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DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2001

Benthic Marine Cyanobacterial Mat Ecosystems: Biogeochemistry and Biomarkers

Cyanobacterial mats are complete ecosystems that can include processes of primary production, diagenesis and lithification. Light sustains oxygenic photosynthesis, which in turn provides energy, organic matter and oxygen to the community. Due to both absorption and scattering phenomena, incident light is transformed with depth in the mat, both in intensity and spectral composition. Mobile photo synthesizers optimize their position with respect to this light gradient. When photosynthesis ceases at night, the upper layers of the mat become reduced and sulfidic. Counteracting gradients of oxygen and sulfide combine to provide daily-contrasting environments separated on a scale of a few mm. The functional complexity of mats, coupled with the highly proximal and ordered spatial arrangement of biota, offers the potential for a staggering number of interactions. At a minimum, the products of each functional group of microorganisms affect the other groups both positively and negatively. For example, cyanobacteria generate organic matter (potential substrates) but also oxygen (a toxin for many anaerobes). Anaerobic activity recycles nutrients to the photosynthesizers but also generates potentially toxic sulfide. The combination of benefits and hazards of light, oxygen and sulfide promotes the allocation of the various essential mat processes between light and dark periods, and to various depths in the mat. Observations of mats have produced numerous surprises. For example, obligately anaerobic processes can occur in the presence of abundant oxygen, highly reduced gases are produced in the presence of abundant sulfate, meiofauna thrive at high sulfide concentrations, and the mats' constituent populations respond to environmental changes in complex ways. While photosynthetic bacteria dominate the biomass and productivity of the mat, nonphotosynthetic, anaerobic processes constitute the ultimate biological filter on the ecosystem's emergent biosignatures, including those sedimentary textures, organic compounds, and minerals that enter the fossil record. The ability of cyanobacterial mats to channel abundant solar energy into the creation and maintenance of complex structures and processes has created a multitude of consequences, both for sedimentation and for the early evolution of our biosphere.

DesMarais, David J.↗

NASA NTRS1972

Equatorial electron energy and number densities in the Jovian magnetosphere

A synchrotron model with a Maxwellian energy distribution of the form e to the (-E/E sub 0) power is used in a comparison with spatially resolved radio interferometric measurements of the Jovian emission. The observations of the decimeter radiation as a function of equatorial distance at 10.4 and 21 cm wavelength were reduced to source emission/cc of source electrons in each of 16 concentric rings. The peak energies for isotropically distributed electrons exceeded the maximum energy for flat orbiting electrons, and the peaks were generally located from 2.25 to 3 Jupiter radii. Beyond 3 radii, the order of magnitude on number density became a sensitive function of pitch angle distribution. The total equatorial intensities at 75 cm wavelength were computed for (E sub 0)(r) and n(r) at different values of B sub 0. The radiative half life for electrons of initial energy E sub 0 in a dipole field was calculated and found to be nearly constant at one year or less for altitudes at and below the position in peak energy.

Luthey, J. L.↗

DOE OSTI2023

Quantum Many-Body Theory from a Solution of the N -Representability Problem

Here, in this study, we present a many-body theory based on a solution of the N-representability problem in which the ground-state two-particle reduced density matrix (2-RDM) is determined directly without the many-particle wave function. We derive an equation that re-expresses physical constraints on higher-order RDMs to generate direct constraints on the 2-RDM, which are required for its derivation from an N-particle density matrix, known as N-representability conditions. The approach produces a complete hierarchy of 2-RDM constraints that do not depend explicitly upon the higher RDMs or the wave function. By using the two-particle part of a unitary decomposition of higher order constraint matrices, we can solve the energy minimization by semidefinite programming in a form where the low-rank structure of these matrices can be potentially exploited. We illustrate by computing the ground-state electronic energy and properties of the H 8 ring.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

On the Verification of Deep Reinforcement Learning Solution for Intelligent Operation of Distribution Grids

