Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “constrained dynamics”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental characterization of hot-electron emission and shock dynamics in the context of the shock ignition approach to inertial confinement fusion

We report on planar target experiments conducted on the OMEGA-EP laser facility performed in the context of the shock ignition (SI) approach to inertial confinement fusion. The experiment aimed at characterizing the propagation of strong shock in matter and the generation of hot electrons (HEs), with laser parameters relevant to SI (1-ns UV laser beams with I ~10 16 W/cm 2 ). Time-resolved radiographs of the propagating shock front were performed in order to study the hydrodynamic evolution. The hot-electron source was characterized in terms of Maxwellian temperature, T h , and laser to hot-electron energy conversion efficiency η using data from different x-ray spectrometers. The post-processing of these data gives a range of the possible values for T h and η [i.e., T h [keV] ϵ (20, 50) and η ϵ (2%, 13%)]. These values are used as input in hydrodynamic simulations to reproduce the results obtained in radiographs, thus constraining the range for the HE measurements. According to this procedure, we found that the laser converts ~10% ± 4% of energy into hot electrons with T h = 27 ± 8 keV. The paper shows how the coupling of different diagnostics and numerical tools is required to sufficiently constrain the problem, solving the large ambiguity coming from the post-processing of spectrometers data. The effect of the hot electrons on the shock dynamics is then discussed, showing an increase in the pressure around the shock front. Furthermore, the low temperature found in this experiment without pre-compression laser pulses could be advantageous for the SI scheme, but the high conversion efficiency may lead to an increase in the shell adiabat, with detrimental effects on the implosion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The benefits of diligence: how precise are predicted gravitational wave spectra in models with phase transitions?

Models of particle physics that feature phase transitions typically provide predictions for stochastic gravitational wave signals at future detectors and such predictions are used to delineate portions of the model parameter space that can be constrained. The question is: how precise are such predictions? Uncertainties enter in the calculation of the macroscopic thermal parameters and the dynamics of the phase transition itself. We calculate such uncertainties with increasing levels of sophistication in treating the phase transition dynamics. Currently, the highest level of diligence corresponds to careful treatments of the source lifetime; mean bubble separation; going beyond the bag model approximation in solving the hydrodynamics equations and explicitly calculating the fraction of energy in the fluid from these equations rather than using a fit; and including fits for the energy lost to vorticity modes and reheating effects. The lowest level of diligence incorporates none of these effects. We compute the percolation and nucleation temperatures, the mean bubble separation, the fluid velocity, and ultimately the gravitational wave spectrum corresponding to the level of highest diligence for three explicit examples: SMEFT, a dark sector Higgs model, and the real singlet-extended Standard Model (xSM). In each model, we contrast different levels of diligence in the calculation and find that the difference in the final predicted signal can be several orders of magnitude. Our results indicate that calculating the gravitational wave spectrum for particle physics models and deducing precise constraints on the parameter space of such models continues to remain very much a work in progress and warrants care.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A workflow to assess the efficacy of brine extraction for managing injection-induced seismicity potential using data from a CO 2 injection site near Decatur, Illinois

Injection of CO 2 for storage in a geologic formation increases pore pressure and alters in situ stresses. Depending on the orientation of any existing fault and fracture planes, such as critically stressed planes, this stress alteration will modify normal stresses acting on planes and could result in frictional sliding and release stored energy in the form of seismicity. Brine extraction (BE) is a technique that can be applied prior to, or during, CO 2 injection to reduce pore pressure for increasing storage capacity and, potentially, for reducing the likelihood of frictional sliding. Here a workflow is described to assess the efficacy of BE for mitigating frictional sliding (i.e., seismicity) during injection and entails: site characterization, stress calculations and failure assessment, static and dynamic modeling, and BE operational planning. Site characterization describes the stress field used to calculate the Coulomb Failure Function (CFF) that constrains allowable pore pressure changes and injection rates in the numerical simulation of CO 2 injection scenarios. The inclusion of BE in the workflow allows for determination of the potential need for pressure reduction, and evaluation of the effectiveness of this operation. Example application of the workflow using an injection field dataset near Decatur, IL, provides insight on fracture planes and stresses at the site, formation properties and the impact of variable CO 2 injection-rate targets on whether BE plans are required. The study workflow indicates that BE could enhance CO 2 injection rate by 39% and correspondingly reduce the potential for injection-induced seismicity as indicated by a reduction in CFF.

