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NASA NTRS2004

Escherichia coli growth under modeled reduced gravity

Bacteria exhibit varying responses to modeled reduced gravity that can be simulated by clino-rotation. When Escherichia coli was subjected to different rotation speeds during clino-rotation, significant differences between modeled reduced gravity and normal gravity controls were observed only at higher speeds (30-50 rpm). There was no apparent affect of removing samples on the results obtained. When E. coli was grown in minimal medium (at 40 rpm), cell size was not affected by modeled reduced gravity and there were few differences in cell numbers. However, in higher nutrient conditions (i.e., dilute nutrient broth), total cell numbers were higher and cells were smaller under reduced gravity compared to normal gravity controls. Overall, the responses to modeled reduced gravity varied with nutrient conditions; larger surface to volume ratios may help compensate for the zone of nutrient depletion around the cells under modeled reduced gravity.

Non-NASA Center↗

NASA NTRS2012

Nature of Reduced Carbon in Martian Meteorites

Martian meteorites provide important information on the nature of reduced carbon components present on Mars throughout its history. The first in situ analyses for carbon on the surface of Mars by the Viking landers yielded disappointing results. With the recognition of Martian meteorites on Earth, investigations have shown carbon-bearing phases exist on Mars. Studies have yielded presence of reduced carbon, carbonates and inferred graphitic carbon phases. Samples ranging in age from the first approximately 4 Ga of Mars history [e.g. ALH84001] to nakhlites with a crystallization age of 1.3 Ga [e.g. Nakhla] with aqueous alteration processes occurring 0.5-0.7 Ga after crystallizaton. Shergottites demonstrate formation ages around 165-500 Ma with younger aqueous alterations events. Only a limited number of the Martian meteorites do not show evidence of significance terrestrial alterations. Selected areas within ALH84001, Nakhla, Yamato 000593 and possibly Tissint are suitable for study of their indigenous reduced carbon bearing phases. Nakhla possesses discrete, well-defined carbonaceous phases present within iddingsite alteration zones. Based upon both isotopic measurements and analysis of Nakhla's organic phases the presence of pre-terrestrial organics is now recognized. The reduced carbon-bearing phases appear to have been deposited during preterrestrial aqueous alteration events that produced clays. In addition, the microcrystalline layers of Nakhla's iddingsite have discrete units of salt crystals suggestive of evaporation processes. While we can only speculate on the origin of these unique carbonaceous structures, we note that the significance of such observations is that it may allow us to understand the role of Martian carbon as seen in the Martian meteorites with obvious implications for astrobiology and the pre-biotic evolution of Mars. In any case, our observations strongly suggest that reduced organic carbon exists as micrometer- size, discrete structures on Mars associated with clay and salt minerals. The Mars Science Laboratory s investigators should be aware of reduced organic carbon components within clay-bearing phases.

Gibson, Everett K., Jr.↗

NASA NTRS2013

Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation

Retrieval algorithms like that used by the Orbiting Carbon Observatory (OCO)-2 mission generate massive quantities of data of varying quality and reliability. A computationally efficient, simple method of labeling problematic datapoints or predicting soundings that will fail is required for basic operation, given that only 6% of the retrieved data may be operationally processed. This method automatically obtains a filter designed to reduce scatter based on a small number of input features. Most machine-learning filter construction algorithms attempt to predict error in the CO2 value. By using a surrogate goal of Mean Monthly STDEV, the goal is to reduce the retrieved CO2 scatter rather than solving the harder problem of reducing CO2 error. This lends itself to improved interpretability and performance. This software reduces the scatter of retrieved CO2 values globally based on a minimum number of input features. It can be used as a prefilter to reduce the number of soundings requested, or as a post-filter to label data quality. The use of the MMS (Mean Monthly Standard deviation) provides a much cleaner, clearer filter than the standard ABS(CO2-truth) metrics previously employed by competitor methods. The software's main strength lies in a clearer (i.e., fewer features required) filter that more efficiently reduces scatter in retrieved CO2 rather than focusing on the more complex (and easily removed) bias issues.

