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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unusual modifications of protein biomarkers expressed by plasmid, prophage, and bacterial host of pathogenic Escherichia coli identified using top‐down proteomic analysis

Rationale Pathogenic bacteria often carry prophage (bacterial viruses) and plasmids (small circular pieces of DNA) that may harbor toxin, antibacterial, and antibiotic resistance genes. Proteomic characterization of pathogenic bacteria should include the identification of host proteins and proteins produced by prophage and plasmid genomes. Methods Protein biomarkers of two strains of Shiga toxin–producingEscherichia coli(STEC) were identified using antibiotic induction, matrix‐assisted laser desorption/ionization tandem time‐of‐flight (MALDI‐TOF‐TOF) tandem mass spectrometry (MS/MS) with post‐source decay (PSD), top‐down proteomic (TDP) analysis, and plasmid sequencing. Alphafold2 was also used to compare predicted in silico structures of the identified proteins to prominent fragment ions generated using MS/MS‐PSD. Strain samples were also analyzed with and without chemical reduction treatment to detect the attachment of pendant groups bound by thioester or disulfide bonds. Results Shiga toxin was detected and/or identified in both STEC strains. For the first time, we also identified the osmotically inducible protein (OsmY) whose sequence unexpectedly had two forms: a full and a truncated sequence. The truncated OsmY terminates in the middle of an α‐helix as determined by Alphafold2. A plasmid‐encoded colicin immunity protein was also identified with and without attachment of an unidentified cysteine‐bound pendant group (~307 Da). Plasmid sequencing confirmed top‐down analysis and the identification of a promoter upstream of the immunity gene that is activated by antibiotic induction, that is, SOS box. Conclusions TDP analysis, coupled with other techniques (e.g., antibiotic induction, chemical reduction, plasmid sequencing, and in silico protein modeling), is a powerful tool to identify proteins (and their modifications), including prophage‐ and plasmid‐encoded proteins, produced by pathogenic microorganisms.

Biochemistry & Molecular Biology↗
DOE OSTI2015

Characterizing the mean-field dynamo in turbulent accretion disks

The formation and evolution of a wide class of astrophysical objects are governed by turbulent, magnetized accretion disks. Understanding their secular dynamics is of primary importance. Apart from enabling mass accretion via the transport of angular momentum, the turbulence affects the long-term evolution of the embedded magnetic flux, which in turn regulates the efficiency of the transport. In this paper, we take a comprehensive next step toward an effective mean-field model for turbulent astrophysical disks by systematically studying the key properties of magnetorotational turbulence in vertically stratified, isothermal shearing boxes. This allows us to infer emergent properties of the ensuing chaotic flow as a function of the shear parameter, as well as the amount of net vertical flux. Using the test-field method, we furthermore characterize the mean-field dynamo coefficients that describe the long-term evolution of large-scale fields. We simultaneously infer the vertical shape and the spectral scale dependence of these closure coefficients, with the latter describing nonlocal contributions to the turbulent electromotive force. Based on this, we infer a scale-separation ratio of about 10 for the large-scale dynamo. We finally determine scaling properties of the mean-field dynamo coefficients. The relevant component of the dynamo α effect is found to scale linearly with the shear rate, as does the corresponding turbulent diffusion, η. Together, these scalings allow us to predict, in a quantitative manner, the cycle period of the well-known butterfly diagram. This lends new support to the importance of the αΩ mechanism in determining the evolution of large-scale magnetic fields in turbulent accretion disks.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Some problems of the calculation of three-dimensional boundary layer flows on general configurations

An accurate solution of the three-dimensional boundary layer equations over general configurations such as those encountered in aircraft and space shuttle design requires a very efficient, fast, and accurate numerical method with suitable turbulence models for the Reynolds stresses. The efficiency, speed, and accuracy of a three-dimensional numerical method together with the turbulence models for the Reynolds stresses are examined. The numerical method is the implicit two-point finite difference approach (Box Method) developed by Keller and applied to the boundary layer equations by Keller and Cebeci. In addition, a study of some of the problems that may arise in the solution of these equations for three-dimensional boundary layer flows over general configurations.

