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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Machine Learning Force Field Parameters from Ab Initio Data

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. Our results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimal structure and parameter learning of Ising models

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, which is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Contraction Options and Optimal Multiple-Stopping in Spectrally Negative Lévy Models

This paper studies the optimal multiple-stopping problem arising in the context of the timing option to withdraw from a project in stages. The profits are driven by a general spectrally negative Lévy process. This allows the model to incorporate sudden declines of the project values, generalizing greatly the classical geometric Brownian motion model. We solve the one-stage case as well as the extension to the multiple-stage case. The optimal stopping times are of threshold-type and the value function admits an expression in terms of the scale function. A series of numerical experiments are conducted to verify the optimality and to evaluate the efficiency of the algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1981

Pilot/vehicle model analysis of visual and motion cue requirements in flight simulation

The optimal control model for pilot/vehicle analysis is used to explore the effects of a CGI visual system and motion system dynamics on helicopter hover simulation fidelity. This is accomplished by expanding the perceptual aspects of the model to include motion sensing and by relating CGI parameters to information processing parameters of the model. Simulator fidelity is examined by comparing predicted performance and workload for flight with that predicted for various simulator configuration. The results of the analysis suggest that simulator deficiencies or a reasonable nature (by current standards) can result in substantial performance and/or workload infidelity. Both CGI and motion system effects are significant for this task. There is also a distinct interaction between the two sources of pilot cues. In particular, the presence of motion reduces the sensitivity to CGI limitations.

Lancraft, R.↗
NASA NTRS1981

Modeling human tracking error in several different anti-tank systems

An optimal control model for generating time histories of human tracking errors in antitank systems is outlined. Monte Carlo simulations of human operator responses for three Army antitank systems are compared. System/manipulator dependent data comparisons reflecting human operator limitations in perceiving displayed quantities and executing intended control motions are presented. Motor noise parameters are also discussed.

Kleinman, D. L.↗
DOE OSTI2018

Optimal Portfolio Management in a Modified Constant Elasticity of Variance Model

We investigate the optimal management of a portfolio consisting of a risk asset and a risk-free bond. The asset dynamics is specified by the M-CEV model. Assuming that the investor has a power utility function, we derive an explicit analytical portfolio-management formula that contains confluent hypergeometric functions. The asymptotic form of the proposed portfolio management strategy is obtained, as well as approximate formulas consisting only of elementary functions. We additionally present an application of our results in the context of algorithmic statistical-arbitrage strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

Herein this article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Valued peaks: Sustainable water allocation for small hydropower plants in an era of explicit ecological needs

Optimizing hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a dynamic, constrained optimization framework for small hydropower plants (SHPs) to evaluate trade-offs between economic profitability and socio-ecological requirements. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service estimate to evaluate different environmental flow scenarios. The optimization process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5 %. An annual increase of 8 % in recreational fishing visits offsets the revenue loss. In conclusion, the developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

13 HYDRO ENERGY↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Differences Between OCO‐2 and GOME‐2 SIF Products From a Model‐Data Fusion Perspective

Space-borne retrievals of solar-induced chlorophyll fluorescence (SIF) over land surfaces have recently become a resource for studying and quantifying the broad scale dynamics of gross carbon uptake (gross primary productivity—GPP) across ecosystems. To prepare for the assimilation of SIF data in terrestrial biosphere models, we examine how differences between SIF products (due to differences in acquisition characteristics and processing chain) may affect the optimization of model parameters and the resultant GPP estimate. We compare recent daily mean SIF products (one from the Orbiting Carbon Observatory-2 [OCO-2] and two from the Global Ozone Monitoring Experiment–2 [GOME-2], GlobFluo [GF] and NASA-v28 [N28], missions), averaged at 0.5° × 0.5° spatial resolution and 16-day temporal resolution, at the biome level. Phase differences between these products are relatively small. A first-order correction of the difference in spectral sampling between the two instruments shows that OCO-2 and N28 are consistent in terms of magnitude and amplitude, while GF is twice as large as the others. Using a bias-blind toy data assimilation framework, we analyze how biases between SIF products, and between model and products, can be partially alleviated by optimizing the slope and intercept parameters of a linear GPP-SIF operator. As observation biases can transfer to biases in other optimized process-based parameters and to modeled carbon fluxes— thereby resulting in unidentified inaccurate parameter values—we argue that potential SIF biases should be treated cautiously in real-world experiments in order to achieve realistic and reliable future simulations.

Gross primary production↗
DOE OSTI2019

Dynamic real‐time optimization for a CO 2 capture process

Growing awareness of climate change has led to increased focus on new energy processes that mitigate generation of CO 2 , or provide for its efficient capture. To enable the development of these processes, advanced modeling and optimization platforms have been created. New capabilities among platforms include efficient solution strategies for online optimization problems. For real time, dynamic optimization of CO 2 capture processes, we demonstrate the application of the Python‐based Pyomo platform to facilitate realization of Moving Horizon Estimation and Nonlinear Model Predictive Control, through novel nonlinear optimization and sensitivity strategies. This capability allows large scale, first principle models to be applied for online optimization. Here, we introduce the Control and Adaptation with Predictive Sensitivity Enhancements (CAPRESE) framework to demonstrate this framework for advanced energy processes. Moreover, we present two case studies: profile tracking with detailed tray‐by‐tray distillation models and Bubbling Fluidized Bed reactors for CO 2 capture. © 2018 American Institute of Chemical Engineers AIChE J , 65: e16511 2019

Thierry, David↗
NASA NTRS1993

Automated Finite Element Modeling of Wing Structures for Shape Optimization

The displacement formulation of the finite element method is the most general and most widely used technique for structural analysis of airplane configurations. Modem structural synthesis techniques based on the finite element method have reached a certain maturity in recent years, and large airplane structures can now be optimized with respect to sizing type design variables for many load cases subject to a rich variety of constraints including stress, buckling, frequency, stiffness and aeroelastic constraints (Refs. 1-3). These structural synthesis capabilities use gradient based nonlinear programming techniques to search for improved designs. For these techniques to be practical a major improvement was required in computational cost of finite element analyses (needed repeatedly in the optimization process). Thus, associated with the progress in structural optimization, a new perspective of structural analysis has emerged, namely, structural analysis specialized for design optimization application, or.what is known as "design oriented structural analysis" (Ref. 4). This discipline includes approximation concepts and methods for obtaining behavior sensitivity information (Ref. 1), all needed to make the optimization of large structural systems (modeled by thousands of degrees of freedom and thousands of design variables) practical and cost effective.

Harvey, Michael Stephen↗
DOE OSTI2022

Random field optimization

Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational design of metamaterials with self contact

Inverse homogenization in combination with contact modeling, topology optimization and shape optimization is used to design metamaterials with optimized macroscopic response. The homogenization assumes length scale separation which allows the non-linear macroscopic behavior to be obtained by analyzing a single unit cell in a lattice structure. Self contact in the unit cell, which is modeled using a third medium contact method, is leveraged to obtain a complex homogenized response. The inverse homogenization problem is initially formulated as a topology optimization problem, where the macroscopic stress–strain behavior is tuned to our liking. However, it is well known that boundary phenomena are difficult to model in topology optimization and that interface modeling is crucial to accurately analyze contact. For that reason, the boundary representation of the topology optimized design is extracted and used as initial design in a subsequent shape optimization. The behaviors of our designs are verified by performing rigorous post-processing analyzes using conforming meshes and conventional contact formulations.

42 ENGINEERING↗