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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A parallel algorithm for the eigenvalues and eigenvectors for a general complex matrix

A new parallel Jacobi-like algorithm is developed for computing the eigenvalues of a general complex matrix. Most parallel methods for this parallel typically display only linear convergence. Sequential norm-reducing algorithms also exit and they display quadratic convergence in most cases. The new algorithm is a parallel form of the norm-reducing algorithm due to Eberlein. It is proven that the asymptotic convergence rate of this algorithm is quadratic. Numerical experiments are presented which demonstrate the quadratic convergence of the algorithm and certain situations where the convergence is slow are also identified. The algorithm promises to be very competitive on a variety of parallel architectures.

Shroff, Gautam↗
NASA NTRS2001

Validation of a High-Order Compact Code for Nonlinear Flows About Complex Geometric

The slides review computational requirements for nozzle exhaust flow and noise calculations and the current numerical method, validation of prefactored compact scheme on CAA benchmark problems, a curvilinear grid performance test of gust response of a Joukowski airfoil, airfoil surface RMS pressure distribution and far field noise radiation results for Joukowski airfoil in a vortical gust, boundary distance study for Joukowski airfoil problem, and performance of ICOMP parallel Macintosh cluster.

Hixon, Ray↗
NASA NTRS1981

An evaluation of a simplified near field noise model for supersonic helical tip speed propellers

Existing propeller noise models are versatile and complex but require large computational times, therefore a simplified noise model that could be used to obtain quick noise estimates for these propellers was evaluated. This simplified noise model compared favorably with a complex model for a straight blade propeller and for swept propeller blades when the propeller sweep was properly considered. The simplified model can thus be used as an approximation to the complex model. Comparisons of either the complex or simplified noise models with the available noise data are not good for supersonic propeller helical tip speeds. By adjusting various constants in the simplified model, the noise estimates can be brought into the same range as the data at the propeller design point but the variation of the model with helical tip Mach number remains different than the data.

Dittmar, J. H.↗
DOE OSTI2014

Computer-Aided Construction of Chemical Kinetic Models

The combustion chemistry of even simple fuels can be extremely complex, involving hundreds or thousands of kinetically significant species. The most reasonable way to deal with this complexity is to use a computer not only to numerically solve the kinetic model, but also to construct the kinetic model in the first place. Because these large models contain so many numerical parameters (e.g. rate coefficients, thermochemistry) one never has sufficient data to uniquely determine them all experimentally. Instead one must work in “predictive” mode, using theoretical rather than experimental values for many of the numbers in the model, and as appropriate refining the most sensitive numbers through experiments. Predictive chemical kinetics is exactly what is needed for computer-aided design of combustion systems based on proposed alternative fuels, particularly for early assessment of the value and viability of proposed new fuels before those fuels are commercially available. This project was aimed at making accurate predictive chemical kinetics practical; this is a challenging goal which requires a range of science advances. The project spanned a wide range from quantum chemical calculations on individual molecules and elementary-step reactions, through the development of improved rate/thermo calculation procedures, the creation of algorithms and software for constructing and solving kinetic simulations, the invention of methods for model-reduction while maintaining error control, and finally comparisons with experiment. Many of the parameters in the models were derived from quantum chemistry calculations, and the models were compared with experimental data measured in our lab or in collaboration with others.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Comparison of Artemis 2 and Artemis 5 Model Outcomes Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

probabilistic risk assessment↗
DOE OSTI2023

Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective

Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Reducing Communication in Algebraic Multigrid Using Additive Variants

Algebraic multigrid (AMG) has proven to be an effective scalable solver on many high performance computers. However, its increasing communication complexity on coarser levels has shown to seriously impact its performance on computers with high communication cost. Moreover, additive AMG variants provide not only increased parallelism as well as decreased numbers of messages per cycle but also generally exhibit slower convergence. Here we present various new additive variants with convergence rates that are significantly improved compared to the classical additive algebraic multigrid method and investigate their potential for decreased communication, and improved communication-computation overlap, features that are essential for good performance on future exascale architectures.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Planning Complex Sequences Using Compressed Representations

A method that notably includes the use of compressed representations interleaved with non-compressed (time-line) representations of a general scheduling problem has been conceived as a means of increasing, by orders of magnitude, the speeds of computations needed for scheduling complex sequences of activities that include cycles wherein subsets of the activities and/or sequences are repeated. The method was originally intended to be used in scheduling large campaigns of scientific observations by instruments aboard a spacecraft. A typical such campaign could include observations of millions of targets, many observations to be made during long repeated passes. The method would also be useful on Earth for scheduling complex sequences of activities that include cycles.

Chien, Steve↗
DOE OSTI2025

Learning quantum computers' errors using interpretable neural networks

Learning and reducing the errors and noise in quantum computing systems is necessary for achieving quantum computation’s promise. However, rapid advances in experimental quantum computing are making this task increasingly difficult, because state-of-the-art systems now contain hundreds of qubits and many characterization techniques are hard to apply at this scale. Furthermore, complex kinds of errors in these systems, such as crosstalk and non-Markovian effects, must be understood and decreased, but these errors are challenging to study with most existing methods. In this project, we explored using neural networks for scalable characterization of complex errors in quantum computers. We proposed and demonstrated characterizing a quantum computer’s errors with neural networks that have interpretable parameters corresponding to the rates of different kinds of errors, within a sparse Lindbladian parameterization for errors. To enable scaling to many qubit systems, these networks then predict how these errors combine within quantum circuits and impact their outcomes using an efficient approximations. We demonstrated these networks ability to learn coherent crosstalk errors and context-dependent errors in a simulated 4-qubit system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Near-wall model for compressible turbulent boundary layers based on an inverse velocity transformation