Mandrake, Lukas↗

NASA NTRS2018

Warming Early Mars by Impact Degassing of Reduced Greenhouse Gases

Reducing greenhouse gases are once again the latest trend in finding solutions to the early Mars climate dilemma. In its current form collision induced absorptions (CIA) involving H2 and/or CH4 provide enough extra greenhouse power in a predominately CO2 atmosphere to raise global mean surface temperatures to the melting point of water provided the atmosphere is thick enough and the reduced gases are abundant enough. Surface pressures must be at least 500 mb and H2 and/or CH4 concentrations must be at or above the several percent level for CIA to be effective. Atmospheres with 1-2 bars of CO2 and 2- 10% H2 can sustain surface environments favorable for liquid water. Smaller concentrations of H2 are sufficient if CH4 is also present. If thick CO2 atmospheres with percent level concentrations of reduced gases are the solution to the faint young Sun paradox for Mars, then plausible mechanisms must be found to generate and sustain the gases. Possible sources of reducing gases include volcanic outgassing, serpentinization, and impact delivery; sinks include photolyis, oxidation, and escape to space. The viability of the reduced greenhouse hypothesis depends, therefore, on the strength of these sources and sinks. In this paper we focus on impact delivered reduced gases.

Haberle, R. M.↗

NASA NTRSDate not supplied

Trace Element Partitioning in the Presence of Sulfur Under Reduced Conditions

Oxygen fugacity (fO2) plays an important role in thephysicochemical properties of that system because fO2 has asubstantial impact on the partitioning behavior between andsolubility of elements in various phases. The reduced nature ofMercury and the aubrite parent bodies (APB) have raised manyquestions regarding the geochemical behavior of typicallylithophile, heat-producing, and rare-earth elements (REE) inmagmas at low fO2. In fact, interpretations of magmaticprocesses on reduced rocky bodies are difficult at presentbecause of the small number of experimental studies relevant tothe extremely low fO2 and high sulfur content of Mercury andthe APB, and our only natural analogs are the highly reducedenstatite chondrites and aubrites [e.g., 1]. Preliminaryobservations suggest that major and minor elements exhibitdifferent geochemical affinities in highly reduced, S-richsystems compared to terrestrial rocks. The partitioning behavior of major, minor, and traceelements between silicate melt, sulfide melt, and metal underhighly reduced conditions will be determined in a suite of super-liquidus experiments at a range of pressures (1 bar to 4 GPa) atNASA Johnson Space Center (JSC). Based on previous studies,heat-producing elements U and Th become more chalcophile,while K becomes less chalcophile, with more reducingconditions [2]. The nominally lithophile elements Mg and Cabecoming more chalcophile and appear as minor elements in(Fe,Mg,Ca)-S with more reducing conditions [3]. Additionally, itappears that nominally siderophile elements become moresiderophile and chalcophile with more reducing conditions,although the partitioning behavior into Mg- and Ca-bearingsulfide melts, rather than FeS, is less understood. Current workis focused on investigating elements for which we currently haveMESSENGER data as well as a host of geochemically relevanttrace elements such as REE.

Brendan A Anzures↗

NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage models at the highest levels of assembly. The effort to generate/verify these models against their detailed counterparts is a challenge not yet conclusively solved. Higher level organizations often place a limit on the node count for delivered models, assuming smaller models result in faster computation. The burden of producing and verifying the accuracy of reduced models is primarily on the lower-level organizations, which consumes resources to produce these models. Limiting the number of allowable nodes may prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which require more nodes than centroid based models. A methodology using Thermal Desktop was described in 2010 using: (1) finite elements and edge nodes for the conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. While the methodology was clear, the implementation would have had to be done manually; with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and be a viable approach for the generation of reduced models. The approach was implemented at NASA-Goddard for the Capture-Containment-and-Return-System (CCRS) payload, resulting in the TCYEE tool. CCRS requires delivery with node limitations through the European Space Agency to its contractors. TCYEE generated the reduced model for delivery and the predictions compared favorably to the detailed model. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements. This paper describes the methodology, implementation, and the performance of reduced models compared to their detailed counterparts.

Computer Programming and Software↗

DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗

DOE OSTI2023

Cost and Life Cycle Analysis for Deep CO 2 Emissions Reduction for Steel Making: Direct Reduced Iron Technologies

