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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Designing an Exploration Atmosphere Prebreathe Protocol

Extravehicular activities (EVAs) at remote locations must maximize limited resources such as oxygen (O2) and also minimize the risk of decompression sickness (DCS). A proposed remote denitrogenation (prebreathe) protocol requires astronauts to live in a mildly hypoxic atmosphere at 8.2 psia while periodically performing EVAs at 4.3 psia. Empirical data are required to confirm that the protocol meets the current accept requirements: less than or equal to 15% incidence of Type I DCS, less than or equal to 20% incidence of Grade IV venous gas emboli (VGE), both at 95% statistical confidence, with no Type II DCS symptom during the validation trial. METHODS: A repeated measures statistical design is proposed in which groups of 6 subjects with physical characteristics similar to active-duty astronauts would first become equilibrated to an 8.2 psia atmosphere in a hypobaric chamber containing 34% O2 and 66% N2, over 48 h, and then perform 4 simulated EVAs at 4.3 psia over the next 9 days. In the equilibration phase, subjects undergo a 3-h 100% O2 mask prebreathe prior to and during a 5-min ascent to 8.2 psia to prevent significant tissue N2 supersaturation on reaching 8.2 psia. Masks would be removed once 34% O2 is established at 8.2 psia, and subjects would then equilibrate to this atmosphere for 48 h. The hypoxia is equivalent to breathing air at 1,220 meters (4,000 ft) altitude, just as was experienced in the shuttle 10.2 psia - 26.5% O2 staged denitrogenation protocol and the current ISS campout denitrogenation protocol. For simulated EVAs, each subject dons a mask and breathes 85% O2 and 15% N2 during a 3-min depressurization to 6.0 psia, holds for 15 min, and then completes a 3-min depressurization to 4.3 psia. The simulated EVA period starts when 6.0 psia is reached and continues for a total of 240 min (222 min at 4.3 psia). During this time, subjects will follow a prescribed repetitive activity against loads in the upper and lower body with mean metabolic rate approaching 1500 BTU/hr [378 kcal/hr (O2 consumption about 1.3 l(sub STPD)/min)] in ambulatory subjects. Noninvasive Doppler ultrasound bubble monitoring for VGE in the pulmonary artery will be performed on subjects by 2 Doppler Technicians at about 15 min intervals while at 4.3 psia. At the end of this period, a 15-min repressurization returns all subjects back to 8.2 psia and the cycle is repeated 3 additional times with a day of rest between simulated EVAs. RESULTS: With an assumed 1.5% probability of DCS [P(DCS)] and accounting for within-subject correlation, running the proposed study with 20 subjects has a 95% probability of meeting the accept criterion for DCS. But if the true probability of DCS is 3.0%, then 30 subjects would be needed to achieve about the same probability to meet our accept criterion. These results assume a standard deviation of 1.4 for the between-subjects random component of P(DCS) on a logit scale, which was estimated from a previous study.

Conkin, Johnny↗
NASA NTRS2002

Using Temperature Sensitive Paint Technology

New facilities and test techniques afford research aerodynamicists many opportunities to investigate complex aerodynamic phenomena. For example, NASA Langley Research Center's National Transonic Facility (NTF) can hold Mach number, Reynolds number, dynamic pressure, stagnation temperature and stagnation pressure constant during testing. This is important because the wing twist associated with model construction may mask important Reynolds number effects associated with the flight vehicle. Beyond this, the NTF's ability to vary Reynolds number allows for important research into the study of boundary layer transition. The capabilities of facilities such as the NTF coupled with test techniques such as temperature sensitive paint yield data that can be applied not only to vehicle design but also to validation of computational methods. Development of Luminescent Paint Technology for acquiring pressure and temperature measurements began in the mid-1980s. While pressure sensitive luminescent paints (PSP) were being developed to acquire data for aerodynamic performance and loads, temperature sensitive luminescent paints (TSP) have been used for a much broader range of applications. For example, TSP has been used to acquire surface temperature data to determine the heating due to rotating parts in various types of mechanical systems. It has been used to determine the heating pattern(s) on circuit boards. And, it has been used in boundary layer analysis and applied to the validation of full-scale flight performance predictions. That is, data acquired on the same model can be used to develop trends from off design to full scale flight Reynolds number, e.g. to show the progression of boundary layer transition. A discussion of issues related to successfully setting-up TSP tests and using TSP systems for boundary layer studies is included in this paper, as well as results from a variety of TSP tests. TSP images included in this paper are all grey-scale so that similar to pictures from sublimating chemical tests areas of laminar flow appear "lighter," or white, and areas of turbulent flow appear "darker."

