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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗
DOE OSTI2025

REIMAGINING HEAT EXCHANGERS FOR NEXT GENERATION ENVIRONMENTAL SYSTEMS

Air-to-refrigerant heat exchangers (HXs) are essential components in space conditioning, refrigeration, and power systems, and recent efforts have focused on making these devices more compact, reducing refrigerant charge and lowering manufacturing costs. Historically, HX innovation has been limited by available computational resources, design tools, and manufacturing constraints. The best available technologies utilize tube-fin and micro- or macro-channel tubes with fins, which are not necessarily the optimal designs achievable with current technology. In this paper, we highlight the latest advancements in air-to-refrigerant HXs, specifically emphasizing innovations achieved through shape and topology optimization. A multi-scale design optimization approach is introduced, alongside similar methods in literature, which enable highly sophisticated shape-optimized tube designs with more than 50% reduction in size and 25% reduction in refrigerant charge, essential for A3 refrigerant charge limit compliance. The frameworks integrate traditional heat and mass transfer science with state-of-the-art machine learning, genetic algorithms, and adjoint algorithms to create novel designs. While many of these innovative designs may not be manufacturable using conventional methods, they allow us explore the boundaries of what is possible. These novel air-to-refrigerant HXs are key enablers for ultra-low-refrigerant charge heat pump and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optimization of a Secondary Air Injector for a Rich-Quench-Lean (RQL) Ammonia Combustor Using Computational Fluid Dynamics

Ammonia has emerged as a promising medium for moving hydrogen around the globe due to its energy density, existing infrastructure for production, transportation, and storage, and multiple applications from fertilizer to power generation. However, one of the most significant challenges with ammonia combustion for power generation is the formation of nitrogen oxides (NOx) during combustion. Several combustion strategies have been developed to minimize the formation of NOx. One such strategy, known as rich-quench-lean (RQL), is a method that combusts NH3 in a fuel-rich environment, followed by a quick mix section and a lean burnout section. Rapid mixing of secondary air before lean burnout is thought to be important to minimize the formation of NOx. A genetic algorithm (GA) is used to parametrically vary the secondary air injector design, including the diameter, count, and angles over a constrained design space. The designs are evaluated using a non-reacting OpenFOAM model, with the objective function being the uniformity index of the secondary air (modeled as a scalar). Optimization campaigns show that traditional correlation-based designs might not be adequate. After running 414 OpenFOAM models, the optimal design is 32, 1 mm diameter, angled air injectors, increasing the uniformity index at the outlet of the quick mix zone by 36.8%. The most promising designs will be manufactured and tested in a small RQL burner setup, which is expected to lead to validation and insight into the practical usage of NH3 combustion for power generation.

ammonia combustion↗
DOE OSTI2024

Blade and Rim Seal Design of a First Stage High Pressure Turbine for a 300 MWe Supercritical CO2 Power Cycle

A first stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design 3D with objectives of increasing efficiency and decreasing heat load to the blade. The National Institute of Standards and Technology Reference Fluid Thermodynamic and Transport Properties Database (NIST REFPROP) [1] data for supercritical CO2 is formatted into tables of bicubic polynomial coefficients for use in condensable gas simulations in FINE/Turbo. Nearly 3000 unique shapes are evaluated via three-dimensional Reynolds Averaged Navier Stokes simulations, yielding increases in efficiency of up to 0.85 percentage points and decreases in heat load of 14%. A final blade, deemed the advanced blade, is chosen for future experimental analysis. Following this, a squealer tip optimization is performed on both the baseline and advanced blade designs. This optimization resulted in a performance gain of 1.25 points in efficiency and 15% reduction in tip heat load compared to the baseline flat tip design at the same clearance. In tandem, an optimization of the rotor-stator platform rim seal is performed using a parametrized geometry allowing for straight, meandering, and knife seal cavities. This multi-objective optimization focuses on decreasing the cooling mass flow and increasing the heat flux from the rotor and stator disk. The optimization resulted in cooling mass flow decreases of up to 26% while maintaining the average heat flux on the rim seal.

Tuite, Logan↗
DOE OSTI2026

Optimized Gear Selection to Maximize Energy Savings in Electric Traction Drives for Medium and Heavy Duty Vehicles

Multi‑gear transmission systems are commonly used in electric traction drives for medium and heavy‑duty vehicles, while most passenger‑vehicle electric drivetrains rely on a single fixed ratio to reduce cost, weight, and complexity. Using multiple gear ratios can enable downsizing of the motor and inverter while still meeting performance requirements. Additionally, appropriately chosen ratios allow the motor to operate more frequently in high‑efficiency regions, improving overall energy usage and reducing operating costs over the drive cycle. This paper presents a systematic approach for selecting optimal gear ratios for electric drive systems. A neural‑network model is first developed to represent motor losses across the full torque–speed range using data generated from finite element analysis. This model enables fast, accurate evaluation of motor efficiency under varying operating conditions. A genetic‑algorithm‑based optimization framework is then applied to identify gear ratios that maximize energy cost savings over the drive cycle, with the resulting optimal ratios stored for real‑time implementation.

Gadiyar, Nishanth [ORNL] (ORCID:0000000348267524)↗
DOE OSTI2020

Autonomous Selective Parts-Based Tracking

Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The approach we propose was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.

42 ENGINEERING↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2024

Pressurized-Water Reactor Core Design using Multi-Objective Plant Fuel Reload Optimization Platform

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimization of core design. The optimization of the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible options of core configuration, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization (PRLO) platform which supports artificial-intelligence-based reactor core designing is now fully capable of handling realistic problems. The PRLO Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual Environment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multi-objective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

42 ENGINEERING↗
DOE OSTI2019

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Utilizing only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Dose–volume histogram evaluation scheme for beam orientations in intensity-modulated radiation therapy

Beam orientations in intensity-modulated radiation therapy (IMRT) is an important, but difficult NP-hard optimization problem. A considerable part of this difficulty is due to the essential prerequisite of defining, appropriately, counter-intuitive criteria for a mathematical plan evaluation that coincides with the clinical judgment of the considered plan. Moreover, the quality of beam directions depends heavily on their intensities. Usually a stochastic selector is utilized for optimizing beam orientations, and the intensities are thus calculated many thousands of times, each time for a different selection of beam directions, resulting in excessive time complexity. Therefore, selecting an appropriate set of beam directions in IMRT is still a time-consuming manual trial-and-error search procedure that depends on intuition and empirical knowledge. To overcome these difficulties, this work utilizes the concept of dose–volume histogram (DVH), which is one of the main recognized quantitative measurement tools used for plan judgment, and presents a DVH evaluation scheme that parallelizes plan evaluation with clinical plan judgment. This scheme is then utilized to optimize the intensities of each individual beam, enabling it to be represented by its DVHs, produced for the different irradiated volumes. Finally, an evolutionary algorithm is employed to search for the best combination of those DVHs reflecting the clinical optimal. The presented methods have been applied to three real clinical examples. In comparison to the standard equally spaced beam plans, improvements were reported in all three cases. In comparison with the greedy algorithm, improvements were reported in those examples requiring more than five beam directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Furthermore we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Neuroevolutionary Learning of Particles and Protocols for Self-Assembly

Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures. Furthermore, the learning algorithm is capable of both directed and exploratory design: it can assemble a material with a user-defined property, or search for novelty in the space of specified order parameters. In the latter mode it explores the space of what can be made, rather than the space of structures that are low in energy but not necessarily kinetically accessible.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

42 ENGINEERING↗
DOE OSTI2024

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. We show an example where, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO 2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.

58 GEOSCIENCES↗