Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “space operations”

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.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling

Abstract. Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2020

Massive spinning bosons on the celestial sphere

A natural extension of the Pasterski-Shao-Strominger (PSS) prescription is described, enabling the map of Minkowski space amplitudes with massive spinning external legs to the celestial sphere to be performed. An integral representation for the conformal primary wave function (CPW) of massive spinning bosons on the celestial sphere is derived explicitly for spin-one and -two. By analogy with the spin-zero case, the spinning bulk-to-boundary propagator on Euclidean AdS is employed to extend the massive CPW integral representation to arbitrary integer spin, and to describe the appropriate inverse transform of massive spinning CPWs back to the plane wave basis in Minkowski space. Subsequently, a massive spin-s momentum operator representation on the celestial sphere is determined, and used in conjunction with known Lorentz generators to derive Poincaré symmetry constraints on generic massive spinning two-, three- and four-point celestial amplitude structures. Finally, as a consistency check, three-point Minkowski space amplitudes of two massless scalars and a spin-one or -two massive boson are explicitly mapped to the celestial sphere, and the resulting three-point function coefficients are confirmed to be in exact agreement with the results obtained from Poincaré symmetry constraints.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Classifying Topology in Photonic Heterostructures with Gapless Environments

Photonic topological insulators exhibit bulk-boundary correspondence, which requires that boundary-localized states appear at the interface formed between topologically distinct insulating materials. However, many topological photonic devices share a boundary with free space, which raises a subtle but critical problem as free space is gapless for photons above the light line. Here, we use a local theory of topological materials to resolve bulk-boundary correspondence in heterostructures containing gapless materials and in radiative environments. In particular, we construct the heterostructure’s spectral localizer, a composite operator based on the system’s real-space description that provides a local marker for the system’s topology and a corresponding local measure of its topological protection; both quantities are independent of the material’s bulk band gap (or lack thereof). Moreover, we show that approximating radiative outcoupling as material absorption overestimates a heterostructure’s topological protection. Importantly, as the spectral localizer is applicable to systems in any physical dimension and in any discrete symmetry class (i.e., any Altland-Zirnbauer class), our results show how to calculate topological invariants, quantify topological protection, and locate topological boundary-localized resonances in topological materials that interface with gapless media in general.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Limit Cycle Oscillating Detonation Wave Behavior Analysis Within a Rotating Detonation Engine

Limit cycle oscillation (LCO) detonation wave behaviors are presented and analyzed for test times exceeding 20 s in a water-cooled rotating detonation engine (RDE). LCO detonation waves exhibit cyclic acceleration and deceleration, resulting in oscillating wave spacing at unique process conditions. In previous RDE studies, similar behaviors have been studied as microsecond-scale instabilities leading to ascending or descending modal transitions. In the current work, however, LCO waves are considered a persistent wave mode, occupying unique portions of the operational envelope adjacent to those of their equally spaced counterparts. These occurrences of LCO waves are repeatable, enduring behaviors. A method to generate shifted contour surfaces specifically intended to extract and analyze wave spacing variation through time, termed limit cycle oscillation visualization (LCOV) surfaces, is presented. LCOV surfaces transform data into the reference frame of a primary traveling wave and are used to analyze quasi-steady, short-timescale, and transitional LCO modes. Results are leveraged to understand the relationship between fill height, wave strength, local wave acceleration, and subsequent LCO wave spacing for individual wave sets. In conclusion, quasi-steady LCO waves display wave spacing oscillations between equal spacing values associated with ±1 wave across runs exceeding 18 s.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Titanium Wire Arc Additive Manufacturing Inert Enclosure and Material Handling Safety Considerations

Wire arc additive manufacturing (WAAM) via metal inert gas (MIG)/gas metal arc welding (GMAW) is a viable option for fabrication of large-scale titanium parts; however, it introduces new safety hazards associated with both the material and the additional system hardware required for the process. Localized gas shielding of the weld arc via standard GMAW torch is inadequate for titanium due to its affinity for oxygen; thereby requiring the use of an inert enclosure to protect the weld from entraining oxygen. The use of the inert enclosure presents potential safety hazards such as operator asphyxiation and brings up discussion of confined space considerations. In addition, the titanium welding process creates pyrophoric titanium soot residue around the deposit, which can undergo deflagration during part cleaning and part removal. This paper provides an overview of the titanium WAAM process along with safety considerations for the design and operation of the inert enclosure as well as functional solutions for the safe handling of the titanium soot by-product.