Hamner, M. P.↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗
NASA NTRS2000

Comprehensive Design Reliability Activities for Aerospace Propulsion Systems

This technical publication describes the methodology, model, software tool, input data, and analysis result that support aerospace design reliability studies. The focus of these activities is on propulsion systems mechanical design reliability. The goal of these activities is to support design from a reliability perspective. Paralleling performance analyses in schedule and method, this requires the proper use of metrics in a validated reliability model useful for design, sensitivity, and trade studies. Design reliability analysis in this view is one of several critical design functions. A design reliability method is detailed and two example analyses are provided-one qualitative and the other quantitative. The use of aerospace and commercial data sources for quantification is discussed and sources listed. A tool that was developed to support both types of analyses is presented. Finally, special topics discussed include the development of design criteria, issues of reliability quantification, quality control, and reliability verification.

Christenson, R. L.↗
NASA NTRS2014

James Webb Space Telescope Initial Mid-Course Correction Monte Carlo Implementation using Task Parallelism

The James Webb Space Telescope will be launched into a highly elliptical orbit that does not possess sufficient energy to achieve a proper Sun-Earth/Moon L2 libration point orbit. Three mid-course correction (MCC) maneuvers are planned to rectify the energy deficit: MCC-1a, MCC-1b, and MCC-2. To validate the propellant budget and trajectory design methods, a set of Monte Carlo analyses that incorporate MCC maneuver modeling and execution are employed. The first analysis focuses on the effects of launch vehicle injection errors on the magnitude of MCC-1a. The second on the spread of potential V based on the performance of the propulsion system as applied to all three MCC maneuvers. The final highlights the slight, but notable, contribution of the attitude thrusters during each MCC maneuver. Given the possible variations in these three scenarios, the trajectory design methods are determined to be robust to errors in the modeling of the flight system.

James Webb Space Telescope↗
NASA NTRS2014

James Webb Space Telescope Initial Mid-Course Correction Monte Carlo Implementation using Task Parallelism

The James Webb Space Telescope will be launched into a highly elliptical orbit that does not possess sufficient energy to achieve a proper Sun-Earth L2 libration point orbit. Three mid-course correction (MCC) maneuvers are planned to rectify the energy deficit: MCC-1a, MCC-1b, and MCC-2. To validate the propellant budget and trajectory design methods, a set of Monte Carlo analyses that incorporate MCC maneuver modeling and execution are employed. The first analysis focuses on the effects of launch vehicle injection errors on the magnitude of MCC-1a. The second on the spread of potential V based on the performance of the propulsion system as applied to all three MCC maneuvers. The final highlights the slight, but notable, contribution of the attitude thrusters during each MCC maneuver. Given the possible variations in these three scenarios, the trajectory design methods are determined to be robust to errors in the modeling of the flight system.

Libration Point Orbit↗
NASA NTRS2017

Verification and Validation Process for Progressive Damage and Failure Analysis Methods in the NASA Advanced Composites Consortium

The Advanced Composites Consortium is a US Government/Industry partnership supporting technologies to enable timeline and cost reduction in the development of certified composite aerospace structures. A key component of the consortium's approach is the development and validation of improved progressive damage and failure analysis methods for composite structures. These methods will enable increased use of simulations in design trade studies and detailed design development, and thereby enable more targeted physical test programs to validate designs. To accomplish this goal with confidence, a rigorous verification and validation process was developed. The process was used to evaluate analysis methods and associated implementation requirements to ensure calculation accuracy and to gage predictability for composite failure modes of interest. This paper introduces the verification and validation process developed by the consortium during the Phase I effort of the Advanced Composites Project. Specific structural failure modes of interest are first identified, and a subset of standard composite test articles are proposed to interrogate a progressive damage analysis method's ability to predict each failure mode of interest. Test articles are designed to capture the underlying composite material constitutive response as well as the interaction of failure modes representing typical failure patterns observed in aerospace structures.

Wanthal, Steven↗
NASA NTRS1992

Experimental validation of structural optimization methods

The topic of validating structural optimization methods by use of experimental results is addressed. The need for validating the methods as a way of effecting a greater and an accelerated acceptance of formal optimization methods by practicing engineering designers is described. The range of validation strategies is defined which includes comparison of optimization results with more traditional design approaches, establishing the accuracy of analyses used, and finally experimental validation of the optimization results. Examples of the use of experimental results to validate optimization techniques are described. The examples include experimental validation of the following: optimum design of a trussed beam; combined control-structure design of a cable-supported beam simulating an actively controlled space structure; minimum weight design of a beam with frequency constraints; minimization of the vibration response of helicopter rotor blade; minimum weight design of a turbine blade disk; aeroelastic optimization of an aircraft vertical fin; airfoil shape optimization for drag minimization; optimization of the shape of a hole in a plate for stress minimization; optimization to minimize beam dynamic response; and structural optimization of a low vibration helicopter rotor.