58 GEOSCIENCES↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Probing neutron-skin thickness with free spectator neutrons in ultracentral high-energy isobaric collisions

We show that the yield ratio of free spectator neutrons produced in high-energy 96 Zr+ 96 Zr to 96 Ru+ 96 Ru collisions is a clean probe of the neutron-skin thickness of colliding nuclei and the slope parameter L of the symmetry energy. The idea is demonstrated based on the proton and neutron density distributions via a state-of-the-art Skyrme-Hartree-Fock-Bogolyubov calculation. Among spectator nucleons given by the Glauber model, free spectator neutrons include those from direct production that survive from clusterization as well as those from deexcitation of heavy clusters described by the popular GEMINI model. More free neutrons are produced in collisions of 96 Zr nucleus due to its larger neutron skin, compared to those produced in collisions of 96 Ru nucleus with a smaller neutron skin. The difference of the free spectator neutron yield is further increased with the increasing difference of the neutron-skin thickness between 96 Zr and 96 Ru with a larger L value, and the increase in ultracentral collisions is particularly insensitive to model details and experimental uncertainties. Since the production of free spectator neutrons is not affected by the complicated dynamics in the mid-rapidity region, the ratio of their multiplicities in ultracentral isobaric collisions is a robust observable for constraining the neutron skin and L value.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Partitioning Net Ecosystem Exchange (NEE) of CO 2 Using Solar-Induced Chlorophyll Fluorescence (SIF)

Accurate partitioning of net ecosystem exchange (NEE) of CO 2 to gross primary production (GPP) and ecosystem respiration (R eco ) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long-standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar-induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF-based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and R eco , especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP and R eco than conventional approaches. Furthermore, this study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real-world environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Wang, Xiao↗
DOE OSTI2024

LANSCE Science Overview [Slides]

LANSCE is primarily focused on three science questions for NNSA and contributes to several other programs. We will primarily consider the NNSA questions: (1) How can we advance our understanding of dynamic material behavior using focused experiments? (2) What tools are needed to address Advanced Manufacturing and Aging? (3) How do we constrain the nuclear reaction networks involved in weapons?

36 MATERIALS SCIENCE↗
DOE OSTI2025

Leveraging High-resolution Molecular Composition of Soil Organic Matter to Enhance Carbon Cycling Modeling

Soils store more carbon than the atmosphere and vegetation combined, yet Earth system models still struggle to predict how this vast reservoir will respond to environmental change. A central limitation is that most soil biogeochemical models represent organic matter using bulk conceptual pools or chemically homogeneous fractions, preventing direct use of rapidly expanding molecular-scale datasets. Here we develop and test a new soil decomposition framework that explicitly integrates high-resolution information on organic matter composition. First, we construct a molecularly informed litter decomposition module in which plant inputs are partitioned into five functional compound classes—carbohydrates, proteins, lignin-like aromatics, lipids, and carbonyls—using a molecular mixing model calibrated to solid-state 13 C Nuclear Magnetic Resonance (NMR) spectra. Class-specific kinetics, lignin-dependent physical protection, and substrate-driven microbial carbon use efficiency allow the module to capture metabolic tradeoffs associated with enzyme production and nutrient limitation. We then embed this litter module within a microbially explicit whole-soil model that tracks the transformation of these compound classes through particulate organic matter, dissolved organic matter, mineral-associated organic matter, and microbial biomass. High-resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FTICR-MS) data are used to link internal pools to measurable soil organic matter fractions and to constrain key process parameters. Applications at soil-core and ecosystem scales demonstrate that the new model reproduces observed soil respiration dynamics while providing mechanistic attribution of CO 2 fluxes to specific chemical classes and pools. Compared to existing frameworks such as the Community Land Model soil biogeochemistry module and the Millennial model, our approach maintains competitive predictive skill while substantially improving interpretability and opportunities for data–model integration. This work illustrates a viable pathway for leveraging molecular-scale observations to reduce structural uncertainty in soil carbon–climate feedback projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Heliostat Consortium: Gap Analysis on Wind Load for Achieving a Fully Competitive Heliostat Industry

The Heliostat Consortium (HelioCon) Wind Load Subtask was initiated with the aim of bringing research work pertaining to wind load measurement, characterization, and prediction taking place across several tasks, including Advanced Manufacturing, Components and Controls, and Field Deployment. The cross-cutting wind load subtopic in the HelioCon roadmap report [1] highlighted standardized methods and tools that are needed for a more detailed understanding of the static and dynamic loads on a heliostat. This will enable cost reduction of wind-dependent heliostat components to avoid unnecessarily conservative, overly constrained designs and increase field efficiency/reliability, to reduce the risk of component failures due to high-wind events (>15 m/s). Gaps related to heliostat wind load include site characterization for wind measurements, critical load cases for heliostat design, turbulence impacts on heliostat tracking error, testing of heliostat array configurations, understanding spatial variation of maximum loads across the solar field, and heliostat field layout and operating strategies. The recommended highest-priority pathway as first steps taken by HelioCon to address these gaps are to develop site characterization guidelines for heliostat design and field load measurements.

