Results for “process optimization” — Page 53

NASA NTRS2018

Comparison of Aircraft Conceptual Design Weight Estimation Methods to the Flight Optimization System

Weight estimation is critical in the aircraft conceptual design process. The Flight Optimization System (FLOPS) is an aircraft conceptual design tool that has been the primary aircraft synthesis software used by the Systems Analysis and Concepts Directorate at NASA Langley Research Center. FLOPS includes multiple modules that represent aircraft design disciplines. The FLOPS weight module includes estimation methods that are similar in nature to other regression based aircraft preliminary weight estimation methods, however the FLOPS methods were created to use a minimum number of input parameters to limit the effort required by the designer to apply it. As FLOPS has recently been made publically available, this work compares the FLOPS weight estimation methods with several similar methods with the goal of explaining the differences in FLOPS, providing conceptual designers with a brief introduction to the method before attempting to apply it, and providing a reference to inform the development of future weight estimating relationships. In this paper, the Boeing 737-200 is used as a test case to highlight to differences and similarities in the methods.

Horvath, Bryce L.↗

NASA NTRSDate not supplied

Application of a Rapid Design Tool to a 3D Woven Structural Joint

The optimization of composite structural joints is an iterative process and a multiscale problem. High fidelity finite element modeling of joints with 3D woven and laminated materials can become computationally expensive. The aim of this paper is to establish a reliable analysis process for the optimization of a composite Y-joint (curved Pi-joint), to be used in an aircraft fuselage, using commercial rapid joint design, analysis, and optimization software. The rapid joint design tool was investigated as a substitute and/or complement to a finite element analysis software. A composite Pi-joint and a composite Y-joint were evaluated using the rapid joint design tool to determine the applicability and limits of the software. Furthermore, three main preliminary parametric studies were performed to better understand the capability of the tool in predicting the stress distributions and trends in the Y-joint. The parameters investigated were the joint curvature, the laminated skin thickness, the adhesive systems, and the ply composition. Lastly, trends in predicted failure load were produced as a function of skin thickness (16ply, 24ply and 32ply). Failure loads were found with varying joint curvature and skin thickness using stress-based adherend failure criterion. The rapid joint design tool was also validated against existing experimental results.

Bonded Joints↗

DOE OSTI2024

Optimal Design Approaches for Cost-Effective Manufacturing & Deployment of Chemical Process Families with Economies of Numbers

This work builds on our optimization formulation for process family design and extends it to explicitly include the benefits of economies of numbers. Economies of numbers (sometimes referred to as economies of learning) is a well-documented cost saving phenomenon. It characterizes the manufacturing cost savings due to standardization; in particular, it is capturing the correlation between cost reduction and the number of times a particular product has been manufactured. Following an approach similar to that in Gazzaneo et al. (2022), we develop a costing expression that captures material costs and manufacturing costs as a function of the number of unit modules produced. If the platform has a small number of unit module designs, we will be manufacturing a large number of each of these designs and gaining increased benefits from economies of numbers. However, increasing the number of unit module designs in the platform gives each process variant more choices to consider (at the cost of reducing economies of numbers). The optimization formulation in Stinchfield et al. (2023) pre-specified the number of unit module designs to be included in the platform. Here, by including the economies of numbers explicitly, we allow the mathematical programming formulation to determine the optimal number of unit module designs to include in the platform. We demonstrate this approach on multiple case studies, including MEA-based carbon capture and water desalination.

Stinchfield, Georgia↗

DOE OSTI2025

Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing

This paper introduces a Physics-Informed Neural Network (PINN) technique that co-trains neural networks (NNs) that represent each function in a system of equations to simultaneously solve equations representing an out-of-autoclave (OOA) cure process while conducting optimization in adherence to process requirements. Specifically, this co-training approach benefits from using NNs to represent OOA inputs (air temperature profile) and outputs (part and tool temperature profiles and degree of cure). Production requirements can then be levied on the inputs, such as maximum air temperature and minimum cure cycle, and simultaneously on the outputs, such as degree of cure, maximum part temperature, and part temperature rate limits. The technique is validated with finite element (FE) simulations and physical experiments for curing a Toray T830H-6 K/3900-2D composite panel. Furthermore, this novel approach efficiently models and optimizes the OOA cure process.