Among heavy industrial sectors worldwide, the steel industry ranks first in carbon dioxide (CO 2 ) emissions. Technologies that produce direct reduced iron (DRI) enable the industry to reduce emissions or even approach net‐zero CO 2 emissions for steel production. Herein, comprehensive cradle‐to‐gate (CTG) life cycle analysis (LCA) and techno‐economic analysis (TEA) are used to evaluate the CO 2 emissions of three DRI technologies. Compared to the baseline of blast furnace and basic oxygen furnace (BF–BOF) technology for steel making, using natural gas (NG) to produce DRI has the potential to reduce CTG CO 2 emissions by 33%. When 83% or 100% renewable H 2 is used for DRI production, DRI technologies can potentially reduce CO 2 emissions by 57% and 67%, respectively, compared to baseline BF–BOF technology. However, the renewable H 2 application for DRI increases the levelized cost of steel (LCOS). When renewable natural gas (RNG) and clean electricity are used for steel production, the CTG CO 2 emissions of all the DRI technologies can potentially be reduced by more than 90% compared to the baseline BF–BOF technology, although the LCOS depends largely on the cost of RNG and clean electricity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Reduced order models for Lagrangian hydrodynamics

It is reported, as a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-dominated solutions, and moving shock fronts with sharp gradients. These challenges hinder the existing projection-based model reduction schemes from being practical. We develop several variations of projection-based reduced order model techniques for Lagrangian hydrodynamics by introducing three different reduced bases for position, velocity, and energy fields. A time-windowing approach is also developed to address the challenge imposed by the advection-dominated solutions. Lagrangian hydrodynamics is formulated as a nonlinear problem, which requires a proper hyper-reduction technique. Therefore, we apply the over-sampling DEIM and SNS approaches to reduce the complexity due to the nonlinear terms. Finally, we also present both a posteriori and a priori error bounds associated with our reduced order model. We compare the performance of the spatial and time-windowing reduced order modeling approaches in terms of accuracy and speed-up with respect to the corresponding full order model for several numerical examples, namely Sedov blast, Gresho vortices, Taylor-Green vortices, and triple-point problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Bayesian operator inference for data-driven reduced-order modeling

This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning a reduced-order model from data is posed as a Bayesian inverse problem with Gaussian prior and likelihood. The resulting posterior distribution characterizes the operators defining the reduced-order model, hence the predictions subsequently issued by the reduced-order model are endowed with uncertainty. The statistical moments of these predictions are estimated via a Monte Carlo sampling of the posterior distribution. Since the reduced models are fast to solve, this sampling is computationally efficient. Furthermore, the proposed Bayesian framework provides a statistical interpretation of the regularization term that is present in the deterministic operator inference problem, and the empirical Bayes approach of maximum marginal likelihood suggests a selection algorithm for the regularization hyperparameters. The proposed method is demonstrated on two examples: the compressible Euler equations with noise-corrupted observations, and a single-injector combustion process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Proper orthogonal decomposition based reduced-order modeling of flux-Limited gray thermal radiation

Here, in this work, a proper orthogonal decomposition (POD) based reduced-order model (ROM) is developed to solve gray, flux-limited thermal radiation diffusion. We focus on the variable opacity radiation penetration benchmark posed by Olson, Auer, and Hall. The T -3 relationship for opacity in conjunction with high-temperature radiation penetrating an initially cold material produces a strong thermal radiation shock. This class of problems is particularly challenging for standard POD-based reduced-order modeling due to the nonlinearities presented by 1) the T 4 source term, and 2) flux-limited diffusion operator. To address these challenges and develop a cost competitive ROM, we employ a “hyper-reduction” technique through discrete empirical interpolation (DEIM) and allow for adaptive reduced-order projections through principal interval decomposition (PID). Performance of the proposed methodology is quantified by comparing the cost savings and accuracy relative to a full-order computation. Reference solutions and snapshot data are obtained through a full-order calculation performed by the University of Chicago maintained astrophysics code, FLASH. For consistency and potential extensibility, the developed ROM is also implemented in FLASH. We find that in the initialization regime, where the thermal radiation wave is initially created by the warming of the material, this class of problems is highly reducible and suitable for POD-based ROMs. However, the strong convective nature of the wave propagation regime is less reducible and more challenging to create an efficient ROM.

