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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structural decomposition of merger-free galaxies hosting luminous AGNs

ABSTRACT Active galactic nucleus (AGN) growth in disc-dominated, merger-free galaxies is poorly understood, largely due to the difficulty in disentangling the AGN emission from that of the host galaxy. By carefully separating this emission, we examine the differences between AGNs in galaxies hosting a (possibly) merger-grown, classical bulge, and AGNs in secularly grown, truly bulgeless disc galaxies. We use galfit to obtain robust, accurate morphologies of 100 disc-dominated galaxies imaged with the Hubble Space Telescope. Adopting an inclusive definition of classical bulges, we detect a classical bulge component in $53.3 \pm 0.5$ per cent of the galaxies. These bulges were not visible in Sloan Digital Sky Survey photometry, however these galaxies are still unambiguously disc-dominated, with an average bulge-to-total luminosity ratio of $0.1 \pm 0.1$. We find some correlation between bulge mass and black hole mass for disc-dominated galaxies, though this correlation is significantly weaker in comparison to the relation for bulge-dominated or elliptical galaxies. Furthermore, a significant fraction ($\gtrsim 90$ per cent) of our black holes are overly massive when compared to the relationship for elliptical galaxies. We find a weak correlation between total stellar mass and black hole mass for the disc-dominated galaxies, hinting that the stochasticity of black hole–galaxy co-evolution may be higher in disc-dominated than bulge-dominated systems.

Fahey, Matthew J.↗
NASA NTRS1994

A structural design decomposition method utilizing substructuring

A new method of design decomposition for structural analysis and optimization is described. For this method, the structure is divided into substructures where each substructure has its structural response described by a structural-response subproblem, and its structural sizing determined from a structural-sizing subproblem. The structural responses of substructures that have rigid body modes when separated from the remainder of the structure are further decomposed into displacements that have no rigid body components, and a set of rigid body modes. The structural-response subproblems are linked together through forces determined within a structural-sizing coordination subproblem which also determines the magnitude of any rigid body displacements. Structural-sizing subproblems having constraints local to the substructures are linked together through penalty terms that are determined by a structural-sizing coordination subproblem. All the substructure structural-response subproblems are totally decoupled from each other, as are all the substructure structural-sizing subproblems, thus there is significant potential for use of parallel solution methods for these subproblems.

Scotti, Stephen J.↗
NASA NTRS2014

Distributed Prognostics based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

centrifugal pump↗
NASA NTRS1993

Neurocomputing strategies in decomposition based structural design

The present paper explores the applicability of neurocomputing strategies in decomposition based structural optimization problems. It is shown that the modeling capability of a backpropagation neural network can be used to detect weak couplings in a system, and to effectively decompose it into smaller, more tractable, subsystems. When such partitioning of a design space is possible, parallel optimization can be performed in each subsystem, with a penalty term added to its objective function to account for constraint violations in all other subsystems. Dependencies among subsystems are represented in terms of global design variables, and a neural network is used to map the relations between these variables and all subsystem constraints. A vector quantization technique, referred to as a z-Network, can effectively be used for this purpose. The approach is illustrated with applications to minimum weight sizing of truss structures with multiple design constraints.

Szewczyk, Z.↗
DOE OSTI2019

Spatial decomposition of structured grids for nuclear reactor simulations

Spatial decomposition methods based on graph partitioning are developed and implemented in the high fidelity neutron transport code MPACT. These graph-based spatial decomposition methods are more general than previous decomposition methods and typically provide better load balance and reduced runtimes due to their improved parallel efficiency. Correlations are drawn between simulation runtime and the balance of the partition for 2D simulations. Comparisons are made using partition balance metrics for different decomposition schemes in 2D and 3D simulations. Finally, for typical ranges of subdomains, graph-based partitioning methods offer significant reductions to runtimes. However, for highly decomposed problems, these graph-based methods may decrease convergence rates, thus reducing parallel efficiency compared to older methods.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1985

Application of singular value decomposition to structural dynamics systems with constraints

Singular value decomposition is used to construct a coordinate transformation for a linear dynamic system subject to linear, homogeneous constraint equations. The method is compared with two commonly used methods, namely classical Gaussian elimination and Walton-Steeves approach. Although the classical method requires fewer numerical operations, the singular value decomposition method is more accurate and convenient in eliminating the dependent coordinates. Numerical examples are presented to demonstrate the application of the method.