Capabilities of deep reinforcement learning (DRL) in obtaining fast decision policies in high dimensional and stochastic environments have led to its extensive use in operational research, including the operation of distribution grids with high penetration of distributed energy resources (DER). However, the feasibility and robustness of DRL solutions are not guaranteed for the system operator, and hence, those solutions may be of limited practical value. This paper proposes an analytical method to find feasibility ellipsoids that represent the range of multi-dimensional system states in which the DRL solution is guaranteed to be feasible. Empirical studies and stochastic sampling determine the ratio of the discovered to the actual feasible space as a function of the sample size. In addition, the performance of logarithmic, linear, and exponential penalization of infeasibility during the DRL training are studied and compared in order to reduce the number of infeasible solutions

Hosseini, Mohammad Mehdi↗

DOE OSTI2026

High-Fidelity, Low-Dissipation/Symmetry-Preserving Numerical Scheme for Solving the Euler Equations with Unstructured, Metric-Based Mesh Adaptation

This work presents an overview of a high-fidelity compressible Euler solver that utilizes the continuous Galerkin (CG) method with added artificial numerical diffusion for stabilization to solve a variety of unsteady and steady benchmark inviscid flow problems. This work shows that discretizing the Euler equations with this CG approach and first order basis functions produces a cost-effective stencil as well as simple well-posed boundary conditions. We show through convergence testing with manufactured solutions that the reduced stencil of CG, combined with the low amount of artificial diffusion required when using the stabilization method outlined in this work, leads to stable and highly accurate results for a variety of unsteady and steady applications. When combined with the adaptive mesh refinement approach used for many of the cases in this work, our results show that the flow solver achieves even more accurate results. A variety of inviscid flow cases are presented in this work, including transient 2D cases with complex shock structures and several steady 3D airfoils sections with a constant span.

Doetsch, Kevin [ORNL] (ORCID:0000000267051705)↗

NASA NTRS1978

Applied Routh approximation

The Routh approximation technique for reducing the complexity of system models was applied in the frequency domain to a 16th order, state variable model of the F100 engine and to a 43d order, transfer function model of a launch vehicle boost pump pressure regulator. The results motivate extending the frequency domain formulation of the Routh method to the time domain in order to handle the state variable formulation directly. The time domain formulation was derived and a characterization that specifies all possible Routh similarity transformations was given. The characterization was computed by solving two eigenvalue-eigenvector problems. The application of the time domain Routh technique to the state variable engine model is described, and some results are given. Additional computational problems are discussed, including an optimization procedure that can improve the approximation accuracy by taking advantage of the transformation characterization.

Merrill, W. C.↗

NASA NTRS1982

On the modelling and simulation of the dynamics and control of large flexible orbiting systems

This paper attempts to review the steps involved in the development of mathematical models that can be used to simulate the in-orbit dynamics of large flexible systems. The use of graph theoretic techniques can often be used to reduce the computational effort involved for calculating the eigenvalues of large ordered systems. Computer generated graphical techniques may provide additional insight into the understanding of elastic modal shape functions of complex systems. Finally the numerical techniques commonly used to develop shape and attitude control laws will be briefly reviewed.

Bainum, P. M.↗

NASA NTRS1992

Comparison of heating protocols for detection of disbonds in lap joints

A thermographic technique is presented which is shown to be effective for disbond detection in lap-joint samples. The study of various heating protocols shows that shorter heating times allow the greatest contrast in smaller disbonds; the optimum heating technique for this case is periodic heating. In order to compare the different heating protocols, contrast is defined as a function of the moments of the histograms for the bonded region and unbonded regions of the reduced thermal images. A comparison of the bonded-unbonded contrast for different heating times establishes the parameters which maximize disbond detection probability.