In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation (Griffin et al., Proc. Natl Acad. Sci., vol. 118, 2021, p. 34) and an algebraic temperature–velocity relation, is developed for high-speed turbulent boundary layers. As input, the model requires the mean flow state at one wall-normal height in the inner layer of the boundary layer and at the boundary-layer edge. As output, the model can predict mean temperature and velocity profiles across the entire inner layer, as well as the wall shear stress and heat flux. The model is tested in an a priori sense using a wide database of direct numerical simulation high-Mach-number turbulent channel flows, pipe flows and boundary layers (48 cases, with edge Mach numbers in the range 0.77–11, and semi-local friction Reynolds numbers in the range 170–5700). The present model is significantly more accurate than the classical ordinary differential equation (ODE) model for all cases tested. The model is deployed as a wall model for large-eddy simulations in channel flows with bulk Mach numbers in the range 0.7–4 and friction Reynolds numbers in the range 320–1800. When compared to the classical framework, in the a posteriori sense, the present method greatly improves the predicted heat flux, wall stress, and temperature and velocity profiles, especially in cases with strong heat transfer. In addition, the present model solves one ODE instead of two, and has a computational cost and implementation complexity similar to that of the commonly used ODE model.

42 ENGINEERING↗
DOE OSTI2023

Deciphering the Mechanism of Base-Triggered Conversion of Ammonia to Molecular Nitrogen and Methylamine to Cyanide

Here we report an in-depth investigation into the ammonia oxidation mechanism by the catalyst [Ru III (tpy)(dmabpy)NH 3 ] 3 + ([Ru(NH 3 )] 3 + ). Stoichiometric reactions of [Ru(NH 3 )] 3 + were carried out with exogenous noncoordinating bases to trigger a proposed redox disproportionation reaction, which was followed using variable-temperature NMR spectroscopy. An intermediate species was identified as a dinitrogen-bridged complex using 15 N NMR and Raman spectroscopy on isotopically labeled complexes. This intermediate is proposed to derive from coupling of nitridyl species formed upon sequential redox disproportion reactions. Acetonitrile displaces the dinitrogen bridge to yield free N 2 . DFT calculations support this lower-energy pathway versus that previously reported for ammonia oxidation by the parent [Ru III (tpy)(bpy)NH 3 ] 3 + complex. These experimental and computational results are consistent with the interpretation of redox disproportionation involving sequential hydrogen atom transfer reactions by an amide/aminyl intermediate, [Ru(NH 2 ) – ] + $\Leftrightarrow$ [Ru(NH 2 ) • ] + , formed upon deprotonation of the parent complex. Control experiments employing a large excess of ammonia as a base indicate this new proposed lower-energy pathway contributes to the oxidation of ammonia to dinitrogen in conditions relevant to electrocatalysis. In addition, analogous methylamine complexes, [Ru(NH 2 CH 3 )] 2 + /3 + , were prepared to further test the proposed mechanism. Treating [Ru(NH 2 CH 3 )] 3 + with a base cleanly yields two products [Ru(NH 2 CH 3 )] 2 + and [Ru(CN)] + in an ~3:1 ratio, fully consistent with the proposed cascade of hydrogen atom transfer reactions by an intermediate.

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

Medial axis and local thickness computation using the Fast Sweeping Method

This report describes an efficient and robust voxel-based methodology for computing the medial axis, local thickness, and distance-to-skeleton of arbitrary three-dimensional geometries. It is assumed that the object can be represented by an exact or approximate signed distance function on a discrete grid. The gradient of such function is used to formulate a hyperbolic partial differential equation (PDE) that models the collapse of the position vector in space. By exploiting the causality property of the PDE, the Fast Sweeping Method is able to obtain the solution in a finite number of sweeps independent of the mesh resolution. The intersection of characteristic lines leads to the formation of shocks and a discrete bisector function is used to identify the medial axis. The same PDE approach is used to compute the local thickness inside the object and obtain the distance-to-skeleton field. Multiple examples are given in two and three dimensions along with a resolution study. The methodology has optimal complexity and yields subsecond computational times for geometries with over a million zones on a single core. The methodology is also capable of parallelization across shared and distributed memory architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1978

A fast complex integer convolution using a hybrid transform

It is shown that the Winograd transform can be combined with a complex integer transform over the Galois field GF(q-squared) to yield a new algorithm for computing the discrete cyclic convolution of complex number points. By this means a fast method for accurately computing the cyclic convolution of a sequence of complex numbers for long convolution lengths can be obtained. This new hybrid algorithm requires fewer multiplications than previous algorithms.

Reed, I. S.↗
DOE OSTI2017

A characterization of workflow management systems for extreme-scale applications

We present that the automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. Finally, the paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments

In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Chimera grids in the simulation of three-dimensional flowfields in turbine-blade-coolant passages

When computing flows inside geometrically complex turbine-blade coolant passages, the structure of the grid system used can affect significantly the overall time and cost required to obtain solutions. This paper addresses this issue while evaluating and developing computational tools for the design and analysis of coolant-passages, and is divided into two parts. In the first part, the various types of structured and unstructured grids are compared in relation to their ability to provide solutions in a timely and cost-effective manner. This comparison shows that the overlapping structured grids, known as Chimera grids, can rival and in some instances exceed the cost-effectiveness of unstructured grids in terms of both the man hours needed to generate grids and the amount of computer memory and CPU time needed to obtain solutions. In the second part, a computational tool utilizing Chimera grids was used to compute the flow and heat transfer in two different turbine-blade coolant passages that contain baffles and numerous pin fins. These computations showed the versatility and flexibility offered by Chimera grids.

Stephens, M. A.↗