Cebeci, T.↗
NASA NTRS1980

An experimental investigation of three dimensional low speed minimum interference wind tunnel for high lift wings

As a means to achieve a minimum interference correction wind tunnel, a partially actively controlled test section was experimentally examined. A jet flapped wing with 0.91 m (36 in) span and R = 4.05 was used as a model to create moderately high lift coefficients. The partially controlled test section was simulated using an insert, a rectangular box 0.96 x 1.44 m (3.14 x 4.71 ft) open on both ends in the direction of the tunnel air flow, placed in the University of Washington Aeronautical Laboratories (UWAL) 2.44 x 3.66 m (8 x 12 ft) wind tunnel. A tail located three chords behind the wing was used to measure the downwash at the tail region. The experimental data indicates that, within the range of momentum coefficient examined, it appears to be unnecessary to actively control all four sides of the test section walls in order to achieve the near interference free flow field environment in a small wind tunnel. The remaining wall interference can be satisfactorily corrected by the vortex lattice method.

Shindo, S.↗
NASA NTRS2003

Optimization Based Efficiencies in First Order Reliability Analysis

This paper develops a method for updating the gradient vector of the limit state function in reliability analysis using Broyden's rank one updating technique. In problems that use commercial code as a black box, the gradient calculations are usually done using a finite difference approach, which becomes very expensive for large system models. The proposed method replaces the finite difference gradient calculations in a standard first order reliability method (FORM) with Broyden's Quasi-Newton technique. The resulting algorithm of Broyden updates within a FORM framework (BFORM) is used to run several example problems, and the results compared to standard FORM results. It is found that BFORM typically requires fewer functional evaluations that FORM to converge to the same answer.

Peck, Jeffrey A.↗
NASA NTRS2001

Predictor-Corrector Entry Guidance for Reusable Launch Vehicles

An online entry guidance algorithm has been developed using a predictor-corrector approach. The algorithm is designed for the Reusable Launch Vehicle (RLV) and is demonstrated by using, the X-33 model. The objective of the design is to handle widely dispersed entry conditions and deliver the vehicle at the Terminal Area Energy Management (TAEM) interface box within an acceptable tolerance and without violating any of the vehicle physical constraints. Combination of several control variables is used in testing the performance and computational requirement of the algorithm. The control variables are the bank angle, angle-of-attack and the time for roll reversal. The bank angle and angle-of-attack profiles are the nominal profiles plus the perturbations in each direction. The initial guess of the bank profile is a 45 degrees bank angle with reversal at 360 seconds from liftoff. A six-element state vector is propagated to the TAEM interface box through the integration of the equations of motion (EOM). Altitude, heading and range errors are computed between the desired and the achieved state at the TAEM interface. These errors are used to correct the initial guess of the control variables. This process is repeated until the errors meet an acceptable level at the TAEM interface. Several numerical optimization methods are used to evaluate the convergent property of the predictor-predictor methodology. Successful results are demonstrated using the X-33 model.

Youssef, Hussein↗
DOE OSTI2023

Increased Interpretability for Model-Driven Deception: MARS LDRD Project

Machine learning has been proposed as a solution to several cybersecurity solutions and one of the most promising applications is for digital twins for intrusion detection and driving deceptive defense. However, machine learning techniques often result in a black-box function that is difficult for end users to interpret which for deception limits their ability to effectively define decoys. In this report, an approach to validate the equations learned are accurate is provided and demonstrated. Following, begins the process of addressing this issue for a model-driven deception technology that produces equations representing the physical process controlled by operation technology devices. This research was performed by applying subject matter expert context to machine learned models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

A tentative unified sea model for scattering and emission

This work is an attempt to relate active and passive observations of the sea surface to each other, considering that more information can be obtained from a set of instruments when they are not just considered as a juxtaposition of separate and independent boxes, but as a single complex instrument. A careful review of several approaches to the solution of the scattering problem for a random rough surface has been performed. Inherent simplifications, and inconsistencies, are pointed out. To reduce the computation time to reasonable values, an approximate model is selected and some numerical results will be presented in order to illustrate the benefit that can be gained by a unified approach.

