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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 19 records

Pulsed Electric Field Processing of Apples

Pulsed electric field (PEF) processing is a nonthermal technology that has the potential to improve the efficiency of apple processing, particularly for firm cultivars that are growing in popularity. This study evaluated the effects of PEF pretreatment on the processability and product quality of Cosmic Crisp apples, a modern cultivar known for its firmness. Apples were treated with PEF and assessed for tissue softening, mechanical energy requirements, particle size reduction, browning tendencies, and juice yield. PEF treatment significantly softened the apple tissue, as demonstrated by reduced compression forces in Instron testing, and this softening translated to decreased grinding energy during applesauce production. There were no changes to the particle size distribution of the resulting applesauce. PEF treatment also altered browning behavior and produced higher juice yields following mechanical disruption, suggesting enhanced membrane permeability and mass transfer. Together, these results show that PEF pretreatment can reduce mechanical energy demands and increase extractability without reducing product quality in firm apple varieties. These outcomes highlight the potential of PEF to increase the value recovered from processing-grade apples and to support more efficient and sustainable apple processing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

Apple Image Processing Educator

A software system design is proposed and demonstrated with pilot-project software. The system permits the Apple II microcomputer to be used for personalized computer-assisted instruction in the digital image processing of LANDSAT images. The programs provide data input, menu selection, graphic and hard-copy displays, and both general and detailed instructions. The pilot-project results are considered to be successful indicators of the capabilities and limits of microcomputers for digital image processing education.

Gunther, F. J.↗

Image-Processing Educator

Apple Image-Processing Educator (AIPE) explores ability of microcomputers to provide personalized computer-assisted instruction (CAI) in digital image processing of remotely sensed images. AIPE is "proof-of-concept" system, not polished production system. User-friendly prompts provide access to explanations of common features of digital image processing and of sample programs that implement these features.

Gunther, F. J.↗

APPLEPIPS /Apple Personal Image Processing System/ - An interactive digital image processing system for the Apple II microcomputer

Recent developments related to microprocessor-based personal computers have made low-cost digital image processing systems a reality. Image analysis systems built around these microcomputers provide color image displays for images as large as 256 by 240 pixels in sixteen colors. Descriptive statistics can be computed for portions of an image, and supervised image classification can be obtained. The systems support Basic, Fortran, Pascal, and assembler language. A description is provided of a system which is representative of the new microprocessor-based image processing systems currently on the market. While small systems may never be truly independent of larger mainframes, because they lack 9-track tape drives, the independent processing power of the microcomputers will help alleviate some of the turn-around time problems associated with image analysis and display on the larger multiuser systems.

Masuoka, E.↗

Comparative Penicillium spp. Transcriptomics: Conserved Pathways and Processes Revealed in Ungerminated Conidia and during Postharvest Apple Fruit Decay

Blue mold, caused by Penicillium spp., is an impactful postharvest disease resulting in significant economic losses due to reduced pome fruit quality and mycotoxin contamination. Using two Penicillium species with different levels of aggressiveness, transcriptomics were implemented in order to identify genes expressed during apple fruit decay and loci expressed in ungerminated conidia. Total RNA was isolated from ungerminated conidia and decayed apple fruit infected with P. expansum R19 or P. polonicum RS1. There were 2442 differentially expressed genes (DEGs) between the R19 and RS1 in apple. Comparisons within species between apple and conidia revealed 4404 DEGs for R19 and 2935 for RS1, respectively. Gene ontology (GO) analysis revealed differential regulation in fungal transport and metabolism genes during decay, suggesting a flux in nutrient acquisition and detoxification strategies. In R19, the oxidoreductase GO category comprised 20% of all DEG groups in apple verses conidia. Ungerminated conidia from both species showed DEGs encoding the glyoxylate shunt and beta-oxidation, specifying the earliest metabolic requirements for germination. This is the first study to identify pre-loaded transcripts in conidia from blue mold fungi, reveal unique genes between species expressed during apple decay, and show the expression dynamics of known fungal virulence factors. These findings will enable development of targeted approaches for blue mold abatement strategies.

59 BASIC BIOLOGICAL SCIENCES↗

Low-cost digital image processing at the University of Oklahoma

Computer assisted instruction in remote sensing at the University of Oklahoma involves two separate approaches and is dependent upon initial preprocessing of a LANDSAT computer compatible tape using software developed for an IBM 370/158 computer. In-house generated preprocessing algorithms permits students or researchers to select a subset of a LANDSAT scene for subsequent analysis using either general purpose statistical packages or color graphic image processing software developed for Apple II microcomputers. Procedures for preprocessing the data and image analysis using either of the two approaches for low-cost LANDSAT data processing are described.