Winfree, William P.↗

NASA NTRS1998

Refraction of Sound Emitted Near Solid Boundaries from a Sheared Jet

A mathematical model is developed to describe the sound emitted from an arbitrary point within a turbulent flow near solid boundaries. A unidirectional, transversely sheared mean flow is assumed, and the cross-section of the cold jet is of arbitrary shape. The analysis begins with Lilley's formulation of aerodynamic noise and, depending upon the specific model of turbulence used, leads via Fourier analysis to an expression for the spectral density of the intensity of the far-field sound emitted from a unit volume of turbulence. The expressions require solution of a reduced Green's function of Lilley's equation as well as certain moving axis velocity correlations of the turbulence. Integration over the entire flow field is required in order to predict the sound emitted by the complete flow. Calculations are presented for sound emitted from a plugflow jet exiting a semi-infinite flat duct. Polar plots of the far-field directivity show the dependence upon frequency and source position within the duct. Certain model problems are suggested to investigate the effect of duct termination, duct geometry, and mean flow shear upon the far-field sound.

Dill, Loren H.↗

NASA NTRS2020

Building a standardized Observing System Simulation Experiment (OSSE) framework for Mars

We advocate that the Decadal Survey recommends the NASA Science Mission Directorate to develop a rigorous Observing System Simulation Experiment (OSSE) framework for Mars, to optimize future atmospheric observations. Atmospheric conditions on Mars are a potential hazard source for landing missions. Errors in the estimates of atmospheric density profiles, inadequate knowledge of wind vertical structure and dust concentration as a function of height are likely causes of uncertainty at the landing site on the order of kilometers. An operational real-time weather forecasting capability for Mars would reduce such uncertainties, carrying enormous benefits to future robotic missions, and would be an invaluable prerequisite for human missions.A real-time forecasting capability relies upon three fundamental components: a critical mass of observing systems, a data assimilation system (DAS), and a global forecast model. The DAS allows the model to ingest the data effectively, optimizing the observational information content,and transforming them into a gridded representation of the atmosphere at a given time, called an ‘analysis’. The analysis is the best estimate of the atmospheric state for that time, and also represents a set of ‘initial conditions’ from which a global model can be initialized, to predict a future state of the atmosphere. The connection between analysis and forecast represents the foundation of modern weather forecasting. However, from the point of view of a forecast system,not all observations are equally impactful, partially because of the problem of “observational error correlation”, one important research topic in data assimilation development. For the Earth, partly due to the spontaneous and deregulated development of observations and forecast capabilities worldwide for more than half a century,the use of observations in contemporary operational forecast systems is suboptimal, with many potentially useful data being underutilized. On the contrary, Mars atmospheric scientists are in the unique situation of designing the next-generation observing systems by learning from the experience gathered on the Earth, so as to assure that the future instruments are specifically optimized to give the maximum benefit to a future weather forecast capability.An immensely powerful tool that has been firmly established by atmospheric scientists on the Earth is represented by a properly designed OSSE framework. A realistic OSSE framework cannot only quantify the benefit of future data types, be them surface based or space borne, but can also help design and optimize an entire observational network. Furthermore, OSSEs can provide deep insights into an atmosphere’s behavior, by addressing conceptual problems of its intrinsic predictability and delineating the regions or features of the atmosphere which are more sensitive to additional data and would benefit from a denser sampling. The difficulties posed by OSSEs are fundamentally different for Earth and Mars. For Earth, the enormous data volume imposes a tremendous constraint on any innovation in the observing systems: it is very hard for a single sensor to impact the skill. For Mars, the problem is the opposite: almost any additional instrument will exert some impact. However, OSSEs can help to evaluate the cost/benefit for every sensor and suggest optimal data configuration and density.The purpose of this white paper is to provide an introduction to a rigorously designed OSSE framework, explain the underlying problems and challenges, and engage the Mars community to collaborate with Earth Atmospheric scientists in order to develop a joint-OSSE framework for Mars with the largest consensual basis possible. An OSSE infrastructure would increase the understanding of the Martian atmosphere, would help NASA to optimize instrument specifications and orbit choice, providing the maximium benefit for a given expenditure of resources, and could even help establishing a roadmap for a future real-time weather forecasting capability.

