SEARCH · Engineering Papers

Results for “Simulation framework”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,657 records · Page 92

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗

DOE OSTI2023

A probabilistic-phase field model for the fracture of brittle materials

Abstract We develop a computational method to determine the failure probability of brittle materials under general mechanical loading conditions. The method is a combination of two parts: (1) numerical simulations of materials with multiple cracks using phase field theory, where the complete fracture process is viewed as ‘damage percolation’ along critical paths or clusters of cracks, rather than the traditional weak-link failure mechanism of Weibull, and (2) an extension of the Batdorf statistical theory of fracture to finite domains, where it is implemented within the finite element framework. The results of phase-field simulations at the ‘percolation threshold’ are used as failure data in the Batdorf theory to determine the overall probability of failure. The input to this approach is the size distribution of cracks in a pristine material. An example is shown, where alumina samples that were previously tested by Abe and coworkers (Abeet al2003J. Am. Ceram. Soc.861019–21) in four-point loading are compared to the results of our numerical simulations. The approach developed here has the advantage of being extendable to more complex thermomechanical loading.

Materials Science↗

NASA NTRSDate not supplied

CFD Model Development of a Cryogenic Storage Tank Self-Pressurization in Normal Gravity and Validation against SHIIVER Experiment

Two-phase flow and heat transfer simulations with interfacial phase change of the Structural Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) self-pressurization experiment were conducted using storage tank CFD model in the framework of the ANSYS Fluent CFD code. The simulations were performed for the 70% fill level case with MLI on domes and no vapor cooling. All the phase change calculations in these simulations were generated by in-house Schrage-based evaporation-condensation model. The calculations were performed using both the Volume of Fluid (VOF) and Sharp Interface multiphase 2D axisymmetric models. A number of parametric and sensitivity studies were performed to check the various aspects of the CFD model. These studies helped to understand the effects of varying several parameters on the tank pressure and temperature during self-pressurization. Turbulence modeling; turbulence damping at the interface; and using constant vs. temperature dependent fluid properties were shown to have the most profound influence on predicted tank pressures and temperatures. Current study indicates that including phase change at the interface into the computational model is crucial for accurate prediction of the tank self-pressurization process. The effect of accommodation coefficient was also studied. Tank pressure values predicted by the VOF, and Sharp Interface models are within 4% of the experimental ones.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

CFD Model Development of a Cryogenic Storage Tank Self-Pressurization in Normal Gravity and Validation against SHIIVER Experiment

Two-Phase flow and heat transfer simulations with interfacial phase change of the Structural Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) self-pressurization experiment were conducted using storage tank CFD model in the framework of the ANSYS Fluent CFD code. The simulations were performed for the 70% fill level case with MLI on domes and no vapor cooling. All the phase change calculations in these simulations were generated by in-house Schrage-based evaporation-condensation model. The calculations were performed using both the VOF and Sharp Interface multiphase 2Daxisymmetric models. A number of parametric and sensitivity studies were performed to check the various aspects of the CFD model. These studies helped to understand the effects of varying several parameters on the tank pressure and temperature during self-pressurization. These parameters include: the effect of turbulence modeling and turbulence damping at the interface; the effect of constant vs. temperature dependent fluid properties; the effect of accommodation coefficient; the effect of modeling phase change at the interface and the effect of the fill level.

Computational Fluid Dynamics↗

DOE OSTI2020

A learning-based time-efficient framework for building energy performance evaluation

Not Available

Bhattacharya, Saptarshi↗

DOE OSTI2025

Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework

Owing to uncertainties in convective microphysics processes, improving parameterizations in Earth system models (ESMs) can benefit from observationally constrained cases suitable for scaling between cloud-resolving models and ESMs. We propose a benchmark large-eddy simulation (LES) cumulus congestus case study from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP 2 Ex) for evaluating and improving ESMs in single-column model (SCM) mode. We seek observational constraints using novel polarimetric retrievals and in situ cloud microphysics measurements. Simulations using bulk and bin microphysics initialized with observed aerosol profiles are compared to cloud-top retrievals of cloud droplet effective radius (R eff ), effective variance (ν eff ), and number concentration (N d ) from the airborne Research Scanning Polarimeter (RSP). Both schemes reproduce characteristics of cloud-top N d and R eff that increase and decrease with altitude, respectively. Cloud-top N d is low-biased relative to RSP retrievals in both schemes, potentially due to limitations in both simulations and retrieval assumptions. Cloud-top R eff is low-biased in the bulk scheme but reasonably reproduced by the bin scheme. Profiles of N d and R eff are sensitive to the collision–coalescence process and the vertical variation in aerosol size distribution. Comparison of simulated and in situ droplet size distributions (DSDs) shows that, to first order, integrated moments are always sensitive to sizes < ~ 30 µm and can also be sensitive to larger sizes if the DSDs are sufficiently broad, with implications for the assumed maximum observed size retrieved by the RSP. The bin scheme captures the observed extended tail of the DSD, while the bulk scheme is unable to due to parametric constraints. Differences in expected relationships between in situ measurements of cloud cores and cloud-top retrievals by RSP demonstrate difficulty in constraining well the case presented herein. Finally, a thermal-tracking framework demonstrates that the dilution of N d throughout a thermal's lifetime is heavily determined by collision–coalescence and the height-varying aerosol distribution and that, in the absence of these, the impact of entrainment on diluting N d is largely offset by secondary activation. Implications for evaluating warm-phase convective microphysics schemes in ESMs and translating results for use on global, space-based polarimetry platforms are discussed.