Composite curing↗

NASA NTRS1989

Application of an optimal filter for inflatable sphere data processing

The improvement in the sphere data processing, concerning the signal to noise ratio, is discussed. Frequency analysis of the radar data is effectuated. It reveals a specific frequency component in the radar angle error, which may originate from the tracking radar mechanism itself. An optimal (Wiener) filter is applied to the radar data in order to suppress the systematic angular error components selectively. Using this technique, a significant improvement in the signal to noise ratio is achieved. The resolution of sphere measurements, previously limited by the length of the polynomial filter in the sphere data processing algorithm, is improved.

Lee, H. S.↗

NASA NTRS1994

Automation of reverse engineering process in aircraft modeling and related optimization problems

During the year of 1994, the engineering problems in aircraft modeling were studied. The initial concern was to obtain a surface model with desirable geometric characteristics. Much of the effort during the first half of the year was to find an efficient way of solving a computationally difficult optimization model. Since the smoothing technique in the proposal 'Surface Modeling and Optimization Studies of Aerodynamic Configurations' requires solutions of a sequence of large-scale quadratic programming problems, it is important to design algorithms that can solve each quadratic program in a few interactions. This research led to three papers by Dr. W. Li, which were submitted to SIAM Journal on Optimization and Mathematical Programming. Two of these papers have been accepted for publication. Even though significant progress has been made during this phase of research and computation times was reduced from 30 min. to 2 min. for a sample problem, it was not good enough for on-line processing of digitized data points. After discussion with Dr. Robert E. Smith Jr., it was decided not to enforce shape constraints in order in order to simplify the model. As a consequence, P. Dierckx's nonparametric spline fitting approach was adopted, where one has only one control parameter for the fitting process - the error tolerance. At the same time the surface modeling software developed by Imageware was tested. Research indicated a substantially improved fitting of digitalized data points can be achieved if a proper parameterization of the spline surface is chosen. A winning strategy is to incorporate Dierckx's surface fitting with a natural parameterization for aircraft parts. The report consists of 4 chapters. Chapter 1 provides an overview of reverse engineering related to aircraft modeling and some preliminary findings of the effort in the second half of the year. Chapters 2-4 are the research results by Dr. W. Li on penalty functions and conjugate gradient methods for quadratic programming problems.

Li, W.↗

NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗

DOE OSTI2024

Precision Polishing of Ablator Capsules via in situ Process Monitoring and Machine Learning–Based Optimization

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing the hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High-density carbon capsules made for ICF experiments at the National Ignition Facility are precision polished to achieve surface smoothness on the order of a few nanometers as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with the goal of elevating the efficiency of this process. Our approach is to use MEMS (micro-electromechanical systems)–based sensors to capture the fine vibration signals generated during the polishing process and combine them with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, in this work, we describe the multiple fronts we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used to ensure the progress of the run as well as a means for faster optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

Optimal cure cycle design for autoclave processing of thick composites laminates: A feasibility study

The thermal analysis and the calculation of thermal sensitivity of a cure cycle in autoclave processing of thick composite laminates were studied. A finite element program for the thermal analysis and design derivatives calculation for temperature distribution and the degree of cure was developed and verified. It was found that the direct differentiation was the best approach for the thermal design sensitivity analysis. In addition, the approach of the direct differentiation provided time histories of design derivatives which are of great value to the cure cycle designers. The approach of direct differentiation is to be used for further study, i.e., the optimal cycle design.

Hou, Jean W.↗

DOE OSTI2025

Atomic Layer Deposition (ALD) of Metal and Metal Oxide Films: A Surface Science Study (Final Report)

This is the final report for this project. The long-term objective of our project is to develop a general molecular-level picture of the surface chemistry associated with ALD processes. Our central hypothesis is that the chemistry of ALD precursors can differ significantly from that seen in solution. Awareness of such differences should provide general guidelines on what to watch for when synthesizing new ALD precursors and designing and optimizing new ALD processes, especially in terms of minimizing the incorporation of impurities in the growing films and of carrying out depositions under mild pressure and temperature conditions. The main objective of our research project has been to advance the fundamental knowledge of the surface chemistry of ALD precursors needed for the design and optimization of film deposition processes. A modern surface-science approach has been implemented to both elucidate the mechanism of the reactions of the precursors on the surface and characterize the composition and morphology of the growing films.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Atomic Layer Deposition (ALD) of Metal and Metal Oxide Films: A Surface Science Study (Final Report)