Oreste Reale↗

NASA NTRSDate not supplied

Jig Twist Optimization of Mach 0.8 Transonic Truss-Braced Wing Aircraft

This paper presents a jig twist optimization study of Mach 0.8 Transonic Truss-Braced Wing (TTBW) aircraft using FUN3D. The solver has been validated against wind tunnel test data. An aeroelastic model deformation correction method is developed for the wind tunnel model to improve the validation. The flight condition corresponding to Mach 0.8 and design lift coefficient 0.695 is selected for the TTBW aircraft jig twist optimization to reduce the drag coefficient. The static aeroelastic simulation module in FUN3D is used for the aeroelastic simulation. Three different orders of the Chebyshev polynomials are used as shape function to represent the variations of the jig twist distribution along the wing span. The preliminary optimization results show that the Mach 0.8 TTBW aircraft with the optimized jig twist achieves a drag reduction of about 5.7 counts at the design condition when 5 𝑡ℎ order Chebyshev polynomials are used as shape function.

TTBW↗

NASA NTRS1995

Optimal guidance law development for an advanced launch system

The objective of this research effort was to develop a real-time guidance approach for launch vehicles ascent to orbit injection. Various analytical approaches combined with a variety of model order and model complexity reduction have been investigated. Singular perturbation methods were first attempted and found to be unsatisfactory. The second approach based on regular perturbation analysis was subsequently investigated. It also fails because the aerodynamic effects (ignored in the zero order solution) are too large to be treated as perturbations. Therefore, the study demonstrates that perturbation methods alone (both regular and singular perturbations) are inadequate for use in developing a guidance algorithm for the atmospheric flight phase of a launch vehicle. During a second phase of the research effort, a hybrid analytic/numerical approach was developed and evaluated. The approach combines the numerical methods of collocation and the analytical method of regular perturbations. The concept of choosing intelligent interpolating functions is also introduced. Regular perturbation analysis allows the use of a crude representation for the collocation solution, and intelligent interpolating functions further reduce the number of elements without sacrificing the approximation accuracy. As a result, the combined method forms a powerful tool for solving real-time optimal control problems. Details of the approach are illustrated in a fourth order nonlinear example. The hybrid approach is then applied to the launch vehicle problem. The collocation solution is derived from a bilinear tangent steering law, and results in a guidance solution for the entire flight regime that includes both atmospheric and exoatmospheric flight phases.

Calise, Anthony J.↗

DOE OSTI2022

GeN-ROM—An OpenFOAM®-based multiphysics reduced-order modeling framework for the analysis of Molten Salt Reactors

This work presents a projection-based multiphysics Model Order Reduction (MOR) framework for the analysis of nuclear systems and its application to parametric simulations of Molten Salt Reactors (MSR). The framework, named GeN-ROM, is developed using OpenFOAM® and employs a Proper Orthogonal Decomposition aided Reduced-Basis technique (POD-RB). It can be used to reduce steady-state and transient multiphysics problems involving parametric fluid dynamics, heat exchange, and neutronics phenomena. For the treatment of structural elements in the hydraulic systems, a porous medium approach has been adopted. The reduction process is data-driven and snapshot information is extracted via POD to learn the solution manifold and to build global spatial basis functions. At the data collection phase, GeN-ROM makes use of the solvers available in GeN-Foam, a similarly OpenFOAM®-based multiphysics framework developed for the analysis of nuclear reactors. The global bases are used both to approximate the solution fields and to project the full-order equations onto lower-dimensional subspaces, thus considerably reducing the number of unknowns in a numerical system. This reduction leads to significant computational speedups, which is ideal for multi-query applications such as uncertainty quantification or design optimization. The developed tool has been tested using a 2D multiphysics model of the Molten Salt Fast Reactor (MSFR) with steady-state and transient scenarios, with speedups on the order of 10 – 10 5 .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗

DOE OSTI2022

Mapping the space of quasisymmetric stellarators using optimized near-axis expansion

