Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computational Icing”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections

Climate model projections have previously been used to compute ice shelf basal melt rates in ice sheet models, but the strategies employed – e.g., ocean input, parameterization, calibration technique, and corrections – have varied widely and are often ad hoc. Here, a methodology is proposed for the calculation of circum-Antarctic basal melt rates for floating ice, based on climate models, that is suitable for ISMIP6, the Ice Sheet Model Intercomparison Project for CMIP6 (6th Coupled Model Intercomparison Project). The past and future evolution of ocean temperature and salinity is derived from a climate model by estimating anomalies with respect to the modern day, which are added to a present-day climatology constructed from existing observational datasets. Temperature and salinity are extrapolated to any position potentially occupied by a simulated ice shelf. A simple formulation is proposed for a basal melt parameterization in ISMIP6, constrained by the observed temperature climatology, with a quadratic dependency on either the nonlocal or local thermal forcing. Two calibration methods are proposed: (1) based on the mean Antarctic melt rate (MeanAnt) and (2) based on melt rates near Pine Island's deep grounding line (PIGL). Future Antarctic mean melt rates are an order of magnitude greater in PIGL than in MeanAnt. The PIGL calibration and the local parameterization result in more realistic melt rates near grounding lines. PIGL is also more consistent with observations of interannual melt rate variability underneath Pine Island and Dotson ice shelves. This work stresses the need for more physics and less calibration in the parameterizations and for more observations of hydrographic properties and melt rates at interannual and decadal timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multicycle large-eddy simulations of a direct-injection hydrogen-fueled optical engine

Hydrogen (H 2 ) is a carbon-free chemical energy carrier and one promising solution for achieving effective decarbonization of the transportation sector, particularly for internal combustion engines (ICEs). With a focus on ICEs, and compared to port-fuel injection, direct injection (DI) of gaseous H 2 during the compression stroke offers potential advantages, which include backfire avoidance and reduction of preignition occurrence. In these last two decades, much research, experimental and numerical, has been devoted to understanding H 2 's mixing and combustion processes in ICEs. Computational fluid dynamics modeling efforts commonly rely on unsteady Reynolds-averaged Navier Stokes (URANS) turbulence frameworks, mostly due to their computational affordability. However, many authors have pointed out the opportunity to perform large-eddy simulations (LESs) to investigate the cyclic variability of H 2 engines and assess potential advantages of using LES in place of URANS, especially for lean operation. This study addresses this knowledge gap and presents a computational fluid dynamics (CFD) study of the H 2 DI process in an optical engine operating at relatively low tumble conditions, using multicycle LESs. In conclusion, the manuscript presents a thorough validation of the results against experimental data available from the literature as well as direct comparison with URANS, demonstrating the feasibility of multicycle LESs for CFD modeling of DI H 2 -fueled ICEs.

Direct injection↗
DOE OSTI2022

Influence of NOx chemistry on the prediction of natural gas end-gas autoignition in CFD engine simulations

Natural gas (NG) represents a promising low-cost/low-emission alternative to diesel fuel when used in high-efficiency internal combustion engines. Advanced combustion strategies utilizing high EGR rates and controlled end-gas autoignition can be implemented with NG to achieve diesel-like efficiencies; however, to support the design of these next-generation NG ICEs, computational tools, including single- and multi-dimensional simulation packages will need to account for the complex chemistry that can occur between the reactive species found in EGR (including NOx) and the fuel. Research has shown that NOx plays an important role in the promotion/inhibition of large hydrocarbon autoignition and when accounted for in CFD engine simulations, can significantly improve the prediction of end-gas autoignition for these fuels. However, reduced NOx-enabled NG mechanisms for use in CFD engine simulations are lacking, and as a result, the influence of NOx chemistry on NG engine operation remains unknown. Here, we analyze the effects of NOx chemistry on the prediction of NG/oxidizer/EGR autoignition and generate a reduced mechanism of a suitable size to be used in engine simulations. Results indicate that NG ignition is sensitive to NOx chemistry, where it was observed that the addition of EGR, which included NOx, promoted NG autoignition. The modified mechanism captured well all trends and closely matched experimentally measured ignition delay times for a wide range of EGR rates and NG compositions. Here, the importance of C2-C3 chemistry is noted, especially for wet NG compositions containing high fractions of ethane and propane. Finally, when utilized in CFD simulations of a Cooperative Fuels Research (CFR) engine, the new reduced mechanism was able to predict the knock onset crank angle (KOCA) to within one crank angle degree of experimental data, a significant improvement compared to previous simulations without NOx chemistry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks

Abstract This work develops a novel ice detection framework specifically suitable for rotorcraft using computational aeroacoustics and Bayesian neural networks. In an offline phase of the work, the acoustic signature of glaze and rime ice shapes on an oscillating wing are computed. In addition, the aerodynamic performance indicators corresponding to the ice shapes are also monitored. These performance indicators include the lift, drag, and moment coefficients. A Bayesian neural network is subsequently trained using projected Stein variational gradient descent to create a mapping from the acoustic signature generated by the iced wings to predict their performance indicators along with quantified uncertainty that is highly important for time- and safety-critical decision-making scenarios. While the training is carried out fully offline, usage of the Bayesian neural network to make predictions can be conducted rapidly online allowing for an ice detection system that can be used in real time and in-flight.

