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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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946 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

National launch system core vehicle

A new launch system is being defined to provide significant improvements in the national launch capability. The most challenging requirements are to reduce operational cost and increase system reliability, dependability, responsiveness while maintaining acceptable mission performance. System architecture studies have established that a family of vehicles is required to launch the required range of payloads. The vehicle design approach implements a design process that identifies the system design criteria and design concepts that enable the vehicle to meet the objectives of cost, reliability, safety and performance. The vehicle design features for the 50 to 100 K payload capability will utilize cost effective elements derived from existing vehicles, emerging technology and lessons learned to enable the system to be developed at a reasonable investment and to deliver payloads in an operationally efficient manner with high reliability.

Worlund, Armis L.↗
NASA NTRS2008

Feasibility of Conducting J-2X Engine Testing at the Glenn Research Center Plum Brook Station B-2 Facility

A trade study of the feasibility of conducting J-2X testing in the Glenn Research Center (GRC) Plum Brook Station (PBS) B-2 facility was initiated in May 2006 with results available in October 2006. The Propulsion Test Integration Group (PTIG) led the study with support from Marshall Space Flight Center (MSFC) and Jacobs Sverdrup Engineering. The primary focus of the trade study was on facility design concepts and their capability to satisfy the J-2X altitude simulation test requirements. The propulsion systems tested in the B-2 facility were in the 30,000-pound (30K) thrust class. The J-2X thrust is approximately 10 times larger. Therefore, concepts significantly different from the current configuration are necessary for the diffuser, spray chamber subsystems, and cooling water. Steam exhaust condensation in the spray chamber is judged to be the key risk consideration relative to acceptable spray chamber pressure. Further assessment via computational fluid dynamics (CFD) and other simulation capabilities (e.g. methodology for anchoring predictions with actual test data and subscale testing to support investigation.

Schafer, Charles F.↗
NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS2023

In-Space Manufacturing and Reclamation

The goal of this interdisciplinary senior design project is to develop a self-contained additive manufacturing machine with an integrated part recycler. The system is designed to be a sustainable manufacturing system on long-duration space flights with minimal interaction from on-board astronauts. Following a systems engineering approach, a commercially available and opensource 3D printer and recycler will be modified, integrated, and automated to satisfy the project goal. System components and subcomponents will be verified to meet requirements and risks will be assessed and mitigated.

Stephen Hawes↗