A method is demonstrated to rapidly calculate the shapes and properties of quasi-axisymmetric and quasi-helically symmetric stellarators. In this approach, optimization is applied to the equations of magnetohydrodynamic equilibrium and quasisymmetry, expanded in the small distance from the magnetic axis, as formulated by Garren & Boozer [ Phys. Fluids B, vol. 3, 1991, p. 2805]. Due to the reduction of the equations by the expansion, the computational cost is significantly reduced, to times of the order of 1 cpu second, enabling wide and high-resolution scans over parameter space. In contrast to traditional stellarator optimization, here, the cost function serves to maximize the volume in which the expansion is accurate. A key term in the cost function is $\| \boldsymbol {\nabla }\boldsymbol B \|$ , the norm of the magnetic field gradient, to maximize scale lengths in the field. Using this method, a database of $5\times 10^5$ optimized configurations is calculated and presented. Quasisymmetric configurations are observed to exist in continuous bands, varying in the ratio of the magnetic axis length to average major radius. Several qualitatively new types of configuration are found, including quasi-helically symmetric fields in which the number of field periods is two or more than six.

Landreman, Matt (ORCID:000000027233577X)↗

DOE OSTI2022

On analysis and stochastic modeling of the particle kinetic energy equation in particle-laden isotropic turbulent flows

In this study, we analyze three-dimensional particle-laden, isotropic turbulence to develop an understanding of inertial particle dynamics from a kinetic energy perspective. Data trends implying inhomogeneous sampling of the flow by particles are identified and used to support a proposed particle behavior: particles appear to accumulate in regions of low flow kinetic energy over time because they lose kinetic energy and slow down in such regions, ultimately causing them to spend more time there. To elucidate this behavior, we derive a particle kinetic energy equation from the particle momentum equation, which incorporates inertial effects through the Schiller–Naumann drag correlation. Upon extracting fundamental physics from this equation, hypotheses regarding the role of the Stokes number in the temporal change of particle kinetic energy and the previously proposed particle behavior are evaluated using simulation data considering three Stokes numbers. Finally, a Fokker–Planck equation is used to derive the steady-state probability density function of the particle kinetic energy. The model fits the simulation data well and provides a tool for further investigation into understanding preferential concentration, as well as a reduced order model for predicting particle kinetic energy in turbulent flows.

42 ENGINEERING↗

DOE OSTI2023

Taxonomic distribution of metabolic functions in bacteria associated with Trichodesmium consortia

The photosynthetic and diazotrophic cyanobacterium Trichodesmium is a key contributor to marine biogeochemical cycles in the subtropical-oligotrophic oceans. Trichodesmium form colonies that harbor a distinct microbial community in comparison to the surrounding seawater. The presence of their associated bacteria can expand Trichodesmium’s functional potential and is predicted to influence the cycling of carbon, nitrogen, phosphorus, and iron (C, N, P, and Fe). To link the bacteria associated with Trichodesmium to key functional traits and elucidate how community structure can influence nutrient cycling, we characterized Red Sea Trichodesmium colonies using metagenomics and metaproteomics. Colonies harbored bacteria that typically associate with algae and particles, such as the ubiquitous Alteromonas macleodii, but also lineages specific to Trichodesmium, such as members from the order Balneolales. The majority of associated bacteria were auxotrophic for different vitamins, indicating their dependency on vitamin production by Trichodesmium. The associated bacteria carry functional traits including siderophore biosynthesis, reduced phosphorus metabolism, and denitrification pathways. The analysis supports Trichodesmium as an active hotspot for C, N, P, Fe, and vitamin exchange. In turn, Trichodesmium may rely on associated bacteria to meet its high Fe demand as several lineages synthesize photolabile siderophores (e.g., vibrioferrin, rhizoferrin, petrobactin) which can enhance the bioavailability of particulate Fe to the entire consortium. Collectively, the results indicate that Trichodesmium colonies provide a structure where these interactions can take place. While further studies are required to clarify the exact nature of these interactions, Trichodesmium’s reliance on particle and algae-associated bacteria and the observed redundancy of key functional traits likely underpins the resilience of Trichodesmium within an ever-changing global environment.

59 BASIC BIOLOGICAL SCIENCES↗