This is the final report for this project. The long-term objective of our project is to develop a general molecular-level picture of the surface chemistry associated with ALD processes. Our central hypothesis is that the chemistry of ALD precursors can differ significantly from that seen in solution. Awareness of such differences should provide general guidelines on what to watch for when synthesizing new ALD precursors and designing and optimizing new ALD processes, especially in terms of minimizing the incorporation of impurities in the growing films and of carrying out depositions under mild pressure and temperature conditions. The main objective of our research project has been to advance the fundamental knowledge of the surface chemistry of ALD precursors needed for the design and optimization of film deposition processes. A modern surface-science approach has been implemented to both elucidate the mechanism of the reactions of the precursors on the surface and characterize the composition and morphology of the growing films. In general, emphasis is being placed on: • Identifying the primary reactions that may lead to the deposition of clean films; •Identifying the secondary reactions that may help the ALD process, by, for instance, helping with the reduction (or oxidation) of the metal atom; • Identifying the secondary reactions that may lead to the deposition of undesirable impurities in the growing films; • Determining the kinetic parameters of the relevant surface reactions in order to define the optimum conditions for film deposition and to minimize impurity deposition. • Characterizing the nature of the resulting films, with focus on their stoichiometry and on the final oxidation states of the constituent elements; and • Using the information obtained to propose better precursors for given ALD processes.

36 MATERIALS SCIENCE↗

NASA NTRS1982

Combining analysis with optimization at Langley Research Center - An evolutionary process

Analytical and computational advances, at Langely Research Center (La RC), contributing to the evolution of computer programs combining analysis and optimization are presented, namely, strength sizing, concurrent strength and flutter sizing, and general optimization. Current work on a software system which executes the analysis and optimization in a sequential rather than concurrent mode is then described, as a step toward the long-term goal at La RC of developing the methodology for such systems. The software system is designated Enginering Analysis Language (EAL)/Programming Structural Synthesis System (PR)SSS), and work is being done on the incorporation of PROSSS into EAL. EAL language can perform most FORTRAN operations, including testing, branching, and looping, and its data base system can easily be accessed by any processor using FORTRAN callable utility subroutines. Some numerical results showing the accuracy of EAL/PROSSS are given.

Rogers, J. L., Jr.↗

DOE OSTI2024

Process–Property–Performance Mapping of Additively Manufactured 316H Stainless Steel Components

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development of advanced materials and components fabricated via additive manufacturing, and is using laser powder bed fusion (LPBF) of 316H stainless steel as an initial case study. In the previous fiscal year, miniature high-throughput specimens were printed on multiple LPBF systems to provide initial processing windows to minimize porosity and limit epitaxial grain growth during prints. This fiscal year, scaled builds were completed on three different LPBF systems at ORNL: a GE Concept Laser M2, a Renishaw AM400, and an EOS M290. Builds on the Concept Laser were conducted on multiple powder lots and processing parameter ranges to provide microstructure effects on time-independent and time-dependent mechanical properties. Builds on the Renishaw were produced using Oak Ridge National Laboratory (ORNL)-optimized printing parameters and Argonne National Laboratory (ANL)-optimized printing parameters to compare outcomes of parallel process optimization efforts at different national laboratories on the same LPBF system. Similarly, the build completed on the EOS M290 replicated the processing parameters of builds completed at Los Alamos National Laboratory (LANL). Optical microscopy and electron backscatter diffraction characterization was completed on all builds. In addition to the general round robin characterization, this work-package generated time-independent data, including tensile and fracture toughness test data on scaled Concept Laser builds as a function of processing parameters and post-build heat treatment. This analysis is complimentary to work in parallel work packages aiming to establish heat treatment and processing effects on time-dependent properties. It was found that although the stress-relief heat treatment provides the highest strength at lower-temperatures, tensile strength begins to converge at higher temperatures regardless of heat treatment condition. In addition, the more rigorous solution annealing and hot-isostatic pressing post-build heat treatments result in higher fracture toughness than the stress-relieved condition. The root-causes of the lower fracture toughness of the stress-relieved LPBF 316H material was informed via a stress-relief optimization study on a scaled concept laser print, where it was found that although dislocation recovery was largely complete after only a couple hours at 650°C, the extended hold of the current 24h heat treatment employed on scaled builds likely caused increased carbide volume fractions along the LPBF 316H grain boundaries, thereby deteriorating crack propagation resistance. This trend was seen to become more deleterious with additional increases of stress-relief temperature to 750°C or 850°C. These results have helped inform a new optimal stress-relief annealing condition for LPBF 316H for future campaign testing (650°C for 2h).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Process–Property–Performance Mapping of Additively Manufactured 316H Stainless Steel Components