Adelman, Howard M.↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1997

Multidisciplinary Optimization Methods for Preliminary Design

An overview of multidisciplinary optimization (MDO) methodology and two applications of this methodology to the preliminary design phase are presented. These applications are being undertaken to improve, develop, validate and demonstrate MDO methods. Each is presented to illustrate different aspects of this methodology. The first application is an MDO preliminary design problem for defining the geometry and structure of an aerospike nozzle of a linear aerospike rocket engine. The second application demonstrates the use of the Framework for Interdisciplinary Design Optimization (FIDO), which is a computational environment system, by solving a preliminary design problem for a High-Speed Civil Transport (HSCT). The two sample problems illustrate the advantages to performing preliminary design with an MDO process.

Korte, J. J.↗
NASA NTRS1988

Machine intelligence and autonomy for aerospace systems

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Heer, Ewald↗
NASA NTRS2008

From Informal Safety-Critical Requirements to Property-Driven Formal Validation

Most of the efforts in formal methods have historically been devoted to comparing a design against a set of requirements. The validation of the requirements themselves, however, has often been disregarded, and it can be considered a largely open problem, which poses several challenges. The first challenge is given by the fact that requirements are often written in natural language, and may thus contain a high degree of ambiguity. Despite the progresses in Natural Language Processing techniques, the task of understanding a set of requirements cannot be automatized, and must be carried out by domain experts, who are typically not familiar with formal languages. Furthermore, in order to retain a direct connection with the informal requirements, the formalization cannot follow standard model-based approaches. The second challenge lies in the formal validation of requirements. On one hand, it is not even clear which are the correctness criteria or the high-level properties that the requirements must fulfill. On the other hand, the expressivity of the language used in the formalization may go beyond the theoretical and/or practical capacity of state-of-the-art formal verification. In order to solve these issues, we propose a new methodology that comprises of a chain of steps, each supported by a specific tool. The main steps are the following. First, the informal requirements are split into basic fragments, which are classified into categories, and dependency and generalization relationships among them are identified. Second, the fragments are modeled using a visual language such as UML. The UML diagrams are both syntactically restricted (in order to guarantee a formal semantics), and enriched with a highly controlled natural language (to allow for modeling static and temporal constraints). Third, an automatic formal analysis phase iterates over the modeled requirements, by combining several, complementary techniques: checking consistency; verifying whether the requirements entail some desirable properties; verify whether the requirements are consistent with selected scenarios; diagnosing inconsistencies by identifying inconsistent cores; identifying vacuous requirements; constructing multiple explanations by enabling the fault-tree analysis related to particular fault models; verifying whether the specification is realizable.

Cimatti, Alessandro↗
DOE OSTI2022

Advanced modeling and simulation of research reactors using dynamic mode decomposition

Full text of publication follows. Due to the ever-increasing safety requirements, the current trend of nuclear reactor analysis is shifting towards high-fidelity multi-physics models, which have a very high computational cost and modelling complexity. As the cost of even a single model run makes it impossible to analyse the behaviour and performance of these models on large-scale commercial plants, it has become even more significant to provide suitable benchmarks to validate and test them extensively. In this sense, research reactors offer a promising solution for the initial validation of high-fidelity models, as they are significantly smaller than commercial reactors and their characteristics are well known. In particular, the reactors of the TRIGA family have been used to assess and validate models and methods for Generation-IV designs, as they have some similar features (such as the dominance of natural convection as cooling mechanism and the difficulties in performing sub-channel analysis using standard codes). Still, the computational requirements of high-fidelity models make them unsuitable for real-time analysis, even following their assessment on research reactors. In this sense, Model Order Reduction (MOR) techniques give an additional strategy to reduce the computational cost of high-fidelity models (whilst preserving sufficient accuracy). In particular, this work focuses on Dynamic Mode Decomposition (DMD), a non-intrusive MOR technique that aims at representing models with explicit temporal dynamics by extracting the time-varying characteristics and the governing structures based only on a set of available data, thus without needing any underlying knowledge of the governing equations. In addition, DMD also computes a low-dimensional surrogate of the dynamic matrix of the system, making it suited for stability analysis and real-time evaluations. This work focuses on the application and validation of the DMD method on the Computational Fluid-Dynamics (CFD) model TRIGA Mark II reactor, also discussing in detail the potentiality of this algorithm as an advanced modelling tool for nuclear reactor analysis. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Qualification of Digitized Legacy Fast Reactor Data