Guissard, A.↗
DOE OSTI2015

Turning statistical physics models into materials design engines

Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material's configuration space, or require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this approach lies in its capability to transform statistical models that describe materials into optimizers to tailor them. Here, by comparing against standard black-box optimization methods, we demonstrate how optimizers generated by this formalism can be faster and more effective, while remaining straightforward to implement. The scope of our approach includes possibilities for solving a variety of complex optimization and design problems concerning materials both in and out of equilibrium.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Graphical User Interface for Simulink Integrated Performance Analysis Model

The J-2X Engine (built by Pratt & Whitney Rocketdyne,) in the Upper Stage of the Ares I Crew Launch Vehicle, will only start within a certain range of temperature and pressure for Liquid Hydrogen and Liquid Oxygen propellants. The purpose of the Simulink Integrated Performance Analysis Model is to verify that in all reasonable conditions the temperature and pressure of the propellants are within the required J-2X engine start boxes. In order to run the simulation, test variables must be entered at all reasonable values of parameters such as heat leak and mass flow rate. To make this testing process as efficient as possible in order to save the maximum amount of time and money, and to show that the J-2X engine will start when it is required to do so, a graphical user interface (GUI) was created to allow the input of values to be used as parameters in the Simulink Model, without opening or altering the contents of the model. The GUI must allow for test data to come from Microsoft Excel files, allow those values to be edited before testing, place those values into the Simulink Model, and get the output from the Simulink Model. The GUI was built using MATLAB, and will run the Simulink simulation when the Simulate option is activated. After running the simulation, the GUI will construct a new Microsoft Excel file, as well as a MATLAB matrix file, using the output values for each test of the simulation so that they may graphed and compared to other values.

Durham, R. Caitlyn↗
DOE OSTI2019

Search for gamma-ray emission from p -wave dark matter annihilation in the Galactic Center

Indirect searches for dark matter through Standard Model products of its annihilation generally assume a cross section which is dominated by a term independent of velocity (s-wave annihilation). However, in many dark matter (DM) models, an s-wave annihilation cross section is absent or helicity suppressed. To reproduce the correct DM relic density in these models, the leading term in the cross section is proportional to the DM velocity squared (p-wave annihilation). Indirect detection of such p-wave DM is difficult because the average velocities of DM in galaxies today are orders of magnitude slower than the DM velocity at the time of decoupling from the primordial thermal plasma, thus suppressing the annihilation cross section today by some 5 orders of magnitude relative to its value at freeze-out. Thus p-wave DM is out of reach of traditional searches for DM annihilations in the Galactic halo. Near the region of influence of a central supermassive black hole, such as Sgr A*, however, DM can form a localized overdensity known as a “spike.” In such spikes, the DM is predicted to be both concentrated in space and accelerated to higher velocities, thereby allowing the γ-ray signature from its annihilation to potentially be detectable above the background. We use the Fermi Large Area Telescope to search for the γ-ray signature of p-wave annihilating DM from a spike around Sgr A* in the energy range 10–600 GeV. Such a signal would appear as a point source and would have a sharp line or boxlike spectral features difficult to mimic with standard astrophysical processes, indicating a DM origin. We find no significant excess of γ rays in this range, and we place upper limits on the flux in γ-ray boxes originating from the Galactic Center. Furthermore, this result, the first of its kind, is interpreted in the context of different models of the DM density near Sgr A*.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Gas dynamic simulations of galaxy formation

Results are presented from a simulation modeling the formation of a group of galaxies in a 'standard' cold, dark matter universe with delta = 1, h sub 0 = 50 km/(s(Mpc)), baryon fraction omega sub b = 0.1 and spectrum normalization sigma sub 8 = 0.6 (bias parameter b = 1.7). Initial conditions are generated within a periodic box with comoving length 16 Mpc in a manner constrained to produce a small cluster of total mass approximately 10 exp 14 solar mass. Two sets of 643 particles are used to model the dark matter and baryon fluids. Each gas particle represents 1.08 x 10 exp -8 solar mass, implying an L* galaxy is resolved by approximately 1000 particles. The system is evolved self-consistently in three dimensions using the combined N-body/hydrodynamic scheme P3MSPH up to a final redshift z = 1. Evolving to the present is prohibited by the fact that the mean density in the simulated volume is above critical and the entire volume would be going nonlinear beyond this point, We are currently analyzing another run with somewhat poorer mass resolution which was evolved to the present.

Evrard, August E.↗
DOE OSTI2020

Measurement of DC Arc-Flash Incident Energy in Large-Scale Photovoltaic Plants: A Basis for Standardization

The deployment of high-power dc equipment is increasing in solar photovoltaic (PV) plants, but very few studies have quantified dc arc-flash risks. Currently, PV plant owners and operators rely on theoretical, simplified models, such as those in NFPA-70E and other publications, for the assessment of risk associated with dc arc-flash. Here, this article presents an overview of arc-flash risks in a PV system based on a series of field experiments based on IEEE-1584 in two large-scale ground-mounted PV plants. The experiments include various high-power dc equipment of a PV plant, such as central inverters, combiner boxes, recombiner boxes, string inverters, and multiple configurations of electrodes in a 20-in calibration cube. The study reveals that none of the available dc arc-flash models are applicable for a PV plant. This work is an important first step toward developing an improved model that more accurately assesses dc arc-flash risk in a PV plant.

