SEARCH · Search NASA
Results for “Mathematics & Computing”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Local rewiring algorithms to increase clustering and grow a small world
Abstract Many real-world networks have high clustering among vertices: vertices that share neighbours are often also directly connected to each other. A network’s clustering can be a useful indicator of its connectedness and community structure. Algorithms for generating networks with high clustering have been developed, but typically rely on adding or removing edges and nodes, sometimes from a completely empty network. Here, we introduce algorithms that create a highly clustered network by starting with an existing network and rearranging edges, without adding or removing them; these algorithms can preserve other network properties even as the clustering increases. They rely on local rewiring rules, in which a single edge changes one of its vertices in a way that is guaranteed to increase clustering. This greedy step can be applied iteratively to transform a random network into a form with much higher clustering. Additionally, the algorithms presented grow a network’s clustering faster than they increase its path length, meaning that network enters a regime of comparatively high clustering and low path length: a small world. These algorithms may be a basis for how real-world networks rearrange themselves organically to achieve or maintain high clustering and small-world structure.
Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow
In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.
Immersive Particle Advection: Through the Scales of Renewable Energy: Preprint
We describe the benefits of immersive flow analysis for three large-scale computational science studies in the field of renewable energy. The studies encompass a range of scales, spanning from the large atmospheric scale of a wind farm to the human scale of an electric vehicle cabin down to the microscopic scale of battery material science. In these studies, users explored the flow patterns and dynamics through immersive particle advection. The integration of high-performance computing with immersive analysis provided a deeper understanding of these systems, helping develop more effective solutions for a sustainable energy future.
Topological Analysis of The SPOKE Graph
The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).
Requirements Framework for Cyber-Informed Engineering
The existing requirements frameworks or models cater to the existing practices. However, they fall short in directly using them for Cyber informed engineering. Therefore, this paper develops and presents a comprehensive framework that can be used for Cyber informed Engineering during the requirements phase. We describe the existing requirements process and models, then develop a requirements framework for cyber informed engineering.
Developing Open-Source Tools for Increasing the Efficiency of Synthetic Aviation Turbine Fuel Certification Process
FuelLib is an open-source Python-based fuel library, developed by NREL, that leverages the group contribution method (GCM) of [1] to systematically estimate the thermodynamic and transport properties of hydrocarbon fuels. FuelLib predicts these properties based on the molecular structure of individual compounds or compound families, using weight percentages of a fuel's composition, typically measured using techniques such as gas chromatography (GC). FuelLib enables property estimation over a wide range of temperatures and pressures of multi-component fuels in the absence of detailed molecular composition data, making it particularly valuable for complex fuel mixtures where detailed experimental characterization of fuel composition is unavailable. These capabilities contribute directly to synthetic aviation turbine fuels (SATF) development, supporting the short-term American Society for Testing and Materials (ASTM) qualification of drop-in fuels while potentially expanding ASTM boundaries to certify a broader range of fuels.
Structural Calculations & Details for Installation of the Xradia UltraXRM-L200 in B327-Room B271K
The purpose of the enclosed calculations and details is to document seismic restraint of the XRM scanner, generator unit, and a helium tank stand.
Projection multiplex recording of computer-synthesised one-dimensional Fourier holograms for holographic memory systems: mathematical and experimental modelling
A multiplex method of recording computer-synthesised one-dimensional Fourier holograms intended for holographic memory devices is proposed. The method potentially allows increasing the recording density in the previously proposed holographic memory system based on the computer synthesis and projection recording of data page holograms. (holographic memory)
Key word in context index and bibliography on computer systems simulation and evaluation
Index program for bibliography on systems evaluation, simulation languages, mathematical models, and computer simulation techniques
Development of a Model and Computer Code to Describe Solar Grade Silicon Production Processes
The program aims at developing mathematical models and computer codes based on these models, which allow prediction of the product distribution in chemical reactors for converting gaseous silicon compounds to condensed-phase silicon. The major interest is in collecting silicon as a liquid on the reactor walls and other collection surfaces. Two reactor systems are of major interest, a SiCl4/Na reactor in which Si(l) is collected on the flow tube reactor walls and a reactor in which Si(l) droplets formed by the SiCl4/Na reaction are collected by a jet impingement method. During this quarter the following tasks were accomplished: (1) particle deposition routines were added to the boundary layer code; and (2) Si droplet sizes in SiCl4/Na reactors at temperatures below the dew point of Si are being calculated.