The Integral Fast Reactor (IFR) fuel compatibility test program (1984-1994) included a variety of fuel pin examinations conducted at the Hot Fuel Examination Facility (HFEF) and the Alpha-Gamma Hot Cell Facility (AGHCF). Hard copy data records of these examinations have been recovered, scanned, and preserved in PDF format. Many hard copy records are now qualified in accordance with an NRC-approved Quality Assurance Program Plan (QAPP), and there is an ongoing effort to qualify additional legacy records. This legacy fuel performance data is vital to support design and licensing of fast reactors with validation of state-of-the-art codes and advanced methods for design and analysis. Stakeholders can most easily utilize this data when the PDF scans have been converted into digital data tables. However, qualification of the scanned hard copy data does not qualify the digital data file resulting from the digitization of the data contained in the record; the subject matter expert (SME) must make a review of the digitized data table as well before it can be designated as qualified. This report outlines a peer review process to qualify the digital data file(s), typically in CSV format, corresponding to hard copy records in accordance with the existing QAPP.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2017

Re-Tooling the Agency's Engineering Predictive Practices for Durability and Damage Tolerance

Over the past decade, the Agency has placed less emphasis on testing and has increasingly relied on computational methods to assess durability and damage tolerance (D&DT) behavior when evaluating design margins for fracture-critical components. With increased emphasis on computational D&DT methods as the standard practice, it is paramount that capabilities of these methods are understood, the methods are used within their technical limits, and validation by well-designed tests confirms understanding. The D&DT performance of a component is highly dependent on parameters in the neighborhood of the damage. This report discusses D&DT method vulnerabilities.

Piascik, Robert S.↗
DOE OSTI2022

PCP consensus protein/peptide alphavirus antigens stimulate broad spectrum neutralizing antibodies

Vaccines based on proteins and peptides may be safer and if calculated based on many sequences, more broad-spectrum than those designed based on single strains. Physicochemical Property Consensus (PCP con ) alphavirus (AV) antigens from the B-domain of the E2 envelope protein were designed, synthesized recombinantly and shown to be immunogenic (i.e. sera after inoculation detected the antigen in dotspots and ELISA). Antibodies in sera after inoculation with B-region antigens based on individual AV species (eastern or Venezuelan equine encephalitis (EEEV con , VEEV con ), or chikungunya (CHIKV con ) bound only their cognate protein, while those designed against multiple species (Mosaik con and EVC con ) recognized all three serotype specific antigens. The VEEV con and EEEV con sera only showed antiviral activity against their related strains (in plaque reduction neutralization assays (PRNT 50/80 ). Peptides designed to surface exposed areas of the E2-A-domain of CHIKV con were added to CHIKV con inocula to provide anti-CHIKV antibodies. EVC con , based on three different alphavirus species, combined with E2-A-domain peptides from AllAV con , a PCPcon of 24 diverse AV, generated broad spectrum, antiviral antibodies against VEEV, EEEV and CHIKV, AV with less than 35% amino acid identity to each other (>65% diversity). This is a promising start to a molecularly defined vaccine against all AV. A further study with these antigens can illuminate what areas are most important for a robust immune response, resistant to mutations in rapidly evolving viruses. The validated computational methods can also be used to design broad spectrum antigens against many other pathogen families.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗
NASA NTRS1984

Application of numerical optimization to rotor aerodynamic design

Based on initial results obtained from the performance optimization code, a number of observations can be made regarding the utility of optimization codes in supporting design of rotors for improved performance. (1) The primary objective of improving the productivity and responsiveness of current design methods can be met. (2) The use of optimization allows the designer to consider a wider range of design variables in a greatly compressed time period. (3) Optimization requires the user to carefully define his problem to avoid unproductive use of computer resources. (4) Optimization will increase the burden on the analyst to validate designs and to improve the accuracy of analysis methods. (5) Direct calculation of finite difference derivatives by the optimizer was not prohibitive for this application but was expensive. Approximate analysis in some form would be considered to improve program response time. (6) Program developement is not complete and will continue to evolve to integrate new analysis methods, design problems, and alternate optimizer options.

Pleasants, W. A., III↗