Harrington, J. A., Jr.↗

The PMDP Roadmap

NASA's complex and highly technical missions rely on effective project teams and managers. Since 1993, through its Project Management Development Process (PMDP), the Academy of Program and Project Leadership (APPL) has offered direction to the Agency's project practitioners as they advance in their careers. PMDP helps identify and sequence professional experiences, courses, and other project-based learning experiences that support individual career goals and center activities by outlining competencies at four levels of development. The result is that PMDP provides NASA project practitioners with a road map to the knowledge and competencies appropriate for their job and the jobs to which they aspire. Plus, new this year, APPL has rolled out its electronic Project Management Development Process (ePMDP) tool, a learning management system that includes a dynamic presentation of the PMDP levels, competency areas, competency organizational structures, Individual Development Plans (IDP), and online PMDP enrollment. APPL's website, www.appl.nasa.gov, provides access to ePMDP as well as other online resources for NASA practitioners enrolled in the Project Management Development Process.

Source record↗

Digital correlator with fewer IC's

Digital correlator requires only few integrated circuits to determine synchronization of two 24-bit digital words. Circuit is easily reduced or expanded to accommodate shorter or longer words and can be utilized in industrial and commercial data processing and telecommunications.

Apple, G. G.↗

Microcomputer-based digital image processing - A tutorial package for exploration geologists

An Apple II microcomputer-based software package for analysis of digital data developed at the University of Oklahoma, the Digital Image Analysis System (DIAS), provides a relatively low-cost, portable alternative to large, dedicated minicomputers for digital image processing education. Digital processing techniques for analysis of Landsat MSS data and a series of tutorial exercises for exploration geologists are described and evaluated. DIAS allows in-house training that does not interfere with computer-based prospect analysis objectives.

Harrington, J. A., Jr.↗

Broadening JPL’s Mission Formulation Paradigm with Human Centered Design

Within NASA’s highly competitive environment for funding, Human Centered Design (HCD) and cybernetics could provide advantages to proposers during the mission formulation phase. Opportunities are limited when it comes to funding new science missions. Proposers are challenged to make a compelling case about the scientific desirability, technical feasibility, and resource viability of their concepts. Organizations follow established processes for proposal development using teams that typically include scientists, engineers, and managers. These team members are highly experienced subject matter experts (SME) in their own disciplines, and can respond to requirements from the solicitation. However, they are typically not trained as designers and communicators. Their approach is rooted within NASA’s science and technology paradigm. How can we improve the proposal development process, refine workflow between team members, and deliver clear and appealing offerings to the stakeholders and evaluators? These questions have been addressed by today’s most innovative companies (e.g., Apple, Google, 3M, Dyson), where the design process is not limited simply to engineering and management, but involves an all-encompassing approach drawing from fields such as social sciences, design, and the arts. Like these commercial enterprises, NASA currently employs systems thinking and integrated design, but can benefit further by moving beyond its current practices, which are mostly driven by rigid engineering, technology, science, and project management considerations. At JPL’s Innovation Foundry and through the Solar System Mission Formulation Office, we broadened this paradigm by including HCD in the mission formulation workflow. Our goal was to create a proposal with improved clarity and appeal, thus helping our team to communicate its message and aid evaluators with their work. In this paper we provide examples and lessons learned from our recent proposal development effort using HCD. We discuss touch points where we infused non-linear designerly approaches and cybernetic circularity into the workflow. Implemented design topics include operational design for team building; process design throughout distinct phases of the proposal development and writing process; communication design for streamlined exchange of information within the team and to stakeholders; interaction design; graphic design; and creating boundary objects. While these approaches may feel new or foreign to SMEs and managers in the aerospace community, they produced significant benefits in this mission formulation effort. We will describe how such approaches can be used to broaden NASA’s technology-driven paradigm through design, thus creating an environment which fosters innovation, improved communication, and strategic advantage for proposers and their organizations.

Turner, Neal↗

Trajectory Optimization: OTIS 4

The latest release of the Optimal Trajectories by Implicit Simulation (OTIS4) allows users to simulate and optimize aerospace vehicle trajectories. With OTIS4, one can seamlessly generate optimal trajectories and parametric vehicle designs simultaneously. New features also allow OTIS4 to solve non-aerospace continuous time optimal control problems. The inputs and outputs of OTIS4 have been updated extensively from previous versions. Inputs now make use of objectoriented constructs, including one called a metastring. Metastrings use a greatly improved calculator and common nomenclature to reduce the user s workload. They allow for more flexibility in specifying vehicle physical models, boundary conditions, and path constraints. The OTIS4 calculator supports common mathematical functions, Boolean operations, and conditional statements. This allows users to define their own variables for use as outputs, constraints, or objective functions. The user-defined outputs can directly interface with other programs, such as spreadsheets, plotting packages, and visualization programs. Internally, OTIS4 has more explicit and implicit integration procedures, including high-order collocation methods, the pseudo-spectral method, and several variations of multiple shooting. Users may switch easily between the various methods. Several unique numerical techniques such as automated variable scaling and implicit integration grid refinement, support the integration methods. OTIS4 is also significantly more user friendly than previous versions. The installation process is nearly identical on various platforms, including Microsoft Windows, Apple OS X, and Linux operating systems. Cross-platform scripts also help make the execution of OTIS and post-processing of data easier. OTIS4 is supplied free by NASA and is subject to ITAR (International Traffic in Arms Regulations) restrictions. Users must have a Fortran compiler, and a Python interpreter is highly recommended.