Juang, J.-N.↗
NASA NTRS2013

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

Structural Decomposition↗
NASA NTRS2011

Improving Distributed Diagnosis Through Structural Model Decomposition

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

Bregon, Anibal↗
NASA NTRS1993

Optimization by decomposition in structural and multidisciplinary applications

An algorithm for a general, multilevel structural optimization by substructuring is derived, based on the linear decomposition concept that is rooted in the Bellman's Optimality Criterion enhanced with the optimum sensitivity derivatives used as a means to account for coupling among the subproblems, each of which is limited to optimization of a substructure. The algorithm applies also to those multidisciplinary problems whose subproblems form a hierarchy similar to that of substructures. In systems where the subproblems communicate with each other at the same level, the decomposition becomes non-hierarchic and the system may be optimized as a whole based on the derivatives of the system behavior with respect to the design variables computed by a method that bypasses finite differencing on the system analysis. When a multidisciplinary system includes a structure as its part, a hybrid, hierarchic/non-hierarchic decomposition applies. Numerical examples and references to computational experience accumulated to date illustrate the discussion.

Sobieszczanski-Sobieski, Jaroslaw↗
NASA NTRS2014

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.

Daigle, Matthew↗
NASA NTRS2015

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

structural model decomposition↗
NASA NTRS2013

A Structural Model Decomposition Framework for Systems Health Management

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

Roychoudhury, Indranil↗
DOE OSTI2023

Long-Term Stability of Ferri-/Ferrocyanide as an Electroactive Component for Redox Flow Battery Applications: On the Origin of Apparent Capacity Fade

We assess the suitability of potassium ferri-/ferrocyanide as an electroactive species for long-term utilization in aqueous organic redox flow batteries. A series of electrochemical and chemical characterization experiments was performed to distinguish between structural decomposition and apparent capacity fade of ferri-/ferrocyanide solutions used in the capacity-limiting side of a flow battery. Our results indicate that, in contrast with previous reports, no structural decomposition of ferri-/ferrocyanide occurs at tested pH values as high as 14 in the dark or in diffuse indoor light. Instead, an apparent capacity fade takes place due to a chemical reduction of ferricyanide to ferrocyanide, via chemical oxygen evolution reaction. We find that this parasitic process can be further exacerbated by carbon electrodes, with apparent capacity fade rates at pH 14 increasing with an increased ratio of carbon electrode surface area to ferricyanide in solution. Based on these results, we report a set of operating conditions that enables the long-duration cycling of alkaline ferri-/ferrocyanide electrolytes and demonstrate how apparent capacity fade rates can be engineered by the initial system setup. If protected from direct exposure to light, the structural stability of ferri-/ferrocyanide anions allows for their practical deployment as electroactive species in long duration energy storage applications.

25 ENERGY STORAGE↗
NASA NTRS1986

Numerical difficulties associated with using equality constraints to achieve multi-level decomposition in structural optimization

There has been recent interest in multidisciplinary multilevel optimization applied to large engineering systems. The usual approach is to divide the system into a hierarchy of subsystems with ever increasing detail in the analysis focus. Equality constraints are usually placed on various design quantities at every successive level to ensure consistency between levels. In many previous applications these equality constraints were eliminated by reducing the number of design variables. In complex systems this may not be possible and these equality constraints may have to be retained in the optimization process. In this paper the impact of such a retention is examined for a simple portal frame problem. It is shown that the equality constraints introduce numerical difficulties, and that the numerical solution becomes very sensitive to optimization parameters for a wide range of optimization algorithms.