42 ENGINEERING↗
DOE OSTI2025

Ice sculpting: An artificial spin ice Tutorial on controlling microstate and geometry for magnonics and neuromorphic computing

Artificial spin ice, arrays of strongly interacting nanomagnets, are complex magnetic systems with many emergent properties, rich microstate spaces, intrinsic physical memory, high-frequency dynamics in the GHz range, and compatibility with a broad range of measurement approaches. This Tutorial article aims to provide the foundational knowledge needed to understand, design, develop, and improve the dynamic properties of artificial spin ice. Special emphasis is placed on introducing the theory of micromagnetics, which describes the complex dynamics within these systems, along with their design, fabrication methods, and standard measurement and control techniques. The article begins with a review of the historical background, introducing the underlying physical phenomena and interactions that govern artificial spin ice. We then explore the standard experimental techniques used to prepare the microstate space of the nanomagnetic array and to characterize magnetization dynamics, both in artificial spin ice and more broadly in ferromagnetic materials. Finally, we introduce the basics of neuromorphic computing applied to the case of artificial spin ice systems with a goal to help researchers new to the field grasp these exciting new developments.

Sultana, Rawnak [Univ. of Delaware, Newark, DE (Un↗
DOE OSTI2023

Normality of I-V Measurements Using ML

There is an increased interest in instrument-computing ecosystems (ICEs) that support science workflows empowered by AI-automated experiments and computations in diverse areas. In particular, electrochemistry ICEs are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell, as illustrated in Fig. 1. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. In particular, we developed Python wrappers for Application Programming Interfaces (APIs) of instrument commands and Pyro client-server modules that enable them to be executed from remote computers. The entire workflow is orchestrated by a Jupyter notebook running on a remote computer.

Al Najjar, Anees↗
DOE OSTI2022

Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations

In this work, we studied the phase diagram for the TIP4P/Ice water model using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice–liquid free energy differences from biased coexistence simulations that reversibly sample the melting and growth of layers of ice. We computed a total of 19 melting points for five different ice polymorphs, which are in excellent agreement with the melting lines obtained from the integration of the Clausius–Clapeyron equation. For proton-ordered and fully proton-disordered ice phases, the results are in very good agreement with previous calculations based on thermodynamic integration. For the partially proton-disordered ice III, we find a large increase in stability that is in line with previous observations using direct coexistence simulations for the TIP4P/2005 model. This issue highlights the robustness of the approach employed here for ice polymorphs with diverse degrees of proton disorder. Our approach is general and can be applied to the calculation of other complex phase diagrams.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. Here, by tuning nanoarray dimensions we have achieved macrospin–vortex bistability and demonstrated a four-state metamaterial spin system, the ‘artificial spin-vortex ice’ (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof-of-concept framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_e$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet-18 performed best, achieving $R^2$ values of 0.99 and 0.98 for $ρ_e$ and $A_e$, respectively, and MAE of 0.10 for mathematical equation in single view tasks. Stereo view ResNet-18 further reduced RMSE by 40% for $ρ_e$ and $A_e$ and reduced MAE by 0.08 for $N_b$. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Ko, J. [Columbia Univ., New York, NY (United State↗
DOE OSTI2026

LANL Institutional Computing Report: Towards a digital twin of Arctic sea ice

Our goal is to develop a digital twin of Arctic sea ice that combines high-resolution predictive modeling with observational products. This effort will provide an optimized model for use in seasonal to sub-seasonal forecasting and a tool for policymakers to better anticipate, plan for, and mitigate the national security impacts of rapidly changing Arctic conditions. The modeling component uses the Discrete Element Model for Sea Ice (DEMSI), which uses discrete elements to represent the sea ice with an explicit representation of forces between them.

58 GEOSCIENCES↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multifidelity deep operator networks for data-driven and physics-informed problems

Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results

Abstract. This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations – a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100 km atmosphere and land, a 0.125∘ river-routing model, and 14→60 km ocean and sea ice. By design, the computational cost of NARRM is ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 10 %–20 % of a globally uniform high-resolution model at 25 km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air–sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land–atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

54 ENVIRONMENTAL SCIENCES↗