Thermal stress analysis of epoxy encapsulants.
Thermal stress analysis of epoxy encapsulants using mathematical model for predicting performance in thermal shock
EPOXY RESIN↗
SEARCH · Engineering Papers
Results for “performance modeling and prediction”
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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Interpreting Write Performance of Supercomputer I/O Systems with Regression Models
This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.
Xie, Bing↗
Dynamic Modeling, Controls, and Testing for Electrified Aircraft
Electrified aircraft have the potential to provide significant benefits for efficiency and emissions reductions. To assess these potential benefits, modeling tools are needed to provide rapid evaluation of diverse concepts and to ensure safe operability and peak performance over the mission. The modeling challenge for these vehicles is the ability to show significant benefits over the current highly refined aircraft systems. The STARC-ABL (single-aisle turbo-electric aircraft with an aft boundary layer propulsor) is a new test proposal that builds upon previous N3-X team hybrid designs. This presentation describes the STARC-ABL concept, the NASA Electric Aircraft Testbed (NEAT) which will allow testing of the STARC-ABL powertrain, and the related modeling and simulation efforts to date. Modeling and simulation includes a turbofan simulation, Numeric Propulsion System Simulation (NPSS), which has been integrated with NEAT; and a power systems and control model for predicting testbed performance and evaluating control schemes. Model predictions provide good comparisons with testbed data for an NPSS-integrated test of the single-string configuration of NEAT.
Connolly, Joseph↗
EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models
In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.
60 APPLIED LIFE SCIENCES↗
Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks
Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Development and Integration of Predictive Models for Manufacturing and Structural Performance of Carbon Fiber Composites in Automotive Applications (Final Report)
The goal of this project is to develop integrated, state-of-the-art, computational modeling tools based on an integrated computational materials engineering (ICME) methodology that are critically needed to enable structural carbon fiber (CF) applications in automobiles. These tools are designed to predict the manufacturing and structural performance of CF composites, including stochastic effects. During the first phase of the project, the manufacturing and structural performance tools, including a stochastic driver, were developed, calibrated, and validated against coupon and component level test results. For this project, from the manufacturing side, the development efforts were focused on high-pressure resin transfer molding (HP-RTM), a potentially game-changing manufacturing technology that is capable of reaching the 3-5-minute cycle time that is necessary for volume manufacturing of composites. On the structural performance side, the crashworthiness of CF structures was studied. Accordingly, computational tools were developed and validated. The resulting differences between the numerical predictions and the experimental results in the first phase of the project were required to be less than 15%. During the second phase of the project, the manufacturing and performance tools were integrated by mapping the manufacturing outcomes (e.g., fiber angles, residual stresses, degree of cure, and defects) into the structural models. Also, using the tools developed in this project, an automotive assembly currently manufactured in steel (2016 GM Malibu) was redesigned to be manufactured using advanced CF composites with the objective of comparing the performance of the CF assembly with the developed ICME model predictions. The weight, performance and cost of the CF assembly were compared with the baseline steel assembly as well. The CF underbody design was shown to be 30% lighter than the corresponding steel design. The CF underbody assembly withstood the side pole impact with less than half the intrusion for the steel assembly. Excellent correlations within 10% for both peak impact load and intrusion were observed between the numerical predictions and experimental results. Based on the cost models developed in the project, the cost increase per Kg saved was determined to be $22. The project team has prepared a total of 28 publications in various journals and conferences and broadly communicated the project results for the benefit of the composite industry. The project team has also prepared a total of 10 patent applications that were submitted to the US Patent and Trademark office.
36 MATERIALS SCIENCE↗
Machine learning prediction of enzyme optimum pH
The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.
97 MATHEMATICS AND COMPUTING↗
GT2024-139262: Development of a Computational Fluid Dynamics Model for a High-Speed Centrifugal Compressor
Computational fluid dynamics (CFD) has become widely used in the design and analysis of turbomachinery components such as centrifugal compressors. However, CFD is only a limited representation of experimental cases and struggles to model complex flows or lack model details to increase computation speed. The High Efficiency Centrifugal Compressor (HECC) was designed by United Technologies Research Center for NASA to investigate the difficulties behind improving centrifugal compressor technology and to provide an open case to the turbomachinery research community. The CFD technology seen in previous numerical models of the compressor have improved since then. In this study, a high-fidelity computational model for the vaneless configuration of HECC operating at its design point was developed and compared to an equivalent experimental case. This model incorporated GPU versions of mesh generation code WAND and solver code LEO from AeroDynamic Solutions, Inc. Blade sections extracted from the solid model formed the basis of the impeller mesh, while the remainder of the stage was modeled as ducts. Standard day aerodynamic conditions, design rotational speed, design mass flow rate, and inlet flow angles served as additional inputs. A mesh sensitivity study was performed prior to using the model for analysis. The accuracy of the model’s predictions of impeller and stage performance were assessed by comparing the total pressure ratio, total temperature rise ratio, and adiabatic efficiency to the corresponding parameters observed in the experimental case. Results showed a general overprediction of compressor performance by the model. Predictions were more accurate for the impeller performance compared to those for the full compressor stage. Spanwise analyses showed that the model tended to follow flow behavior patterns exhibited in the experiment. Beyond this study, this model was used to examine HECC aerodynamics and performance in research following thereafter.
centrifugal compressor↗
Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study
This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50–900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.