Thareja, R.↗
NASA NTRS2011

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

Daigle, Matthew↗
NASA NTRS2016

Synthesis, Structure, Characterization, and Decomposition of Nickel Dithiocarbamates: Effect of Precursor Structure and Processing Conditions on Solid-State Products

Single-crystal X-ray structures of four nickel dithiocarbamate complexes, the homoleptic mixed-organic bis-dithiocarbamates Ni[S2CN(isopropyl)(benzyl)]2, Ni[S2CN(ethyl)(n-butyl)]2, and Ni[S2CN(phenyl)(benzyl)]2, as well as the heteroleptic mixed-ligand complex NiCl[P(phenyl)3][(S2CN(phenyl)(benzyl)], were determined. Synthetic, spectroscopic, structural, thermal, and sulfide materials studies are discussed in light of prior literature. The spectroscopic results are routine. A slightly distorted square-planar nickel coordination environment was observed for all four complexes. The organic residues adopt conformations to minimize steric interactions. Steric effects also may determine puckering, if any, about the nickel and nitrogen atoms, both of which are planar or nearly so. A trans-influence affects the Ni-S bond distances. Nitrogen atoms interact with the CS2 carbons with a bond order of about 1.5, and the other substituents on nitrogen display transoid conformations. There are no strong intermolecular interactions, consistent with prior observations of the volatility of nickel dithiocarbamate complexes. Thermogravimetric analysis of the homoleptic species under inert atmosphere is consistent with production of 1:1 nickel sulfide phases. Thermolysis of nickel dithiocarbamates under flowing nitrogen produced hexagonal or -NiS as the major phase; thermolysis under flowing forming gas produced millerite (-NiS) at 300 C, godlevskite (Ni9S8) at 325 and 350 C, and heazlewoodite (Ni3S2) at 400 and 450 C. Failure to exclude oxygen results in production of nickel oxide. Nickel sulfide phases produced seem to be primarily influenced by processing conditions, in agreement with prior literature. Nickel dithiocarbamate complexes demonstrate significant promise to serve as single-source precursors to nickel sulfides, a quite interesting family of materials with numerous potential applications.

microscopy↗
DOE OSTI2023

Towards COP27: Decarbonization patterns of residential building in China and India

As the two largest emerging emitters with the highest growth in operational carbon emissions from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the newest decomposing structural decomposition approach. Results indicate (1) the annual operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (–87.7 in China and – 130.2 kg of carbon dioxide (kgCO 2 ) per household in India) and appliances (~ –169.7 in China and ~ –43.4 kgCO 2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 M-tons of CO 2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO 2 per household) nearly caught up with China (182.5 kgCO 2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP28 1 age.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Pathway for decarbonizing residential building operations in the US and China beyond the mid-century

With global carbon budget targets looming, residential buildings in top economies must become carbon neutral as soon as possible to reserve more emission space for emerging carbon-emitting economies. This study is the first to compare the operational decarbonization process of China’s and the United States (US) residential buildings from 2000 to 2060 by combining the end-use emission model with the decomposing structural decomposition (DSD) method and Monte Carlo simulation. The results show that from 2001 to 2020 China decarbonized 1544 mega-tons of carbon dioxide (MtCO 2 ) and the US decarbonized 1848 MtCO 2 . In the business-as-usual scenario, China will hit its emission peak in 2031 (±3) with 934 (±61) MtCO 2 , while the US will maintain a lock-in level of 736 (±133) MtCO 2 since the 2030s. In the decarbonization scenario, operational carbon neutrality for residential buildings in 2060 is promoted by an increase in clean power generation proportion, building-integrated power generation level, building electrification level, and a reduction in end-use energy intensity, which will contribute 34.4 %, 21.4 %, 14.3 %, and 29.9 % in China and 32.9 %, 33.1 %, 8.2 %, and 25.8 % in the US, respectively. Especially, building-integrated power generation in China only costs about 40 % of what it costs in the US. Besides, high-decarbonization strategies for residential building operations are proposed as references for governments to formulate targeted climate policies. Overall, this study offers data benchmarks for buildings’ carbon neutrality of top economies to further promote synergistic carbon neutrality with the buildings of emerging economies in the age of Post COP27.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