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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations

A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.

Glasser, M. E.↗
NASA NTRS1988

Life prediction modeling based on cyclic damage accumulation

A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, where initiation was defined as a 0.030 inch surface length crack. A principal engineering feature of the CDA method is the minimum data base required for implementation. Model constants can be evaluated through a few simple specimen tests such as monotonic loading and rapic cycle fatigue. The method was expanded to account for the effects on creep-fatigue life of complex loadings such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. A significant data base was generated on the behavior of the cast nickel-base superalloy B1900+Hf, including hundreds of specimen tests under such loading conditions. This information is being used to refine and extend the CDA life prediction model, which is now nearing completion. The model is also being verified using additional specimen tests on wrought INCO 718, and the final version of the model is expected to be adaptable to most any high-temperature alloy. The model is currently available in the form of equations and related constants. A proposed contract addition will make the model available in the near future in the form of a computer code to potential users.

Nelson, Richard S.↗
NASA NTRSDate not supplied

SHIIVER Assemby Thermal Model Prediction for Transient Test Conditions

An integrated thermal model of the Structure Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) was built in Thermal Desktop/Sinda-Fluint and used to predict thermal performance of the system with the technologies being tested from the eCryo project. The technologies tested include the vapor cooling of the structure using the boil-off and multilayer insulation (MLI) on the hydrogen tank. The thermal model is used to 1) understand system transient behaviors; 2) perform trade studies of different vapor-cooling designs; 3) define the test operations matrix, test procedures, and conditions; and 4) address thermal-related questions at different phases of SHIIVER project. Details of the thermal model are herein described, and results corresponding to the test operations are presented for four different configurations at different fill levels. The thermal performance of each configuration is predicted with the thermal model regarding the heat reduction and boil-off reduction by using vapor cooling and/or MLI on the structures attached to the hydrogen tank.

thermal modeling↗
DOE OSTI2018

Models Predicting Void Swelling Incubation Dose as a function of Irradiation Conditions

This report documents the development status of a cluster dynamics model to predict microstructural evolution in high-energy particle irradiated ferritic martensitic steels, in which we have recently implemented a model for the biased absorption of self-interstitial defects at small, nanometer-sized cavities that can provide an intrinsic nucleation barrier for the onset of void swelling. Following a brief review of the cluster dynamics modeling approach, this report describes the atomistic modeling results that demonstrate this phenomenon, along with an investigation on the influence of helium content on modifying the intrinsic cavity bias. Subsequently, the report describes the initial results obtained from our cluster dynamics modeling of the incorporation of this cavity bias on the predicted swelling incubation dose as a function of irradiation temperature. Finally, the report concludes with a discussion of the remaining modeling research required to fully validate the model for application to high dose neutron or ion beam irradiation exposure conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria

Abstract Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ 70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ 70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.

42 ENGINEERING↗
DOE OSTI2022

Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade

Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.

Engineering↗
DOE OSTI2020

Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)

This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

A prediction model for bipolar RAMs in a high energy ion/proton environment

A model has been developed which predicts the relative susceptibility of bipolar RAMs to heavy ion and proton upset. During the course of evaluating this model, physical and electrical variations were also evaluated indicating that the minimum internal signal level is the primary upset susceptibility indicator. Unfortunately, all of the physical and electrical variations expected during a normal product development cycle are in direct opposition to improved high-energy particle upset tolerance. Hence, a trade-off between highly susceptible, low power (medium speed) devices must be made against the less susceptible, higher power (high speed) equivalent device, taking into account the systems trade-off with respect to system power, software, error correction procedures and/or circuit redundancy.

Myers, D. K.↗
DOE OSTI2024

Multiobjective Constrained Symbolic Regression for Predictive Modeling of Material Creep Behavior

When creep testing is repeated on samples of the same alloy under the same parametric conditions (i.e., stress and temperature), the resulting strain/time curves can vary from each other considerably as shown in Figure 1 [1]. The time required to creep test a material to rupture can extend to the order of years. Because of this, a numerical model that can quickly analyze the incomplete results of an ongoing experiment to predict 1) the incomplete portion of the strain/time curve leading up to the rupture point and 2) the rupture point itself would be of great utility to the materials community. Such a model has the potential to save 1) the time required to finish running the experiment to rupture 2) the associated monetary cost of finishing said experiment. Furthermore, it would be advantageous if the predictive model could give a parametric function modeling strain/time curves for material scientists to investigate the impact of the temperature and stress parameters on the resulting creep behavior. This work introduces a piecewise symbolic regression algorithm to predict the remainder of the strain/time curve. Preliminary results show good model performance.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Observation simulation experiments with regional prediction models

