Results for “complexity analysis” — Page 92

DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗

NASA NTRSDate not supplied

Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool

Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.

Missions to Mars↗

NASA NTRS2024

Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool

Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.

Missions to Mars↗

NASA NTRS2004

A Novel Biomedical Device Utilizing Light Emitting Nano-Structures

This paper will discuss the development of a novel biomedical detection device that will be used to detect microorganisms with the use of infrared fluorochrome polymers attached to antibodies in fluids such as water. The fluorochrome polymers emit light in the near inferred region (NIR), approximately 805 nm, when excited by an NIR laser at 778 nm. The device could remarkably change the way laboratory testing is done today. The testing process is usually performed on a time scale of days while our device will be able to detect microorganisms in minutes. This type of time efficient analysis is ideal for use aboard the International Space Station and the Space Shuttle (ISS/SS) and has many useful commercial applications, for instance at a water treatment plant and food processing plants. With more research and experimentation the testing might also one day be used to detect bacteria and viruses in complex fluids such as blood, which would revolutionize blood analysis as it is performed today. My contribution to the project has been to develop a process which will allow an antibody/fluorescent dye pair to be conjugated to a specific bacteria or virus and than to to be separated from a sample body of water for detection. The antibody being used in this experiment is anti beta galactosidase and its complement enzyme is beta galactosidase, a non harmful derivative of E. Coli. The anti beta galactosidase has been conjugated to the fluorochrome polymer, IRDye800, which emits at approximately 806 nm. The dye when excited by the NIR laser emits a signal which is detected by a spectrometer and then is read by state of the art computer software. The state-of-the-art process includes incubating the anti beta galactosidase and beta galactosidase in a phosphate buffer solution in a test tube, allowing the antibody to bind to specific sites on the enzyme. After the antibody is bound to the enzyme, it is centrifuged in specific filters that will allow free antibody to wash away and leave the antibody-enzyme complexes on top in solution for testing and analysis. This solution is pipetted into a cuvette, a special plastic test tube, which will then be excited by the laser. The signal read will tell US that an antibody is present and since it is bound to the enzyme, that the bacteria is also present.

Varaljay, Vanessa A.↗

NASA NTRS2016

Submillimeter and Far-Infrared Dielectric Properties of Thin Films

The complex dielectric function enables the study of a material's refractive and absorptive properties and provides information on a material's potential for practical application. Commonly employed line shape profile functions from the literature are briefly surveyed and their suitability for representation of dielectric material properties are discussed. An analysis approach to derive a material's complex dielectric function from observed transmittance spectra in the far-infrared and submillimeter regimes is presented. The underlying model employed satisfies the requirements set by the Kramers-Kronig relations. The dielectric function parameters derived from this approach typically reproduce the observed transmittance spectra with an accuracy of less than 4%.

Cataldo, Giuseppe↗

DOE OSTI2025

Multiple oxidation states of uranium stabilized by an O , N , O -ligand

Elements of the 5f row maintain a wide variety of oxidation states that have been exploited in synthesis, catalysis, and separations. Herein, we describe the complexation of an O,N,O chelator, ExPh, with uranium in the (IV) and (VI) (uranyl) oxidation states. These two uranium complexes, U(IV)ExPh and UO 2 ExPh, respectively, were characterized in the solid state via single-crystal X-ray diffraction (SC-XRD) analysis. Electrochemical studies using cyclic voltammetry were employed to investigate the multiple redox events associated with the U(IV) complex. Spectro-electrochemical analysis of U(IV)ExPh provided spectroscopic evidence of a stable U(V) species. Chemical oxidation of U(IV)ExPh allowed isolation of the U(V) complex, U(V)ExPh. All three stable uranium complexes produced in this study were characterized via IR, UV-vis and NMR spectroscopies, and micro-spectrophotometry. On the other hand, efforts to reduce U(IV)ExPh to the corresponding U(III) species or produce this putative complex directly from ExPh failed to yield an isolable product. The stabilization of three formal oxidation states of uranium, coupled with previous lanthanide-row results, paves the way for studies of ExPh and its analogues in minor actinide chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2019

High-fidelity Multidisciplinary Sensitivity Analysis and Design Optimization for Rotorcraft Applications

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. A sensitivity-enabled fluid dynamics solver and a nonlinear flexible multibody dynamics solver are coupled to predict aerodynamic loads and structural responses of helicopter rotor blades. A discretely consistent adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system is validated by conducting simulations for a benchmark rotorcraft model and comparing solutions with established analyses and experimental data. Sensitivities of lift computed by the multidisciplinary sensitivity analysis are verified by comparison with the sensitivities obtained by complex-variable simulations. Finally the multidisciplinary sensitivity analysis is applied to a constrained gradient-based design optimization for a HART-II rotorcraft configuration.

