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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.

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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning Aided Modeling of Granular Materials: A Review

Artificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

42 ENGINEERING↗
NASA NTRS1992

SSME Streamtube Evaluation Program (SSTEP)

An analytical model of the Space Shuttle Main Engine (SSME) called SSME Streamtube Evaluation Program (SSTEP) has been developed based upon the assumption that the propellant flows through the main combustion chamber can be represented by a bundle of parallel streamtubes. The motivation for the development of SSTEP lies in the desire to gain a basic understanding of the engine performance effects of several common SSME hardware modifications. Specifically, this model has been used to evaluate the changes in performance due to boundary layer coolant hole enlargement, LOX post plugging, acoustic cavity elimination, baffle removal, and main combustion chamber coolant leakage. The results show a good general agreement with the available test data suggesting at least a qualitative agreement between SSTEP modeling and actual engine performance. Through the use of several adjustment factors, which represent relaxations of the SSTEP formulation assumptions, it is shown that the test data can be very closely matched and that SSTEP can be used as a performance prediction tool.

Greene, William D.↗
NASA NTRS2013

Nuclear Thermal Rocket Simulation in NPSS

Four nuclear thermal rocket (NTR) models have been created in the Numerical Propulsion System Simulation (NPSS) framework. The models are divided into two categories. One set is based upon the ZrC-graphite composite fuel element and tie tube-style reactor developed during the Nuclear Engine for Rocket Vehicle Application (NERVA) project in the late 1960s and early 1970s. The other reactor set is based upon a W-UO2 ceramic-metallic (CERMET) fuel element. Within each category, a small and a large thrust engine are modeled. The small engine models utilize RL-10 turbomachinery performance maps and have a thrust of approximately 33.4 kN (7,500 lbf ). The large engine models utilize scaled RL-60 turbomachinery performance maps and have a thrust of approximately 111.2 kN (25,000 lbf ). Power deposition profiles for each reactor were obtained from a detailed Monte Carlo N-Particle (MCNP5) model of the reactor cores. Performance factors such as thermodynamic state points, thrust, specific impulse, reactor power level, and maximum fuel temperature are analyzed for each engine design.

Belair, Michael L.↗
DOE OSTI2016

Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. Here, we argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections.This makes current models likely to miss important feedbacks in the real Earth–Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. Lastly, the importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth–Human system models for devising effective science-based policies and measures to benefit current and future generations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Potassium carbonate decomposition modeling within rotating detonation engines for direct power extraction applications

We report that recently, there has been a significant interest in detonation-based combustion systems, such as rotating detonation engines (RDE), due to potential performance advantages in propulsion and energy applications. This includes improvements in thermodynamic efficiency, inherently high gas velocities, and the possibility of achieving an increase in total pressure (i.e. pressure gain) through the combustion process. Due to approximating a constant volume combustion process, these devices also tend to exhibit extremely high local gas temperatures relative to a comparable constant pressure combustor. These advantages overlap well with desirable performance characteristics of direct power extraction (DPE) technologies, such as a magnetohydrodynamic (MHD) generator. Typically, in DPE systems hot combustion products are seeded with an easily ionizable material such as potassium carbonate (K 2 CO 3 ) in order to boost the electrical conductivity. However, due to the short gas residence times within an RDE, it was unclear whether forming an electrically conductive combustion plasma would be feasible for integration with a downstream MHD generator. A model is presented which describes the heating, decomposition, and ionization of solid K 2 CO 3 particles and aqueous solutions of K 2 CO 3 in water, for a given initial particle diameter. This model was combined with available computational fluid dynamics (CFD) data for an oxygen-methane RDE in a one-way coupled Eulerian-Lagrangian framework to predict particle trajectories and the corresponding heating, decomposition, and ionization histories. Electrical conductivities were computed using a previously developed model, and a method was proposed to determine an equivalent average electrical conductivity. Results show that particle sizes below ~30 µm are able to fully decompose before reaching the exit of the RDE. While the one-way coupled nature of the simulations precluded rigorous evaluation of the effects of seed material on detonability, a substantial temperature reduction is expected at the detonation wave front due to heating and decomposition. A preliminary comparison is presented between the RDE and an equivalent constant pressure adiabatic combustor, showing a potential performance advantage for the RDE.

42 ENGINEERING↗
DOE OSTI2012

Next Generation Wind Turbine

The goal of this collaborative effort between Western New England University's College of Engineering and FloDesign Wind Turbine (FDWT) Corporation to wok on a novel areodynamic concept that could potentially lead to the next generation of wind turbines. Analytical studies and early scale model tests of FDWT's Mixer/Ejector Wind Turbine (MEWT) concept, which exploits jet-age advanced fluid dynamics, indicate that the concept has the potential to significantly reduce the cost of electricity over conventional Horizontal Axis Wind Turbines while reducing land usage. This project involved the design, fabrication, and wind tunnel testing of components of MEWT to provide the research and engineering data necessary to validate the design iterations and optimize system performance. Based on these tests, a scale model prototype called Briza was designed, fabricated, installed and tested on a portable tower to investigate and improve the design system in real world conditions. The results of these scale prototype efforts were very promising and have contributed significantly to FDWT's ongoing development of a product scale wind turbine for deployment in multiple locations around the U.S. This research was mutually beneficial to Western New England University, FDWT, and the DOE by utilizing over 30 student interns and a number of faculty in all efforts. It brought real-world wind turbine experience into the classroom to further enhance the Green Engineering Program at WNEU. It also provided on-the-job training to many students, improving their future employment opportunities, while also providing valuable information to further advance FDWT's mixer-ejector wind turbine technology, creating opportunities for future project innovation and job creation.

