead to a robust local magnetic moment. In this study, we find a low formation energy at low pressure that is compatible with other experimentally known phases, suggesting that a stable phase might be obtained.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Dye Nanoaggregate Structures in MK-2, N3, and N749 Dye···TiO 2 Interfaces That Represent Dye-Sensitized Solar Cell Working Electrodes

The working electrode of a dye-sensitized solar cell (DSSC) consists of dye molecules adsorbed onto nanoparticles of a semiconductor such as TiO 2 . A reliable prediction of the DSSC photovoltaic performance of a given dye requires in-depth knowledge about the precise structure of the dye···TiO 2 interface. In this work, X-ray reflectometry (XRR) and grazing-incidence small-angle X-ray scattering (GISAXS) are employed to determine the dye···TiO 2 interfacial structure and associated dye aggregation behavior of three high-performance DSSC dyes, i.e., a metal-free organic dye, MK-2, and the two archetypal ruthenium-based organometallic dyes, N3 and N749 (Black Dye). Results show that all three dyes form nanoaggregates in dye···TiO 2 interfaces. We determine the dye nanoaggregate separations, sizes, distribution densities, and the extent of short-range order within each dye self-assembly in the longitudinal and lateral directions. Dye···TiO 2 composites fabricated using dye solutions of varying concentrations are analyzed. We find that nanoaggregates of the three dyes are separated by several hundred nanometers (158-203 nm) in dye···TiO 2 interfaces that have been fabricated using concentrated dye solutions (0.5 mM or 1.0 mM). MK-2 and N749 dyes also display smaller interparticle separations. Dye nanoparticle diameters are of the order of 156-198 nm, sizes that are comparable to the largest interparticle separations. Thus, no extraneous dye particles can be fitted into gaps between particles, so the dye self-assembly is saturated. Self-assemblies of all three dyes exhibit both lateral and longitudinal short-range order; N3 displays a particularly short coherence length along the TiO 2 surface, with extensive structured disorder along the longitudinal direction. The operation of DSSC working electrodes would therefore seem to be dependent on a dye self-assembly that may exhibit several levels of structural granularity and dye aggregation effects.

14 SOLAR ENERGY↗
DOE OSTI2026

The AWAKEN wind farm benchmark, Part 2: Modeling results

Accurately modeling wind farm performance in complex atmospheric flows remains a challenge. This paper presents the modeling results of the American WAKE experimeNt (AWAKEN) wind farm benchmark, a collaborative effort involving 16 research groups from academia and industry within the International Energy Agency Wind Technology Collaboration Programme Task 57. The study evaluates a diverse suite of simulation tools, ranging from fast-running engineering wake models to high-fidelity large-eddy simulations, against a diurnal case study observed during the AWAKEN campaign. The benchmark utilized a three-phase structure to progressively assess model performance as observational data availability increased. Initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools. A distinct spatial bias was observed where models struggled to resolve the interplay between a low-level jet, wakes, and terrain-induced flow acceleration. In subsequent phases, leveraging additional measurements for model improvement led to a reduction in mean absolute error across the model ensemble; however, this effect was most pronounced in engineering wake models, where targeted calibration reduced error by up to 40~\%. Overall, the study demonstrates that inflow characterization remains a primary prerequisite for accuracy, particularly for models relying on coarse forcing datasets. While the limited ability to resolve local terrain-flow interactions under single-day conditions represent a recognized constraint, the overall findings on wake modeling and real-world validation still provide valuable guidance for model application and for mitigating this limitation.

Bodini, Nicola↗
NASA NTRS1985

X-29 initial flight test results

It is announced that the X-29 forward-swept-wing (FSW) aircraft has been built, with flight testing under way and proceeding smoothly. The X-29 is a single-seat, single-engine supersonic aircraft that blends an optimized FSW, a close-coupled near-coplar canard, an F-5A forward fuselage module employing two side-mounted engine inlets, and a new aft fuselage. An F404-GE-400 engine with afterburner provides about 16,000 lb of thrust. The X-29 was designed to be near neutrally stable in the supersonic region to minimize drag; it becomes highly unstable transonically and subsonically, which dictates the use of a computerized fly-by-wire flight control system capable of stabilizing the aircraft. The X-29 advanced technology demonstrator began flight testing on Dec. 14, 1984; by Sept. 26, 1985, its envelope had been expanded to 0.75 Mach, 350 knots estimated airspeed, and 30,000 feet altitude. The aircraft has flown with very few problems and aerodynamic, structural, and control system results have correlated well with predictions. Areas where prediction and performance are not so well correlated are noted.

Ishmael, S. D.↗
NASA NTRS1988

Comprehensive rotorcraft analysis methods

The development and application of comprehensive rotorcraft analysis methods in the field of rotorcraft technology are described. These large scale analyses and the resulting computer programs are intended to treat the complex aeromechanical phenomena that describe the behavior of rotorcraft. They may be used to predict rotor aerodynamics, acoustic, performance, stability and control, handling qualities, loads and vibrations, structures, dynamics, and aeroelastic stability characteristics for a variety of applications including research, preliminary and detail design, and evaluation and treatment of field problems. The principal comprehensive methods developed or under development in recent years and generally available to the rotorcraft community because of US Army Aviation Research and Technology Activity (ARTA) sponsorship of all or part of the software systems are the Rotorcraft Flight Simulation (C81), Dynamic System Coupler (DYSCO), Coupled Rotor/Airframe Vibration Analysis Program (SIMVIB), Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics (CAMRAD), General Rotorcraft Aeromechanical Stability Program (GRASP), and Second Generation Comprehensive Helicopter Analysis System (2GCHAS).

Stephens, Wendell B.↗