42 ENGINEERING↗

DOE OSTI2026

Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition for Reduced-Order Modeling of S n Radiation Transport

Deterministic solutions to the Sn radiation transport equation can be computationally expensive to calculate. Reduced-order modeling enables efficient approximation of the full-order model (FOM) solution. We propose a novel method for constructing reduced-order models (ROMs) of the S n radiation transport equation, offline maximizing minimally invasive (OMMI) proper orthogonal decomposition (POD). POD uses the method of snapshots to create a reduced-order basis for constructing an ROM. Minimally invasive POD leverages the sweep infrastructure existing in deterministic transport codes to create a POD-based ROM, even when infeasible by traditional methods. Offline maximizing minimally invasive proper orthogonal decomposition (OMMI-POD) extends minimally invasive POD by performing sweeps offline, therefore maximizing the potential speedup. OMMI-POD does so by creating a library of reduced systems from a training set. This library of reduced systems is then interpolated to provide a rapid approximate solution of the S n radiation transport equation. The model is evaluated on a set of test problems, achieving a low error with a 466 times speedup over the FOM. Also presented is a study of the effect of sampling method on the performance of OMMI-POD, specifically comparing naive uniform sampling to the more accurate and computationally expensive greedy sampling.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2023

Reduced-Order Modeling and Parameter Identification of Wind Tunnel Measurement Systems

We present a method to develop a physics-based, reduced-order model of a wind tunnel measurement system (including a sting, strain gage force balance, and test article) that can be used to predict the dynamics of the system. This reduced-order model is combined with a simple finite element beam model of a sting to estimate the dynamics of the full assembly. We make comparisons between a full finite element model and the hybrid reduced-order model to show that this hybrid reduced-order model is capable of predicting the first six natural frequencies to within 10% error. This technique could be used to identify reduced-order parameters for a large number of balances and stings, which could then be used to estimate the dynamics of different measurement assemblies.

Reduced-order Modeling↗

NASA NTRSDate not supplied

Reduced-Order Modeling and Parameter Identification of Wind Tunnel Measurement Systems

Reduced-order Modeling↗

DOE OSTI2024

A decade of curtailment studies demonstrates a consistent and effective strategy to reduce bat fatalities at wind turbines in North America

Abstract There is a rapid, global push for wind energy installation. However, large numbers of bats are killed by turbines each year, raising concerns about the impacts of wind energy expansion on bat populations. Preventing turbine blades from spinning at low wind speeds, referred to as curtailment, is a method to reduce bat fatalities, but drawing consistent inference across studies has been challenging. We compiled publicly available studies that evaluated curtailment at six wind energy facilities in North America across 10 years. We used meta‐regression of 29 implemented treatments to determine fatality reduction efficacy as well as sources of variation influencing efficacy. We also estimated species‐specific fatality reduction for three species that comprise most fatalities in North America: hoary bat ( Lasiurus cinereus ), eastern red bat ( Lasiurus borealis ) and silver‐haired bat ( Lasionycteris noctivagans ). We found that curtailment reduced total bat fatalities by 33% with every 1.0 ms −1 increase in curtailment wind speed. Estimates of the efficacy for the three target species were similar (hoary bats: 28% per ms −1 , 95% CI: 0.4%–48%, eastern red bats: 32% per ms −1 , 95% CI: 13%–47% and silver‐haired bats: 32% per ms −1 , 95% CI: 3%–53%). Across multiple facilities and years, a 5.0 ms −1 cut‐in speed was estimated to reduce total bat fatalities by an average of 62% (95% CI: 54%–69%). Mortality reductions at individual facilities in any given year were estimated to fall between 33%–79% (95% prediction interval). Inter‐annual differences rather than inter‐site or turbine characteristics accounted for most of the variation in efficacy rates. Species‐specific average mortality reduction at 5.0 ms −1 curtailment wind speed was 48% (95% CI: 24%–64%) for hoary bats, 61% (95% CI: 42%–74%) for eastern red bats and 52% (95% CI: 30%–66%) for silver‐haired bats. Practical implication . curtailment reduced bat mortality at wind turbines in this North American study. Efficacy increased proportionally as curtailment speed is raised, and patterns and rates of efficacy were similar across species. This indicates that curtailment is an effective strategy to reduce bat fatalities at wind energy facilities, but exploration of further refinements could both minimize bat mortality and maximize energy production.