14 SOLAR ENERGY↗
NASA NTRS2008

Electron Heating and Quasiparticle Tunnelling in Superconducting Charge Qubits

We have directly measured non-equilibrium quasiparticle tunnelling in the time domain as a function of temperature and RF carrier power for a pair of charge qubits based on the single Cooper-pair box, where the readout is performed with a multiplexed quantum capacitance technique. We have extracted an effective electron temperature for each applied RF power, using the data taken at the lowest power as a reference curve. This data has been fit to a standard T? electron heating model, with a reasonable correspondence with established material parameters.

Single Cooper Pair Box↗
NASA NTRS2006

System Architecture Modeling for Technology Portfolio Management using ATLAS

Strategic planners and technology portfolio managers have traditionally relied on consensus-based tools, such as Analytical Hierarchy Process (AHP) and Quality Function Deployment (QFD) in planning the funding of technology development. While useful to a certain extent, these tools are limited in the ability to fully quantify the impact of a technology choice on system mass, system reliability, project schedule, and lifecycle cost. The Advanced Technology Lifecycle Analysis System (ATLAS) aims to provide strategic planners a decision support tool for analyzing technology selections within a Space Exploration Architecture (SEA). Using ATLAS, strategic planners can select physics-based system models from a library, configure the systems with technologies and performance parameters, and plan the deployment of a SEA. Key parameters for current and future technologies have been collected from subject-matter experts and other documented sources in the Technology Tool Box (TTB). ATLAS can be used to compare the technical feasibility and economic viability of a set of technology choices for one SEA, and compare it against another set of technology choices or another SEA. System architecture modeling in ATLAS is a multi-step process. First, the modeler defines the system level requirements. Second, the modeler identifies technologies of interest whose impact on an SEA. Third, the system modeling team creates models of architecture elements (e.g. launch vehicles, in-space transfer vehicles, crew vehicles) if they are not already in the model library. Finally, the architecture modeler develops a script for the ATLAS tool to run, and the results for comparison are generated.

Thompson, Robert W.↗
NASA NTRS1995

Development of a two-equation turbulence model for hypersonic flows: Evaluation of a low Reynolds number correction to the Kappa - epsilon two equation compressible turbulence model - Volume 1

The objective of the current research is the development of an improved k-epsilon two-equation compressible turbulence model for turbulent boundary layer flows experiencing strong viscous-inviscid interactions. The development of an improved model is important in the design of hypersonic vehicles such as the National Aerospace Plane (NASP) and the High Speed Civil Transport (HSCT). Improvements have been made to the low Reynolds number functions in the eddy viscosity and dissipation of solenoidal dissipation of the k-epsilon turbulence mode. These corrections offer easily applicable modifications that may be utilized for more complex geometries. The low Reynolds number corrections are functions of the turbulent Reynolds number and are therefore independent of the coordinate system. The proposed model offers advantages over some current models which are based upon the physical distance from the wall, that modify the constants of the standard model, or that make more corrections than are necessary to the governing equations. The code has been developed to solve the Favre averaged, boundary layer equations for mass, momentum, energy, turbulence kinetic energy, and dissipation of solenoidal dissipation using Keller's box scheme and the Newton spatial marching method. The code has been validated by removing the turbulent terms and comparing the solution with the Blasius solution, and by comparing the turbulent solution with an existing k-epsilon model code using wall function boundary conditions. Excellent agreement is seen between the computed solution and the Blasius solution, and between the two codes. The model has been tested for both subsonic and supersonic flat-plate turbulent boundary layer flow by comparing the computed skin friction with the Van Driest II theory and the experimental data of Weighardt; by comparing the transformed velocity profile with the data of Weighardt, and the Law of the Wall and the Law of the Wake; and by comparing the computed results of an adverse pressure gradient with the experimental data of Fernando and Smits. Good agreement is obtained with the experimental correlations for all flow conditions.

Knight, Doyle D.↗
DOE OSTI2023

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete‐time uncertain linear systems is presented. We consider uncertain processes, affected by box‐bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.

97 MATHEMATICS AND COMPUTING↗