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development of advanced materials and components fabricated via additive manufacturing, and is using laser powder bed fusion (LPBF) of 316H stainless steel as an initial case study. In the previous fiscal year, miniature high-throughput specimens were printed on multiple LPBF systems to provide initial processing windows to minimize porosity and limit epitaxial grain growth during prints. This fiscal year, scaled builds were completed on three different LPBF systems at ORNL: a GE Concept Laser M2, a Renishaw AM400, and an EOS M290. Builds on the Concept Laser were conducted on multiple powder lots and processing parameter ranges to provide microstructure effects on time-independent and time-dependent mechanical properties. Builds on the Renishaw were produced using Oak Ridge National Laboratory (ORNL)-optimized printing parameters and Argonne National Laboratory (ANL)-optimized printing parameters to compare outcomes of parallel process optimization efforts at different national laboratories on the same LPBF system. Similarly, the build completed on the EOS M290 replicated the processing parameters of builds completed at Los Alamos National Laboratory (LANL). Optical microscopy and electron backscatter diffraction characterization was completed on all builds. In addition to the general round robin characterization, this work-package generated time-independent data, including tensile and fracture toughness test data on scaled Concept Laser builds as a function of processing parameters and post-build heat treatment. This analysis is complimentary to work in parallel work packages aiming to establish heat treatment and processing effects on time-dependent properties. It was found that although the stress-relief heat treatment provides the highest strength at lower-temperatures, tensile strength begins to converge at higher temperatures regardless of heat treatment condition. In addition, the more rigorous solution annealing and hot-isostatic pressing post-build heat treatments result in higher fracture toughness than the stress-relieved condition. The root-causes of the lower fracture toughness of the stress-relieved LPBF 316H material was informed via a stress-relief optimization study on a scaled concept laser print, where it was found that although dislocation recovery was largely complete after only a couple hours at 650°C, the extended hold of the current 24h heat treatment employed on scaled builds likely caused increased carbide volume fractions along the LPBF 316H grain boundaries, thereby deteriorating crack propagation resistance. This trend was seen to become more deleterious with additional increases of stress-relief temperature to 750°C or 850°C. These results have helped inform a new optimal stress-relief annealing condition for LPBF 316H for future campaign testing (650°C for 2h).

36 MATERIALS SCIENCE↗

NASA NTRS2019

A Design Methodology for Optimizing and Integrating Composite Materials in Gear Structures

The application of composite materials to gear structures is complex because of the gear shape, the need for precise dimensional tolerances, and the complex dynamic load condition. Methods are presented in this work to design and optimize an integrated composite hub-web structure that can be used as part of a hybrid composite-steel gear. The composite hub-web structure is a planar structure with a large decrease in thickness from the hub to the rim. Methods for design and optimization of this variable-thickness structure are presented along with a method for forming braided prepreg material to conform to the shape of this structure. A layered approach is presented that integrates cut plies or filler materials for thickness buildup with continuous-fiber layers for the primary load path. An additional gear design concept is presented that has an axially extended continuous-fiber composite structure that can be combined with the planar structure. A proposed optimization methodology is introduced where an optimized design is output for each type of optimization simulation. Through this process, composite knowledge can be incorporated in the optimization, and more control is given over the design during the process. The methods in this study provide a tool for designing and optimizing composite structures for gears and other high-power-density applications.

optimization↗

DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Observable optimization for precision theory: machine learning energy correlators

The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In many cases, such as classification or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something computable systematically beyond the level of current simulation tools. In this work, we demonstrate that precision-theory-compatible observable space exploration can be systematized by using neural simulation-based inference techniques from machine learning. We illustrate this approach by exploring the space of marginalizations of the energy 3-point correlator to optimize sensitivity to the top quark mass. We first learn the energy-weighted probability density from simulation, then search in the space of marginalizations for an optimal triangle shape. Although simulations and machine learning are used in the process of observable optimization, the output is an observable definition which can be then computed to high precision and compared directly to data without any memory of the computations which produced it. We find that the optimal marginalization is isosceles triangles on the sphere with a side ratio approximately $1 : 1 : \sqrt{2}$ (i.e. right triangles) within the set of marginalizations we consider.

Jets and Jet Substructure↗