Emes, Matthew (ORCID:0000000341474387)↗
DOE OSTI2020

Water and vapor transport in algal‐fungal lichen: Modeling constrained by laboratory experiments, an application for Flavoparmelia caperata

ABSTRACT Algal‐fungal symbionts share water, nutrients, and gases via an architecture unique to lichens. Because lichen activity is controlled by moisture dynamics, understanding water transport is prerequisite to understand their fundamental biology. We propose a model of water distributions within foliose lichens governed by laws of fluid motion. Our model differentiates between water stored in symbionts, on extracellular surfaces, and in distinct morphological layers. We parameterize our model with hydraulic properties inverted from laboratory measurements of Flavoparmelia caperata and validate for wetting and drying. We ask: (1) Where is the bottleneck to water transport? (2) How do hydration and dehydration dynamics differ? and (3) What causes these differences? Resistance to vapor flow is concentrated at thallus surfaces and acts as the bottleneck for equilibrium, while internal resistances are small. The model captures hysteresis in hydration and desiccation, which are shown to be controlled by nonlinearities in hydraulic capacitance. Muting existing nonlinearities slowed drying and accelerated wetting, while exaggerating nonlinearities accelerated drying and slowed wetting. The hydraulic nonlinearity of F. caperata is considerable, which may reflect its preference for humid and stable environments. The model establishes the physical foundation for future investigations of transport of water, gas, and sugar between symbionts.

Potkay, Aaron↗
DOE OSTI2026

Memory-efficient nonsmooth dynamic optimization using adaptive randomized compression

Dynamic optimization problems arise in many applications including flow control, full waveform inversion, and medical imaging. These problems are plagued by significant computational challenges. One such challenge — and the focus of this work — is the memory limitation induced by the size of the underlying dynamical system. In particular, the entire dynamic trajectory is required for derivative computation and therefore must be stored or recomputed using, e.g., checkpointing. Although recent work demonstrated the use of adaptive randomized sketching to overcome the memory challenge, that work only applies to smooth unconstrained problems, prohibiting its use for nonsmooth regularized and constrained problems. The inclusion of nonsmooth regularizers and constraints is critical as they often arise in an attempt to preserve certain physical properties or to promote sparsity. To solve these problems, we introduce a trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function that leverages randomized sketching to compress the dynamical system trajectories and adaptively adjust the sketch rank to satisfy a gradient inexactness condition. We prove convergence of this algorithm and demonstrate that it achieves substantial memory reduction on three discretized PDE-constrained optimization applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

58 GEOSCIENCES↗
DOE OSTI2022

Structural magnetic glassiness in the spin ice Dy 2 Ti 2 O 7

The origin and nature of glassy dynamics presents one of the central enigmas of condensed-matter physics across a broad range of systems ranging from window glass to spin glasses. The spin-ice compound Dy 2 Ti 2 O 7 , which is perhaps best known as hosting a three-dimensional Coulomb spin liquid with magnetically charged monopole excitations, also falls out of equilibrium at low temperature. How and why it does so remains an open question. Based on an analysis of low-temperature diffuse neutron-scattering experiments employing different cooling protocols alongside recent magnetic noise studies, combined with extensive numerical modeling, we argue that upon cooling, the spins freeze into what may be termed a “structural magnetic glass,” without an a priori need for chemical or structural disorder. Specifically, our model indicates the presence of frustration on two levels, first producing a near-degenerate constrained manifold inside which phase ordering kinetics is in turn frustrated. A remarkable feature is that monopoles act as sole annealers of the spin network and their pathways and history encode the development of glass dynamics, allowing the glass formation to be visualized. Our results suggest that spin ice Dy 2 Ti 2 O 7 provides one prototype of magnetic glass formation specifically and a setting for the study of kinetically constrained systems more generally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Abstract Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme–substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Ferreiro, David↗
DOE OSTI2022

Molecular Dynamics of High Pressure Tin Phases I: Strength and deformation evaluations of empirical potentials [Slides]

Multi-phase problems have so many more unknowns, we’d like to have a tool to constrain some open questions related to microstructure and twin & dislocation behavior. We want an atomistic scale perspective on aspects of strength. Some multi-scale questions accessible to atomistic study: What lattice-specific behavior influences dislocation production/mobility and/or twinning? Do the phase transformations wipe-out, modify or preserve grain size and orientation? Does plastic strain reset at phase transition? If so under what conditions? Tin is the material chosen for the effort because it is non-hazardous and has multiple accessible solid phases at relatively low pressures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Simultaneous inference of plate boundary stresses and mantle rheology using adjoints: large-scale 2-D models

SUMMARY Plate motions are a primary surface constraint on plate and mantle dynamics and rheology, plate boundary stresses and the occurrence of great earthquakes. Within an optimization method, we use plate motion data to better constrain uncertain mantle parameters. For the optimization problem characterizing the maximum a posteriori rheological parameters we derive gradients using adjoints and expressions to approximate the posterior distributions for stresses within plate boundaries. We apply these methods to a 2-D cross section from the western to eastern Pacific, with temperature distributions and fault zone geometries developed primarily from seismic and plate motion data. We find that the best-fitting stress exponent, n, is about 2.8 and the yield stress about 100 MPa or less. The normal stress on the interplate fault zones is about 100 MPa and the shear stresses about 10 MPa or less.

Rudi, Johann (ORCID:0000000265639265)↗