Wang, Li↗

NASA NTRS2016

Using NASA's GeneLab for VESGEN Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways

One fundamental requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. A unifying perspective is that vascular patterning offers a useful readout of molecular signaling that necessarily integrates these complex pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascularrelated changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the spatial and dynamic dimensions of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions. Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.

Venation↗

DOE OSTI2021

Symbolic pregression: Discovering physical laws from distorted video

In this work, we present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled synthetic video (or, more generally, for discovering and modeling predictable features in time-series data). We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of nonlinearity, acceleration, and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression (“pregression”) step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis. An inertial frame is autodiscovered by minimizing the combined equation complexity for multiple experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1979

Transonic airfoil codes

Computer codes for the design and analysis of transonic airfoils are considered. The design code relies on the method of complex characteristics in the hodograph plane to construct shockless airfoil. The analysis code uses artificial viscosity to calculate flows with weak shock waves at off-design conditions. Comparisons with experiments show that an excellent simulation of two dimensional wind tunnel tests is obtained. The codes have been widely adopted by the aircraft industry as a tool for the development of supercritical wing technology.

Garabedian, P. R.↗

NASA NTRS1974

High-response on-line gas analysis system for hydrogen-reaction combustion products

The results of testing an on-line quadrupole gas analyzer system are reported. Gas samples were drawn from the exhaust of a hydrogen-oxygen-nitrogen rocket which simulated the flow composition and dynamics at the combustor exit of a supersonic combustion ramjet engine. System response time of less than 50 milliseconds was demonstrated, with analytical accuracy estimated to be + or - 5 percent. For more complex chemical systems with interfering atom patterns, analysis would be more difficult. A cooled-gas pyrometer probe was evaluated as a total temperature indicator and as the primary mass flow measuring element for the total sample flow rate.

Metzler, A. J.↗

NASA NTRS2002

Studying the Iron Line Complex in the Bright Seyfert Galaxy NGC 5506

This grant was to support the reduction and analysis of our approved XMM observation of the nearby Seyfert 2 galaxy NGC 5506. The observation has been carried out simultaneously with a BeppoSAX observation of the same source. The proposal was aimed to study in detail the Compton reflection component and the complex Iron K line of this source, combining the still unique capability of BeppoSAX in hard X-rays (to strongly constrain the reflection component, and then the intrinsic nuclear continuum), and the sensitivity of XMM at the energy of the Iron Line complex. NGC 5506 is one of the brightest AGN in hard X-rays and has been intensively studied in the past. GINGA detected the complex iron line as well as the reflection component. Both ASCA (spectroscopically) and Rossi-XTE (through variability analysis) suggested that the FeK line is complex, possibly made up of several distinct components. The centroid of the FeK complex in a subsequent BeppoSAX observation was bluer than the 6.4 keV energy of the relatively low-ionization iron Kalpha transition. NGC 5506 has been observed simultaneously by NewtonXMM and BeppoSAX on February 2-3 2001. we have reduced and analyzed both the NewtonXMM and the BeppoSAX data, and have written and published a paper on our results (appeared in Volume 377 (page 31) of A&A-Letters). Our main results can be summarized as follows: (a) we confirm that the FeK line is complex, and for the first time disentangle its components: we find that at least two components made up the FeK complex, one neutral and narrow, at 6.4 keV (rest energy), and another one either broader and highly ionized, at about 6.7 keV (rest frame), or, in turn, made up of two narrow and unresolved components from the He-like and the H-like ions of Fe; (b) the two possible solution for the high-ionization Fe-K component, are statistically indistinguishable. However, physically, a blend of two narrow lines from photoionized matter seems to be preferable to emission of a relativistically broadened line from an ionized accretion disk; (c) The bulk of the Compton reflection continuum originates in neutral matter, and is therefore associated with the narrow FeK line at 6.4 keV: they are most likely emitted by distant matter.

Nicastro, Fabrizio↗

NASA NTRS2021

A Computational Analysis of Boundary Layer Instability over the BOLT Configuration

The complex boundary layer flow over the BOLT flight configuration is known to exhibit multiple and potentially interacting instability mechanisms. This paper represents a continuation of our numerical investigation of the flow instabilities over the main test surface of the BOLT configuration by using state-of-the-art tools in multidimensional instability analysis. Specifically, the paper extends our previous computations by considering the separate effects of a nonzero angle-of-attack and a nonzero yaw on the modal instability characteristics of the boundary layer streaks adjacent to the minor-axis symmetry plane. At a selected condition from the ascent part of the anticipated flight trajectory, either type of departure from the design condition is shown to have a considerable impact on the structure of the laminar basic state and, hence, also on the amplification characteristics of the instability waves. A broader set of computations, including N-factor predictions for the relevant instabilities, will be presented in the final paper.