Research efforts in FY 1990 included studies employing regional scale numerical models as aids in evaluating potential contributions of specific satellite observing systems (current and future) to numerical prediction. One study involves Observing System Simulation Experiments (OSSEs) which mimic operational initialization/forecast cycles but incorporate simulated Advanced Microwave Sounding Unit (AMSU) radiances as input data. The objective of this and related studies is to anticipate the potential value of data from these satellite systems, and develop applications of remotely sensed data for the benefit of short range forecasts. Techniques are also being used that rely on numerical model-based synthetic satellite radiances to interpret the information content of various types of remotely sensed image and sounding products. With this approach, evolution of simulated channel radiance image features can be directly interpreted in terms of the atmospheric dynamical processes depicted by a model. Progress is being made in a study using the internal consistency of a regional prediction model to simplify the assessment of forced diabatic heating and moisture initialization in reducing model spinup times. Techniques for model initialization are being examined, with focus on implications for potential applications of remote microwave observations, including AMSU and Special Sensor Microwave Imager (SSM/I), in shortening model spinup time for regional prediction.

Diak, George↗
NASA NTRS2015

The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints.

Risk Analysis↗
NASA NTRS1994

A test of source-surface model predictions of heliospheric current sheet inclination

The orientation of the heliospheric current sheet predicted from a source surface model is compared with the orientation determined from minimum-variance analysis of International Sun-Earth Explorer (ISEE) 3 magnetic field data at 1 AU near solar maximum. Of the 37 cases analyzed, 28 have minimum variance normals that lie orthogonal to the predicted Parker spiral direction. For these cases, the correlation coefficient between the predicted and measured inclinations is 0.6. However, for the subset of 14 cases for which transient signatures (either interplanetary shocks or bidirectional electrons) are absent, the agreement in inclinations improves dramatically, with a correlation coefficient of 0.96. These results validate not only the use of the source surface model as a predictor but also the previously questioned usefulness of minimum variance analysis across complex sector boundaries. In addition, the results imply that interplanetary dynamics have little effect on current sheet inclination at 1 AU. The dependence of the correlation on transient occurrence suggests that the leading edge of a coronal mass ejection (CME), where transient signatures are detected, disrupts the heliospheric current sheet but that the sheet re-forms between the trailing legs of the CME. In this way the global structure of the heliosphere, reflected both in the source surface maps and in the interplanetary sector structure, can be maintained even when the CME occurrence rate is high.

Burton, M. E.↗
NASA NTRS1985

Model predictions of ozone changes

The predictions of several one-dimensional models were examined for a number of prescribed scenarios in steady-state or time-dependent approximations. A small number of two-dimensional steady-state calculations for a limited number of scenarios were compared with the results of one-dimensional models. The sensitivity of the calculated predictions to the values of input parameters were examined in order to assess recognized uncertainties in these predictions.

Johnston, H. S.↗
DOE OSTI2026

Assessing the Accuracy of Property Model Predictions for Cost Optimization of Desalination Technologies

Accurate modeling of seawater thermophysical and thermodynamic properties is critical for optimizing desalination processes. This study compares three seawater property models, a Reaktoro multicomponent model, the thermophysical seawater properties library from the Massachusetts Institute of Technology, and a simplified sodium chloride model, in the context of levelized cost of water (LCOW) minimization for reverse osmosis (RO) and mechanical vapor compression systems. Process simulations and cost optimizations reveal that although all three models yield comparable LCOW and specific energy consumption (SEC) estimates under baseline conditions, deviations among their predictions increase with salinity. Relative differences in LCOW and SEC reach up to 6% and 8%, respectively. RO results show greater variability due to differences in osmotic pressure predictions, which affect pressure constraints at high recoveries. Computational performance varies substantially; specifically, Reaktoro simulations are up to 28 times slower than empirical models due to their detailed equilibrium calculations. These results suggest that empirical models offer acceptable accuracy for routine desalination process design, while Reaktoro provides advantages in scenarios requiring detailed speciation, such as scaling or pH adjustment studies. These findings underscore the importance of selecting appropriate property models based on the modeling objective of desalination applications and motivate future work integrating thermodynamic rigor with empirical efficiency.

Physical and chemical properties↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