Stanford, McKenna Wallace [Columbia Univ., New Yor↗

DOE OSTI2021

A Flexible and Generic Functional Mock-up Unit Based Threat Injection Framework for Grid-interactive Efficient Buildings: A Case Study in Modelica

Grid-interactive efficient buildings (GEBs) have been considered as an important asset to support the power grid reliability by utilizing the demand flexibility offered by GEBs. GEBs are enabled by advances in sensors and controls, and the communication between building equipment, whole buildings, and the grid. The integration of different building technologies and network-based communication system makes GEBs vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the impact of threats on the system performance. This paper proposes a generic and flexible threat injection framework for commonly-used building energy simulators such as EnergyPlus and Modelica to support threat modeling and evaluation. This framework leverages functional mock-up unit (FMU) to develop a general modeling interface for threat injection and simulation. A numerical case study using Modelica as a building energy simulator is conducted to demonstrate the capability of the framework for supporting single/multiple-order threat modeling and simulation of a GEB. Four threats and their combinations are injected on a Modelica-based threat-free building energy and control system, including operating supply fan at its full speed, remotely cycling the chiller on and off, blocking the chiller from receiving the chilled water supply temperature setpoints, and hijacking the global zone air temperature setpoint. Simulation results show that the cyber-attack that leads to short-term signal blocking has small effects on the system operation due to the "self-healing" feature of the heating, ventilation, and air-conditioning (HVAC) interactive control system. The threat that takes control of resetting the global zone air temperature setpoints has the most adverse impact on the system energy use, peak power demand, thermal comfort and the provision of demand flexibility. The combination of four threats have aggregative effects on the system but the effects are less than the additive effects of the individual threat.

Fu, Yanyang↗

NASA NTRSDate not supplied

A CFD Framework for Film Boiling and Quenching in Cryogenic Chilldown

Tests of cryogenic chilldown in a vertical stainless-steel tube are simulated using computational fluid dynamics (CFD). Subcooled liquid nitrogen enters a room-temperature tube at one end, resulting in film boiling and eventual quenching of the tube walls. A CFDbased modeling framework employing the mixture multiphase model was developed to simulate this chilldown process and was applied to validate it against experimental data. Extensive sensitivity studies have been performed to create a consistent modeling framework, and a range of test conditions are simulated to assess the modeling capability. The importance of the rewetting temperature and its relation to the foam limit temperature is discussed and considered in the CFD framework. The presented results demonstrate a qualitatively consistent representation of the film boiling and quenching regime. However, the quantitative accuracy of the simulations varies with the test conditions. In addition, simulated distributions of the vapor volume fraction are presented, but the current lack of experimental volume fraction data prevents comparisons.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

A CFD Framework for Film Boiling and Quenching in Cryogenic Chilldown

Computational Fluid Dynamics↗

DOE OSTI2025

Extreme radiation emission regime for electron beams in strong focusing ion channels and undulators

A fundamental comparison between a magnetic undulator and an ion channel, or betatron, radiation from relativistic electrons is presented. While conventional theories nominally range from the undulator (𝐾 <1) to the wiggler (𝐾 >1) regime, they are only applicable for sufficiently large Lorentz factors (𝛾 0 ≫𝐾). They therefore do not account for high 𝐾/𝛾 0 cases, for which we show that particle trajectories and radiation characteristics strongly deviate from the linear predictions in both magnetic undulators and ion channels. This problem arises from the fundamental differences between a magnetostatically and electrostatically induced oscillation. A reformulation of both the ion channel betatron wavelength and amplitude, as well as the same parameters in a magnetic undulator, permits us to compare cases with equivalent oscillation period and amplitude in the two different scenarios. The notable differences in spectral features of the two radiation mechanisms can then be addressed via numerical simulations of single particle as well as full beam dynamics. Additionally, we identify and quantify a novel transverse orbit precession effect in ion channels for particles with initial angular momentum relative to the device axis. This effect, which is significant in cases of strong transverse kinetic energy oscillations, alters both the radiation divergence and the beam emittance. In this paper, we present this new theoretical framework and compare its results with numerical simulation applied to realizable experimental tests of such radiation sources.