Rarely-taught critical skills for successful math and computational work in industry/government-sector
Presentation to students from the University of Arizona college of Mathematical Sciences on industry job skills relevant to math, data science, and statistics.
Efficient computation of basic sums for random polydispersed composites
The main goal of the paper was to develop algorithms and methods for computation of basic sums, the mathematical objects of great importance in computational materials science having applications to description of the representative volume element (RVE) and to the effective properties of 2D composites. The previously used algorithm had the exponential complexity. We propose a linearly complex algorithm. All the presented algorithms can be easily implemented in modern scientific computing packages, while maintaining both efficient calculations and a high level of abstraction. The proposed approach is applied to derivation of a polynomial approximation of the effective conductivity formula for 2D random material with non-overlapping circular inclusions with normally distributed radii. The obtained formulas are applied to the optimal packing problem of disks on the plane.
Analysis of tumor-immune functional responses in a mathematical model of neoantigen cancer vaccines
Cancer neoantigen vaccines have emerged as a promising approach to stimulating the immune system to fight cancer. We propose a simple model including key elements of cancer-immune interactions and conduct a phase plane analysis to understand the immunological mechanisms of cancer neoantigen vaccines. Analytical results are obtained for two widely used functional forms that represent the killing rate of tumor cells by immune cells: the law of mass action (LMA) and the dePillis-Radunskaya Law (LPR). Using the LMA, our results reveal that a slowly growing tumor can escape the immune surveillance and that there is a unique periodic solution. The LPR offers richer dynamics, in which tumor elimination and uncontrolled tumor growth are both present. We show that tumor elimination requires sufficient number of initial activated T cells in relationship to the malignant cells, which lends support to using the neoantigen cancer vaccine as an adjuvant therapy after the primary tumor is surgically removed or treated using radiotherapy. We also derive a sufficient condition for uncontrolled tumor growth under the assumption of the LPR. Here, the juxtaposition of analyses with these two different choices for the killing rate function highlights their importance on model behavior and biological implications, by which we hope to spur further theoretical and experimental work to understand mechanisms underlying different functional forms for the killing rate.
Special Issue: Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics
Microbial communities are networks of species, the interaction structure of which dynamically reorganizes in a varying environment. Even in a static condition, community dynamics are often difficult to predict due to highly nonlinear interspecies interactions. Understanding fundamental principles of microbial interactions is therefore key for predicting and harnessing community function and properties. As extensively reviewed previously, mathematical models and computational methods that can predictively link interactions to community behaviors are indispensable tools for achieving this goal. This Special Issue of Processes collects eleven papers from lead scientists and researchers active in the area under the topic of “Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics.” The collected papers cover various topics of interest: 1) two review/opinion papers discussing recent advances in biofilm modeling and specific issues for successful collaboration between experimentalists and theorists, 2) one paper on the dynamics of complex environmental communities, 3) six papers dealing with fundamental aspects of microbial interactions and stability in model communities, and 4) two papers on the development and utilization of microbial consortia for biotechnology applications.
An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models
Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.
Mesh Convergence Issues
(U) This document is written to describe issues that impact on the ability of codes to converge to a unique answer. Several issues that could be discussed in the area of mesh convergence and resolution the topics include the resolution of geometry, stress gradients, shock propagation, material softening, and localization of deformation. After a quick discussion of what mesh convergence means, this paper presents a review of some of the criteria for mesh convergence. With an introductory level understanding of these criteria, two problems are presented to demonstrate convergence and non-convergence. The first example is a simple convergent hemisphere of gas, known as the Noh problem. With this problem convergence is shown. The Noh problem is also used to investigate the impact of mesh stiffening, or viscosity. The second problem is that of an explosively driven hemisphere. The hemisphere made of U6Nb with an inner radius of 40 mm and an outer radius of 41 mm was filled with PBX9501 and tested. Test results included surface temperature and information on the breakup, such as distribution of fines. Convergence computational methods are discussed along with some proposals for obtaining mesh convergence for a wide range of applications.
Institutional Computing use for CASL Studies of Multiphase Momentum Closure
No abstract provided.