Walters, Alex↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2020

Calibration and Initial Results of Space Radiation Dosimetry Using the iMESA-R

The integrated Miniaturized Electrostatic Analyzer-Reflight (iMESA-R) is a space weather instrument designed to measure plasma density, temperature, and spacecraft charging, along with total ionizing dose and dose rate. A constellation consisting of five nearly identical instruments has been designed and developed to act as science payloads hosted on-board Department of Defense satellites operating in low-Earth orbit. To validate the dosimetry component of the iMESA-R, a radiation test study was performed to calibrate the dosimeter and ascertain the attenuation due to the instrument aluminum housing. An 80 curie Cobalt-60 radioisotope source emitting beta, X-ray, and gamma ray radiation decay products was used to calibrate the dosimeter response as a function of distance and instrument shielding. We present results of the calibration study along with initial on-orbit data presented from the first operational iMESA-R hosted on-board Space Test Program Satellite 5 in a polar Earth orbit. The initial on-orbit data demonstrate the ability to map the radiation environment, particularly the South Atlantic Anomaly and the auroral regions using total ionizing dose rate due to electrons of E > 1.5 MeV and protons of E > 25 MeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Apparent fine tunings for field theories with broken space-time symmetries

We exhibit a class of effective field theories that have hierarchically small Wilson coefficients for operators that are not protected by symmetries but are not finely tuned. These theories possess bounded target spaces and vacua that break space-time symmetries. We give a physical interpretation of these theories as generalized solids with open boundary conditions. We show that these theories realize unusual RG flows where higher dimensional (seemingly irrelevant) operators become relevant even at weak coupling. Finally, we present an example of a field theory whose vacuum energy relaxes to a hierarchically small value compared to the UV cut-off.

Physics↗
DOE OSTI2025

Challenges and Optimization of Mu2e Proton Target Design with Radiative Cooling

Mu2e, the Muon-to-Electron Conversion Experiment, aims to identify physics beyond the Standard Model, namely, the conversion of muons to electrons without the emission of neutrinos. The muons are produced from pions generated in a production target when it is hit by an 8 GeV proton beam from the Fermilab Booster/Main Injector. The proton target design space is strongly constrained by a one-year operating lifetime and the need for radiative cooling in a vacuum environment. Uncertainties in the lifetime of the existing baseline design – a monolithic, segmented tungsten target – are large, particularly due to unknown impacts of radiation damage at the very high proton fluences expected in the experiment. We have begun evaluation of a new design utilizing Inconel 718 over the WL10 used in the existing target design. As a result, the structural design of the target has evolved significantly. This evolution focuses on lowering the target temperature, minimizing obstruction to muons, increasing structural stability, maximizing fatigue lifetime, simplifying the fabrication process, and more. The thermal management, structural stability and fatigue lifetime are emphasized here. These optimizations have led to a promising new target design for the Mu2e experiment.

Liu, Z. [Fermilab]↗
DOE OSTI2022

Spectra for Toeplitz Operators Associated with a Constrained Subalgebra

A two-point algebra is a set of bounded analytic functions on the unit disk that agree at two distinct points $a, b \in \mathbb{D}$. This algebra serves as a multiplier algebra for the family of Hardy Hilbert spaces $H^2_t := \{ f \in H^2 : tf(b)\}$ where $t \in \mathbb{C} \cup \{\infty\}$. We show that various spectra of certain Toeplitz operators acting on these spaces are connected.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks

