SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The presentation reviews Agency process requirements and the purpose, benefits, and experiences or seven software engineering processes. The processes include: product integration, configuration management, verification, software assurance, measurement and analysis, requirements management, and planning and monitoring.
The Trash Compaction and Processing System (TCPS) aims to reduce volume, biologically safen, physically stabilize, manage effluents, and recover resources from astronaut trash in the International Space Station (ISS). The Heat Melt Compactor (HMC) at NASA Ames Research Center (ARC) serves as a test system that supports TCPS development by conducting risk reduction activities associated with an ISS flight demonstration. One of the risk reduction activities is to test processing for other types of trash models. A foam model was added to reduce the volume of foam that takes up space in the ISS. This paper will discuss Zotek® F30 foam processing in the HMC unit and testing verification in correlation to defined TCPS requirements.
The Trash Compaction and Processing System (TCPS) aims to reduce volume, biologically safen, physically stabilize, manage effluents, and recover resources from astronaut trash in the International Space Station (ISS). The Heat Melt Compactor (HMC) at NASA Ames Research Center (ARC) serves as a test system that supports TCPS development by conducting risk reduction activities associated with an ISS flight demonstration. One of the risk reduction activities is to test processing for other types of trash models. A foam model was added to reduce the volume of foam that takes up space in the ISS. This paper will discuss Zotek® F30 foam processing in the HMC unit and testing verification in correlation to defined TCPS requirements.
Laser-wire metal additive manufacturing (AM) is one of the ideal direct energy deposition (DED) processes for creating large-scale parts with a medium level of complexity. However, the DED process involves complex thermal signatures and wide length scales making the fabrication of realistic AM components and part qualification often reliant on experimental trial-and-error optimization. While experimental measurements over the full volume of a part are valuable and necessary, measuring the entire area of a part is significantly laborious and practically infeasible, particularly for large parts in terms of cost and rapid qualification. Therefore, in this work, we developed an effective thermal and microstructure modeling framework based on the Johnson–Mehl-Avrami-Kolmogorov (JMAK) and Koistinen & Marburger (KM) models through a top-down approach that considers plate distortion-affected thermal profiles. A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. The predicted results were validated through detailed metallurgical measurements. A combined voxel-by-voxel approach with a sparse data reconstruction technique produced a near-perfect reconstruction of the original data. This approach anticipates a significant reduction in data points and computation time and resources. Lastly, we conclude with potential extensions of this work to other modeling efforts.
This study uses friction stir processing (FSP) for thermomechanical processing of high-pressure die-casting (HPDC) to modify microstructure and improve mechanical properties. FSP is carried out on two different HPDC aluminum alloys: (a) general-purpose, high-iron, HPDC A380 alloy and (b) premium quality, low-iron HPDC Aural-5 alloy in thin wall, flat plate geometry. Subsequent mechanical testing shows ~30 % and ~65 % enhancement in yield strength and tensile ductility. In addition, FSP leads to ~10 times improvement in fatigue life for A380 alloy and ~70 % improvement in fracture toughness for Aural-5 alloy. These findings emphasize the capability of FSP to modify the microstructure of HPDC Al-alloys-based structural components so that they can demonstrate a good combination of strength, ductility, fracture toughness, and high fatigue properties for long-term durability and reliability.
As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.
Randomly sampled random processes, discussing sampling time statistics and stationary point processes
Improper random process where impulse occurrence times constitutes stationary point process, noting autocorrelation function and spectral density function
The Trash Compaction and Processing System (TCPS) aims to reduce volume, biologically safen, physically stabilize, manage effluents, and recover resources from astronaut trash in the International Space Station (ISS). At NASA Ames Research Center (ARC), there are two test systems: the Heat Melt Compactor (HMC), which compacts the trash into a tile with temperature and pressure, and the Source Contaminant Control System (SCCS), which is the gas management subsystem aimed to reduce contaminants by converting them into carbon dioxide (CO 2 ) and water (H 2 O) vapor. Both the HMC and SCCS serve as test systems that support TCPS development by conducting risk reduction activities associated with an ISS technology demonstration. In this paper, the risk reduction activities discussed are the testing of different trash models for operational scenarios in the HMC, investigating the water extracted from those trash models, and managing the gaseous effluents from the trash via contaminant conversion through the SCCS. The HMC has undergone several tests of different trash models and cases in which a containment bag is used or not. For the first topic, this paper will analyze the water collected and recovered from these tests and analyze the effect of specific trash containment bags. For the second topic, this paper will individually evaluate the carbon bed and catalytic oxidizer (CatOx) to understand what components the carbon bed removes and what contaminants are converted in the CatOx. For the third and final topic, this paper will discuss Zotek® F30 foam processing in the HMC unit and testing verification in correlation to defined TCPS requirements. A foam model was added to reduce the volume of foam that take up space in the ISS. Overall, this paper is a compilation and an updated analysis of tests conducted in the lab-scale TCPS at ARC within the past year.
Existing algorithms for predicting milling chatter have not been widely adopted in industry since they require specialized instruments to measure the stability inputs. This study describes how the machining process for a meter-scale aluminum aerostructure was optimized using a physics-guided Bayesian stability model. The study was performed in collaboration with an industrial partner on production machines to evaluate the practicality of the proposed method under real-world conditions. For each cutting tool, the Bayesian approach automatically selected a small number of cutting tests, which were monitored using a microphone to observe the chatter frequency. The algorithm learned the system dynamics, cutting forces, and stability map from these test results. A novel algorithm for predicting tool bending stress was incorporated into the test selection algorithm to avoid tool breakage. On average, each set of optimized cutting parameters required less than six tests to identify and were 97% more productive than baseline parameters from the cutting tool manufacturer. The machining program was then further optimized using commercial feedrate scheduling software to remove cutting force spikes and reduce air cutting time. Five components were machined using the optimized process. These results demonstrate the potential for physics-guided Bayesian models to improve productivity in industrial settings.
In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.
This process model is a conceptual process model that simulate the reactive captures of CO2 and the subsequent conversion of captured CO2 to methanol. The model was developed using Aspen Plus.
This dataset contains files used to perform the microstructure simulations in the manuscript "A two-phase cellular automata model of grain structure in laser powder bed fusion-processed stainless steel", data analysis scripts, and experimental data used for comparison purposes, in Acta Materialia
The solar imaging X-ray telescope experiment (designated the S-056 experiment) is described. It will photograph the sun in the far ultraviolet or soft X-ray region. Because of the imaging characteristics of this telescope and the necessity of using special techniques for capturing images on film at these wave lengths, methods were developed for computer processing of the photographs. The problems of image restoration were addressed to develop and test digital computer techniques for applying a deconvolution process to restore overall S-056 image quality. Additional techniques for reducing or eliminating the effects of noise and nonlinearity in S-056 photographs were developed.
The development of large scale data processing systems for remote sensing is studied by evaluating: (1) the suitability of several sensor types with regard to producing data required for multispectral machine analysis; (2) various types of data preprocessing necessary to prepare such data for analysis; and (3) transfer of machine processing techniques for earth resources data to user community.
Review of some of the computer-aided digital image processing techniques recently developed. Special attention is given to mapping and mosaicking techniques and to preliminary developments in range determination from stereo image pairs. The discussed image processing utilization areas include space, biomedical, and robotic applications.
Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.
The services provided by the Spacelab Information Management System are discussed. The majority of the services are provided by the common-support subsystems in the Support Module furnished by the Spacelab manufacturer. The information processing requirements for the space processing applications (SPA) are identified. The requirements and capabilities for electric power, display and control panels, recording and telemetry, intercom, and closed circuit television are analyzed.