17 WIND ENERGY↗

DOE OSTI2022

Preconditioned least‐squares Petrov–Galerkin reduced order models

Abstract In this article, we introduce a methodology for improving the accuracy and efficiency of reduced order models (ROMs) constructed using the least‐squares Petrov–Galerkin (LSPG) projection method through the introduction of preconditioning. Unlike prior related work, which focuses on preconditioning the linear systems arising within the ROM numerical solution procedure to improve linear solver performance, our approach leverages a preconditioning matrix directly within the minimization problem underlying the LSPG formulation. Applying preconditioning in this way has the potential to improve ROM accuracy for several reasons. First, preconditioning the LSPG formulation changes the norm defining the residual minimization, which can improve the residual‐based stability constant bounding the ROM solution's error. The incorporation of a preconditioner into the LSPG formulation can have the additional effect of scaling the components of the residual being minimized to make them roughly of the same magnitude, which can be beneficial when applying the LSPG method to problems with disparate scales (e.g., dimensional equations, multi‐physics problems). Importantly, we demonstrate that an “ideal preconditioned” LSPG ROM (a ROM in which the preconditioner is the inverse of the Jacobian of its corresponding full order model) emulates projection of the full order model solution increment onto the reduced basis. This quantity defines a lower bound on the error of a ROM solution for a given reduced basis. By designing preconditioners that approximate the Jacobian inverse—as is common in designing preconditioners for solving linear systems—it is possible to obtain a ROM whose error approaches this lower bound. The proposed approach is evaluated on several mechanical and thermo‐mechanical problems implemented within the Albany HPC code and run in the predictive regime, with prediction across material parameter space. We demonstrate numerically that the introduction of simple Jacobi, Gauss‐Seidel, and ILU preconditioners into the proper orthogonal decomposition/LSPG formulation reduces significantly the ROM solution error, the reduced Jacobian condition number, the number of nonlinear iterations required to reach convergence, and the wall time (thereby improving efficiency). Moreover, our numerical results reveal that the introduction of preconditioning can deliver a robust and accurate solution for test cases in which the unpreconditioned LSPG method fails to converge.

Lindsay, Payton↗

DOE OSTI2022

The transformation of U(VI) and V(V) in carnotite group minerals during dissimilatory respiration by a metal reducing bacterium

Recent results from laboratory and field studies support that dissimilatory metal reducing (DMR) bacteria influence the fate and transport of uranium in anaerobic subsurface environments. To date, most research efforts have focused on the reduction of soluble U(VI) by DMR bacteria to form insoluble uraninite (UO 2 ). Subsurface environments harbor, however, large reservoirs of U(VI) in solid or mineral form. Uranium that is structure-bound in minerals is expected to be more refractory to microbial reduction than soluble U, based on analogy with Fe respiration. The reducibility of U(VI) could impact the fate of U(IV) by controlling mineral precipitation reactions, which has implications for the long-term immobilization of U in subsurface environments. Here, we studied anoxic cultures of Shewanella putrefaciens CN32 incubated with natural carnotite-group minerals by X-ray diffraction, electron microscopy, scanning transmission X-ray microscopy (STXM). Near-edge X-ray absorption fine structure (NEXAFS) spectroscopy measurements at U-N 4,5 , V-L 2,3 , and O-K edges on cultures incubated up to 10 months show that V(V) was reduced to V(IV), whereas U was not reduced. In contrast, V(V) and U(VI) in solution were both completely reduced to lower oxidation states by CN32, as precipitates within the exopolymer surrounding the bacteria. Assays for the toxicity of U and V to CN32 showed that biofilm formation was stimulated at 0.001 M U(VI), and growth was inhibited at concentrations of U(VI) greater than 0.001 M. Vanadium did not inhibit growth or stimulate biofilm formation at any concentration tested. Investigations of the bacteria-mineral and bacteria-metal interface at the nanometer and molecular scales provide new insights into the co-respiration of V and U that help explain their biogeochemical cycling and have implications for subsurface bioremediation of these elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Determining the Ensemble N -Representability of Reduced Density Matrices

The N-representability problem for reduced density matrices remains a fundamental challenge in electronic structure theory. Following our previous work that employs a unitary-evolution algorithm based on an adaptive derivative-assembled pseudo-Trotter variational quantum algorithm to probe pure-state N-representability of reduced density matrices [J. Chem. Theory Comput. 2024, 20, 9968], in this work we propose a practical framework for determining the ensemble N-representability of a p-body matrix. This is accomplished using a purification strategy that embeds an ensemble state into a pure state defined on an extended Hilbert space, such that the reduced density matrices of the purified state reproduce those of the original ensemble. By iteratively applying variational unitaries to an initial purified state, the proposed algorithm minimizes the Hilbert-Schmidt distance between its p-body reduced density matrix and a specified target p-body matrix, which serves as a measure of the N-representability of the target. This methodology facilitates both error correction of defective ensemble reduced density matrices and quantum-state reconstruction on a quantum computer, offering a route for density-matrix refinement. We validate the algorithm with numerical simulations on systems of two, three, and four electrons in both simple models as well as molecular systems at finite temperature, demonstrating its robustness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