Boundary layer transition↗

NASA NTRS2021

A Computational Analysis of Boundary Layer Instability over the BOLT Configuration

The complex boundary layer flow over the BOLT flight configuration is known to exhibit multiple and potentially interacting instability mechanisms. This paper represents a continuation of our numerical investigation of the flow instabilities over the main test surface of the BOLT configuration by using state-of-the-art tools in multidimensional instability analysis. Specifically, the paper extends our previous computations by considering the separate effects of a nonzero angle of attack and a nonzero yaw on the modal instability characteristics of the boundary layer streaks adjacent to the minor-axis symmetry plane, specifically near both ends of the azimuthal region of a thick boundary layer in the middle, where this region rapidly changes to a thinner boundary layer on either side. At the t = 28.8767 s condition from the ascent part of the anticipated flight trajectory with a flight Mach number of M ∞ = 5.53 and unit Reynolds number of 4.25 x 10 6 /m, either type of departure from the design condition is shown to have a considerable impact on the structure of the basic state rollup within the region of interest and, hence, also on the amplification characteristics of instability waves within the resulting streaks. Yet, for a yaw angle of β = 4 degrees, the computations indicate only a slight reduction with respect to the peak N-factor of nearly 11 at the design condition of zero degrees yaw and zero degrees angle of attack. In contrast, an angle of attack equal to α = 4 degrees, the peak N-factor decreases to nearly 6 on the leeward side and increases above 16 on the windward side, making the onset of transition highly likely on the windward side. Computations also highlight the role of streak instabilities that originate as Mack mode disturbances and also demonstrate the potential pitfalls in using surface pressure sensors alone to gauge the magnitude of instability amplification.

Boundary layer transition↗

DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗

NASA NTRS2015

The THS Experiment: Simulating Titans Atmospheric Chemistry at Low Temperature (200K)

In Titan's atmosphere, composed mainly of N2 (95-98%) and CH4 (2-5%), a complex chemistry occurs at low temperature, and leads to the production of heavy organic molecules and subsequently solid aerosols. Here, we used the Titan Haze Simulation (THS) experiment, an experimental setup developed at the NASA Ames COSmIC simulation facility to study Titan's atmospheric chemistry at low temperature. In the THS, the chemistry is simulated by plasma in the stream of a supersonic expansion. With this unique design, the gas is cooled to Titan-like temperature ( approximately 150K) before inducing the chemistry by plasma, and remains at low temperature in the plasma discharge (approximately 200K). Different N2-CH4-based gas mixtures can be injected in the plasma, with or without the addition of heavier precursors present as trace elements on Titan, in order to monitor the evolution of the chemical growth. Both the gas- and solid phase products resulting from the plasma-induced chemistry can be monitored and analyzed using a combination of complementary in situ and ex situ diagnostics. A recent mass spectrometry[1] study of the gas phase has demonstrated that the THS is a unique tool to probe the first and intermediate steps of Titan's atmospheric chemistry at Titan-like temperature. In particular, the mass spectra obtained in a N2-CH4-C2H2-C6H6 mixture are relevant for comparison to Cassini's CAPS-IBS instrument. The results of a complementary study of the solid phase are consistent with the chemical growth evolution observed in the gas phase. Grains and aggregates form in the gas phase and can be jet deposited on various substrates for ex situ analysis. Scanning Electron Microscopy images show that more complex mixtures produce larger aggregates. A mass spectrometry analysis of the solid phase has detected the presence of aminoacetonitrile, a precursor of glycine, in the THS aerosols. X-ray Absorption Near Edge Structure (XANES) measurements also show the presence of imine and nitrile functional groups, showing evidence of nitrogen chemistry. These complementary studies show the high potential of THS to better understand Titan's chemistry and the origin of aerosol formation.

Laboratory↗

DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

Predicting Fatigue Lives Under Complex Loading Conditions

Cyclic Damage Accumulation (CDA) computer program performs high-temperature, low-cycle-fatigue life prediction for materials analysis. Designed to account for effects on creep-fatigue life of complex loadings involving such factors as thermomechanical fatigue, hold periods, wave-shapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features practical for application to actual component analysis using modern finite-element or boundary-element methods. Although developed for use in predicting crack-initiation lifetimes of gas-turbine-engine materials, also applied to other materials as well. Written in FORTRAN 77.

Mcgaw, Michael A.↗