There has been much recent interest in designing symmetry-aware neural networks (NNs) exhibiting relaxed equivariance. Such NNs aim to interpolate between being exactly equivariant and being fully flexible, affording consistent performance benefits. In a separate line of work, certain structured parameter matrices -- those with displacement structure, characterized by low displacement rank (LDR) -- have been used to design small-footprint NNs. Displacement structure enables fast function and gradient evaluation, but permits accurate approximations via compression primarily to classical convolutional neural networks (CNNs). In this work, we propose a general framework -- based on a novel construction of symmetry-based structured matrices -- to build approximately equivariant NNs with significantly reduced parameter counts. Our framework integrates the two aforementioned lines of work via the use of so-called Group Matrices (GMs), a forgotten precursor to the modern notion of regular representations of finite groups. GMs allow the design of structured matrices -- resembling LDR matrices -- which generalize the linear operations of a classical CNN from cyclic groups to general finite groups and their homogeneous spaces. We show that GMs can be employed to extend all the elementary operations of CNNs to general discrete groups. Further, the theory of structured matrices based on GMs provides a generalization of LDR theory focussed on matrices with cyclic structure, providing a tool for implementing approximate equivariance for discrete groups. We test GM-based architectures on a variety of tasks in the presence of relaxed symmetry. We report that our framework consistently performs competitively compared to approximately equivariant NNs, and other structured matrix-based compression frameworks, sometimes with a one or two orders of magnitude lower parameter count.

Samudre, Ashwin↗
DOE OSTI2022

Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems

Koopman operator theory provides an alternative to study nonlinear networked dynamical systems by mapping the state space to an abstract higher dimensional space where the system evolution is linear. Recent works show the application of graph neural networks (GNNs) to learn state to object-centric embeddings and achieve centralized block-wise computation of Koopman operator (KO) under additional assumptions on the underlying agents properties and constraints on the KO structure. However, the computational complexity of learning the Koopman increases exponentially for networked systems where the number of possible system states grows in a combinatorial fashion with the number of nodes. The learning challenge is further amplified for sparse networks by two factors: 1) sample sparsity for learning the Koopman operator in the non-linear space, and 2) the divergence in the dynamics of individual nodes or from one subgraph to another. Our work aims to address these challenge by formulating the representation learning of networked dynamical systems into a multi-agent paradigm and learning the Koopman operator in a distributive manner. The computational as well as performance advantages of distributed Koopman is predominant for sparse networks whereas for fully connected networks, it is shown to coincide with the centralized one. The empirical study on rope system, network of oscillators and a synthetic power system show comparable and superior performance along with computational benefits with the state-of-the-art methods.

Mukherjee, Sayak↗
DOE OSTI2021

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations

In this work, we propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electron–Nucleus Hyperfine Coupling Calculated from Restricted Active Space Wavefunctions and an Exact Two-Component Hamiltonian

Exact two-component (X2C) relativistic nuclear hyperfine magnetic field operators were incorporated in X2C ab-initio wavefunction calculations at the multi-reference restricted active space (RAS) level for calculations of nuclear hyperfine magnetic properties. Spin-orbit coupling was treated via RAS state interaction (SO-RASSI). The method was tested by calculations of electron – nucleus hyperfine coupling constants. The approach, implemented in the OpenMolcas program, overcomes a major limitation of a previous SO-RASSI implementation for hyperfine coupling that relied on non-relativistic hyperfine operators [J. Chem. Theor. Comput. 2015, 11, 538–549] and therefore had only limited applicability. Furthermore, results from calculations on systems with light and heavy main group elements, transition metals, lanthanides, and one actinide complex, demonstrate reasonably good agreement with experimental data, where available, as long as the active space can generate sufficient spin polarization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Accurate numerical, integral methods for computing drift-kinetic Trubnikov-Rosenbluth potentials

A novel numerical method is employed to compute the integral form of the axi-symmetric Trubnikov-Rosenbluth potentials. Two methods for quadrature in pitch-angle are described and their convergence properties are studied. Careful attention is given to quadrature over a singular Green's function. Here it is shown that an infinite series representation of the Green's function can be used more efficiently than its closed form involving complete elliptic integrals. Then a collocation method in speed, with its associated quadrature scheme, is laid out and its convergence properties are studied. Using the proposed scheme, accurate low-order moments of the field collision operator are obtained using relatively few velocity space degrees of freedom. The scheme is showcased by solving for the equilibrium, axi-symmetric bootstrap current in tokamaks. A C 0 Gauss-Lobatto-Legendre finite element pitch-angle basis with vertex nodes at the trapped/passing boundary is shown, in the context of the integral methods used, to be much more efficient than the more common Legendre polynomial expansion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. Furthermore, the window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

36 MATERIALS SCIENCE↗