Riehl, John P.↗

Chasing ghosts: characterization of artifact generation in coded aperture decoding due to experimental implementation

Coded aperture imaging is a form of lensless aperture imaging that projects multiple overlapping images of the source onto the detector, enhancing signal strength, which is advantageous for low-flux sources or high-resolution imaging. This technique requires decoding of the detector signal to reconstruct the original source, which involves convolving the detector data with the aperture pattern. When the signal is from a centered point source, the reconstructed source image is known as the point spread function (PSF). A clean PSF without artifacts is a Dirac delta function [Appl. Opt. 20, 1858 (1981)]. This paper examines the robustness of the decoding process against variations in experimental tolerances by analyzing artifact growth in the reconstructed PSF. We illustrate the effects of incorrect magnification, rotation, and detector size and find that aperture–detector rotational misalignment about the imaging axis is the most sensitive parameter, with significant artifact generation occurring with angular offsets of less than one degree. We discuss compensation methods for imperfect aperture placement, finding that small detector sizes produce uncompensatable artifact generation, and compare theoretical predictions with experimental PSF measurements of a rank , 6.8 mm thick (less than one mean free path) coded aperture with a 3.5 mm cell size, conducted at the MegaJOuLe Neutron Imaging Radiography dense plasma focus [IEEE Trans. Plasma Sci. 49, 3299 (2021)] using a 2.45 MeV neutron source. Based on our findings, we recommend using magnified coded apertures in the under-sampled regime, which allows for the inclusion of fiducial markers to characterize aperture–detector rotational offsets and the addition of mechanical coupling, where possible, to constrain rotational and magnification offsets.

Selwood, M. P. [Lawrence Livermore National Labora↗

Energy efficiency in industrial drying: A hybrid ultrasonic system with a novel dynamic optimization framework

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agrifood products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multiobjective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Dynamic optimization↗

Exploiting parallel computing with limited program changes using a network of microcomputers

Network computing and multiprocessor computers are two discernible trends in parallel processing. The computational behavior of an iterative distributed process in which some subtasks are completed later than others because of an imbalance in computational requirements is of significant interest. The effects of asynchronus processing was studied. A small existing program was converted to perform finite element analysis by distributing substructure analysis over a network of four Apple IIe microcomputers connected to a shared disk, simulating a parallel computer. The substructure analysis uses an iterative, fully stressed, structural resizing procedure. A framework of beams divided into three substructures is used as the finite element model. The effects of asynchronous processing on the convergence of the design variables are determined by not resizing particular substructures on various iterations.

Rogers, J. L., Jr.↗

Initial experience with distributing structural calculations among computers operating in parallel

An existing program is currently being adapted to perform finite element analysis by distributing substructures over a network of four Apple IIe microcomputers connected to a shared disk. In this network, one microcomputer controls the entire process while the others perform the analysis on each substructure in parallel. This substructure analysis is used in an iterative, fully stressed, structural resizing procedure. This procedure allows experimentatation with resizing in which all analyses are not completed during a single iteration. This research gives some insight on how to configure multidiscriplinary analysis and optimization procedures for decomposable engineering systems using either high performance engineering workstations or a parallel processor supercomputer. In addition, the operational experience gained facilitates the implementation of analysis programs on these new computers when they become available in an engineering environment.

Rogers, J. L., Jr.↗

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

Implementation of context independent code on a new array processor: The Super-65

The feasibility of rewriting standard uniprocessor programs into code which contains no context-dependent branches is explored. Context independent code (CIC) would contain no branches that might require different processing elements to branch different ways. In order to investigate the possibilities and restrictions of CIC, several programs were recoded into CIC and a four-element array processor was built. This processor (the Super-65) consisted of three 6502 microprocessors and the Apple II microcomputer. The results obtained were somewhat dependent upon the specific architecture of the Super-65 but within bounds, the throughput of the array processor was found to increase linearly with the number of processing elements (PEs). The slope of throughput versus PEs is highly dependent on the program and varied from 0.33 to 1.00 for the sample programs.

Colbert, R. O.↗