60 APPLIED LIFE SCIENCES↗
Prediction modeling of physiological responses and human performance in the heat with application to space operations
This institute has developed a comprehensive USARIEM heat strain model for predicting physiological responses and soldier performance in the heat which has been programmed for use by hand-held calculators, personal computers, and incorporated into the development of a heat strain decision aid. This model deals directly with five major inputs: the clothing worn, the physical work intensity, the state of heat acclimation, the ambient environment (air temperature, relative humidity, wind speed, and solar load), and the accepted heat casualty level. In addition to predicting rectal temperature, heart rate, and sweat loss given the above inputs, our model predicts the expected physical work/rest cycle, the maximum safe physical work time, the estimated recovery time from maximal physical work, and the drinking water requirements associated with each of these situations. This model provides heat injury risk management guidance based on thermal strain predictions from the user specified environmental conditions, soldier characteristics, clothing worn, and the physical work intensity. If heat transfer values for space operations' clothing are known, NASA can use this prediction model to help avoid undue heat strain in astronauts during space flight.
Pandolf, Kent B.↗
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.
36 MATERIALS SCIENCE↗
Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models
In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.
Ahumada, A. J., Jr.↗
Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance
This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.
radioisotope power system↗
Parametric instabilities in computer system performance prediction
Results found by applying a predictive model to a particular system (University of Maryland Computer Center's Univak 1100/42) are presented. Given the correct model parameters, the system performance from the model closely matches the actual system observed performance. However, the major application of system modeling is the prediction of performance when the system configuration is altered. The prediction results presented are disappointing but useful with the major problem traced to parametric instabilities. The interactions between the user demands, overhead activities, and device characteristics must be considered in developing system models.
Dowdy, L. W.↗
Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program
Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.
Bolshinskiy, L. G.↗
Wind Tunnel and Hover Performance Test Results for Multicopter UAS Vehicles
There is currently a lack of published data for the performance of multicopter unmanned aircraft system (UAS) vehicles, such as quadcopters and octocopters, often referred to collectively as drones. With the rapidly increasing popularity of multicopter UAS, there is interest in better characterizing the performance of this type of aircraft. By studying the performance of currently available vehicles, it will be possible to develop models for vehicles at this scale that can accurately predict performance and model trajectories. This paper describes a wind tunnel test that was recently performed in the U.S. Army's 7- by 10-ft Wind Tunnel at NASA Ames Research Center. During this wind tunnel entry, five multicopter UAS vehicles were tested to determine forces and moments as well as electrical power as a function of wind speed, rotor speed, and vehicle attitude. The test is described here in detail, and a selection of the key results from the test is presented.
Multicopter UAS↗
Correlation of Amine Swingbed On-Orbit CO2 Performance with a Hardware Independent Predictive Model
The Amine Swingbed Payload is an experimental system deployed on the International Space Station (ISS) that includes a two-bed, vacuum regenerated, amine-based carbon dioxide (CO2) removal subsystem as the principal item under investigation. The aminebased subsystem, also described previously in various publications as CAMRAS 3, was originally designed, fabricated and tested by Hamilton Sundstrand Space Systems International, Inc. (HSSSI) and delivered to NASA in November 2008. The CAMRAS 3 unit was subsequently designed into a flight payload experiment in 2010 and 2011, with flight test integration activities accomplished on-orbit between January 2012 and March 2013. Payload activation was accomplished in May 2013 followed by a 1000 hour experimental period. The experimental nature of the Payload and the interaction with the dynamic ISS environment present unique scientific and engineering challenges, in particular to the verification and validation of the expected Payload CO2 removal performance. A modeling and simulation approach that incorporates principles of chemical reaction engineering has been developed for the amine-based system to predict the dynamic cabin CO2 partial pressure with given inputs of sorbent bed size, process air flow, operating temperature, half-cycle time, CO2 generation rate, cabin volume and the magnitude of vacuum available. Simulation runs using the model to predict ambient CO2 concentrations show good correlation to on-orbit performance measurements and ISS dynamic concentrations for the assumed operating conditions. The dynamic predictive modelling could benefit operational planning to help ensure ISS CO2 concentrations are maintained below prescribed limits and for the Orion vehicle to simulate various operating conditions, scenarios and transients.
Papale, William↗
Predicting Cost/Performance Trade-Offs for Whitney: A Commodity Computing Cluster
Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. We develop closed form expressions detailing the number and size of messages sent by each benchmark. Coupling these with measured single processor performance, network latency, and network bandwidth, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $1,000,000.
Becker, Jeffrey C.↗