17 WIND ENERGY↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
NASA NTRS1988

A crystallographic model for nickel base single crystal alloys

The purpose of this research is to develop a tool for the mechanical analysis of nickel-base single-crystal superalloys, specifically Rene N4, used in gas turbine engine components. This objective is achieved by developing a rate-dependent anisotropic constitutive model and implementing it in a nonlinear three-dimensional finite-element code. The constitutive model is developed from metallurgical concepts utilizing a crystallographic approach. An extension of Schmid's law is combined with the Bodner-Partom equations to model the inelastic tension/compression asymmetry and orientation-dependence in octahedral slip. Schmid's law is used to approximate the inelastic response of the material in cube slip. The constitutive equations model the tensile behavior, creep response and strain-rate sensitivity of the single-crystal superalloys. Methods for deriving the material constants from standard tests are also discussed. The model is implemented in a finite-element code, and the computed and experimental results are compared for several orientations and loading conditions.

Dame, L. T.↗
DOE OSTI2021

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

In-Cylinder Diagnostics to Overcome Efficiency Barriers in Natural Gas Engines (LDRD 200166)

The high-level objective of this project is to solve national-security problems associated with petroleum use, cost, and environmental impacts by enabling more efficient use of natural-gas-fueled internal combustion engines. An improved science-base on end-gas autoignition, or “knock,” is required to support engineering of more efficient engine designs through predictive modeling. An existing optical diesel engine facility is retrofitted for natural gas fueling with laser-spark-ignition combustion to provide in-cylinder imaging and pressure data under knocking combustion. Zero-dimensional chemical-kinetic modeling of autoignition, adiabatically constrained by the measured cylinder pressure, isolates the role of autoignition chemistry. OH* chemiluminescence imaging reveals six different categories of knock onset that depend on proximity to engine surfaces and the in-cylinder deflagration. Modeling results show excellent prediction regardless of the knock category, thereby validating state-of-the-art kinetic mechanisms. The results also provide guidance for future work to build a science base on the factors that affect the deflagration rate.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1992

Application of engineering codes to the assessment of spacecraft charging induced hazards

One of the major hurdles to developing a better understanding of the environmentally induced interactions in spacecraft systems (spacecraft charging) is the lack of simple tools to analyze the behavior of the spacecraft in a sub-storm environment. The available tool, NASA Charging Analyzer Program (NASCAP), is expensive to use and treats only half of the problem - surface effects only. In response to this need, we have developed, under our IRAD Program, a set of engineering tools that operate on personal computers and addresses both the surface and bulk charging aspects of the interactions. These engineering codes utilize circuit analysis techniques and model simplifications to allow rapid computations. The codes were compared to both NASCAP predictions and to bulk charging data with excellent agreement for both phenomena. The models used in our engineering codes are shown, and in the engineering codes, the models are defined in terms of plates or triangles in an unconfined space. The predictions are based upon current balances to each plate. The code has routines to allow interactions between the plates to simulate 'barrier' effects. The agreement for the predicted differential surface voltages for the model is given. In conducting the comparison of engineering codes to NASCAP predictions, it was found that some inconsistencies existed in the NASCAP code that are not obvious to the user. The use of the grid imposes constraints that voltage gradients exist between the grid points. This allows the existence of voltage gradients in conductors and influences surface resistivity predictions. These points are discussed along with the predictions for treating these inconsistencies properly. The model predictions for a variable intensity substorm is also presented.

Stevens, N. J.↗
NASA NTRS1988

Regenerative life support system research

Sections on modeling, experimental activities during the grant period, and topics under consideration for the future are contained. The sessions contain discussions of: four concurrent modeling approaches that were being integrated near the end of the period (knowledge-based modeling support infrastructure and data base management, object-oriented steady state simulations for three concepts, steady state mass-balance engineering tradeoff studies, and object-oriented time-step, quasidynamic simulations of generic concepts); interdisciplinary research activities, beginning with a discussion of RECON lab development and use, and followed with discussions of waste processing research, algae studies and subsystem modeling, low pressure growth testing of plants, subsystem modeling of plants, control of plant growth using lighting and CO2 supply as variables, search for and development of lunar soil simulants, preliminary design parameters for a lunar base life support system, and research considerations for food processing in space; and appendix materials, including a discussion of the CELSS Conference, detailed analytical equations for mass-balance modeling, plant modeling equations, and parametric data on existing life support systems for use in modeling.

Source record↗
NASA NTRS2014

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.

Daigle, Matthew↗
DOE OSTI2020

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling

Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.

Surrogate model, reduced order model, sensitivity ↗