Frazzitta, Andrea [University of Rome “La Sapienza↗

NASA NTRS2024

Design, Development, and Use of a Lunar Lander Simulation for NASA's Artemis Program

This paper describes the design, development, and initial use of a generalized and configurable lunar lander simulation to support NASA’s Artemis Program. This simulation is being developed for the Crew Compartment Office (CrewCo) in the Human Landing Systems (HLS) program and is called the HLS CrewCo Lander Simulation (HCLS). The HCLS provides insight into the challenges associated with returning humans to the Moon and the particular difficulties of operating at the Lunar South Pole. A generalized lunar landing spacecraft based on a government reference design has been modeled but the simulation can be modified and adapted to model vendor designs as well. The simulation architecture and toolsets provide a flexible framework that allows for quickly prototyping and evaluating various aspects of a piloted lunar landing system. This includes the modeling of all principal human controlled flight phases: rendezvous and docking with crew transfer systems; docked orbital outpost operations; undocking and lunar transfer; lunar orbital operations; lunar deorbit, descent, and landing (DDL); lunar surface operations; lunar ascent; and return to the orbital outpost. The flexibility of the simulation allows for the integrated evaluation of potential vehicle subsystems, crew displays, guidance and control modes, and trajectory designs. The purpose of the HCLS is not to design the ideal lunar lander, but rather to understand the strengths and weaknesses of vehicle design choices.

Edwin Z Crues↗

NASA NTRS2024

Design, Development, and Use of a Lunar Lander Simulation for NASA’s Artemis Program

James Gentile↗

DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗

DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗

NASA NTRS2023

A Framework for Evaluating Distributed Electric Propulsion on the SUSAN Electrofan Aircraft

This work presents a framework for evaluating models and algorithms for Distributed Electric Propulsion (DEP) on the SUSAN Electrofan Aircraft. Throughout the development of the SUSAN aircraft, the performance of various configurations of the aircraft will need to be analyzed. However, the static behavior alone is not sufficient to describe the performance of these configurations. Therefore, simulation with fully integrated subsystem models is required. The proposed framework considers the vehicle aerodynamic, propulsion, and control subsystems. The presented framework automatically generates control laws for any vehicle configuration in response to changes in these subsystems. To compare these different vehicle configurations, various time and frequency domain performance metrics are compared. Three different system modifications are used as cases to evaluate this framework. The first modification integrates the propulsion control system with the flight controller to enable differential thrust without stalling the main engine. This evaluation case is used to validate the framework for aircraft configurations with coupled subsystems. The second modification compares the effect of the vertical tail size on open and closed loop performance. This evaluation case is used to validate the framework for controlling different configurations and tuning towards comparable closed loop performance despite changes to the aircraft's aerodynamic model. The third modification implements two different control allocation schemes. This evaluation case demonstrates the framework's ability to evaluate allocation modifications needed to take advantage of DEP. The first evaluation case is used to show that controller integration enables differential thrust, improving realized wingfan bandwidth by up to 40\% in simulation. The second evaluation case demonstrates that the framework can stabilize the reduced tail size aircraft with closed loop control. The third evaluation case demonstrates that a pseudoinverse control allocation scheme improves lateral velocity settling time by approximately 17~seconds over a symmetric-thrust allocation. These cases show that the framework is useful for evaluating the performance of integrated system designs, enabling analyses of new models and algorithms for the SUSAN distributed electric propulsion vehicle.

Nicholas C Ogden↗

NASA NTRS2018

Design of a Modular Monolithic Implicit Solver for Multi-Physics Applications

The design of a modular multi-physics high-order space-time finite-element framework is presented together with its extension to allow monolithic coupling of different physics. One of the main objectives of the framework is to perform efficient high- fidelity simulations of capsule/parachute systems. This problem requires simulating multiple physics including, but not limited to, the compressible Navier-Stokes equations, the dynamics of a moving body with mesh deformations and adaptation, the linear shell equations, non-re effective boundary conditions and wall modeling. The solver is based on high-order space-time - finite element methods. Continuous, discontinuous and C1-discontinuous Galerkin methods are implemented, allowing one to discretize various physical models. Tangent and adjoint sensitivity analysis are also targeted in order to conduct gradient-based optimization, error estimation, mesh adaptation, and flow control, adding another layer of complexity to the framework. The decisions made to tackle these challenges are presented. The discussion focuses first on the "single-physics" solver and later on its extension to the monolithic coupling of different physics. The implementation of different physics modules, relevant to the capsule/parachute system, are also presented. Finally, examples of coupled computations are presented, paving the way to the simulation of the full capsule/parachute system.

Carton De Wiart, Corentin↗

DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗

DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

organic↗