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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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This paper describes using Proper Orthogonal Decomposition (POD) method to create low-order dynamical models for the Model Filter component of Beacon-based Exception Analysis for Multi-missions (BEAM).
Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.
A method is given for obtaining a mathematical model of a class of black boxes having multiple inputs and multiple outputs in terms of Ito stochastic integral equations. This method is applicable to the class of black boxes having ergodic correlation functions when there is zero applied input. The point of view adopted in this paper is phenomenological in that it is desired that calculations made using the mathematical model should be close to what is actually observed at the output of the black box. How close is defined in the problem statement.
Within the transport model evaluation project (TMEP) of simulations for heavy-ion collisions, the mean-field response is examined here. Specifically, zero-sound propagation is considered for neutron-proton symmetric matter enclosed in a periodic box, at zero temperature and around normal density. The results of several transport codes belonging to two families (BUU-like and QMD-like) are compared among each other and to exact calculations. For BUU-like codes, employing the test particle method, the results depend on the combination of the number of test particles and the spread of the profile functions that weight integration over space. These parameters can be properly adapted to give a good reproduction of the analytical zero-sound features. QMD-like codes, using molecular dynamics methods, are characterized by large damping effects, attributable to the fluctuations inherent in their phase-space representation. Moreover, for a given nuclear effective interaction, they generally lead to slower density oscillations, as compared to BUU-like codes. The latter problem is mitigated in the more recent lattice formulation of some of the QMD codes. Furthermore, the significance of these results for the description of real heavy-ion collisions is discussed.
SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.
The analytical and experimental results of an all-composite wing stub box are presented in this report. The wing stub box, which is representative of an inboard portion of a commercial transport high-aspect-ratio wing, was fabricated from stitched graphite-epoxy material with a Resin Film Infusion manufacturing process. The wing stub box was designed and constructed by the McDonnell Douglas Aerospace Company as part of the NASA Advanced Composites Technology program. The test article contained metallic load-introduction structures on the inboard and outboard ends of the graphite-epoxy wing stub box. The root end of the inboard load introduction structure was attached to a vertical reaction structure, and an upward load was applied to the outermost tip of the outboard load introduction structure to induce bending of the wing stub box. A finite element model was created in which the center portion of the wing-stub-box upper cover panel was modeled with a refined mesh. The refined mesh was required to represent properly the geometrically nonlinear structural behavior of the upper cover panel and to predict accurately the strains in the stringer webs of the stiffened upper cover panel. The analytical and experimental results for deflections and strains are in good agreement.
Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.
Current research on software reliability does not lend itself to quantitatively assessing the risk posed by a piece of life-critical software. Black-box software reliability models are too general and make too many assumptions to be applied confidently to assessing the risk of life-critical software. We present a model for assessing the risk caused by a piece of software; this model combines software testing results and Hamlet's probable correctness model. We show how this model can assess software risk for those who insure against a loss that can occur if life-critical software fails.
Existing generator parameterization methods, typically developed for large turbine generator units, are difficult to apply to small kW-level diesel generators in microgrid applications. Here, this article presents a model parameterization method that estimates a complete set of kW-level diesel generator parameters simultaneously using only load-step-change tests with limited measurement points. This method provides a more cost-efficient and robust approach to achieve high-fidelity modeling of diesel generators for microgrid dynamic simulation. A two-stage hybrid box-constrained Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal parameter set given the parameter bounds. A heuristic algorithm, namely Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to identify proper initial estimates at the first stage, followed by a modified Levenberg-Marquardt algorithm designed to fine tune the solution based on the first-stage result. The proposed method is validated against dynamic simulation of a diesel generator model and field measurements from a 16kW diesel generator unit.
The influence of physical variables on the aerodynamic drag of a box shaped vehicle model was studied. The physical variables included built-in rounded front corners, and two different designs of add on flow vanes for the front of box shaped vehicle with 67 deg and 90 deg of arc. For a diesel powered vehicle, only slightly larger than a family van, the built in rounded front corners provide a calculated fuel saving of about 6.0 liters per hour of driving (1.6 gallons per hour) at 88.6 km per hour (55 mph) in national average winds, as compared to the baseline vehicle having all square corners. The corresponding savings for a baseline vehicle to which front mounted flow vanes were added is competitive. For a gasoline powered vehicle the volumetric fuel savings would be larger by a factor of about 1.7. The fuel savings for a standard size motor home would be greater for the diesel or gasoline powered vehicles by from 30 to 35 percent because of the larger frontal area. Thus projected fuel savings for a standard size motor home powered by gasoline can approach 12.5 to 13.5 liters (3.3 to 3.6 gallons) for each hour driving at highway speeds.
This paper reports computational analyses of testsin a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using 55-deg sphere-cone models placed in a free jet downstream of the 13-inch diameter conical nozzle in the Ames 60-MW Interaction Heating Facility. Some of the sphere-cone models include surface features such as manufactured fences and gaps intended to simulate effects of differential recession, all of which disturb the flow, producing augmented heating locally and downstream.Test calibration data were obtained using slug and Gardon gage stagnation calorimeters, and a sphere-cone calorimeter model with six Gardon gages and five pressure tabs.The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested, and comparisons with the experimental measurements. These simulations take into account nonuniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields.
This paper reports computational analyses of tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using 55-deg sphere-cone models placed in a free jet downstream of the 13-inch diameter conical nozzle in the Ames 60-MW Interaction Heating Facility. Some of the sphere-cone models include surface features such as manufactured fences and gaps intended to simulate effects of differential recession, all of which disturb the flow, producing augmented heating locally and downstream. Test calibration data were obtained using slug and Gardon gage stagnation calorimeters, and a sphere-cone calorimeter model with six Gardon gages and five pressure tabs. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested, and comparisons with the experimental measurements. These simulations take into account nonuniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields.
This paper reports computational analyses of tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using 55-deg sphere-cone models placed in a free jet downstream of the 13-inch diameter conical nozzle in the Ames 60-MW Interaction Heating Facility. Some of the sphere-cone models include surface features such as manufactured fences and gaps intended to simulate effects of differential recession, all of which disturb the flow, producing augmented heating locally and downstream. Test calibration data were obtained using slug and Gardon gage stagnation calorimeters, and a sphere-cone calorimeter model with six Gardon gages and five pressure tabs. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested, and comparisons with the experimental measurements. These simulations take into account nonuniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields.
This paper reports computational analyses of tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using 23.6-cm diameter, 55° sphere-cone models placed in a free jet downstream of the 33.0-cm diameter conical nozzle in the 60-MW Interaction Heating Facility. Some of the sphere-cone models include surface features such as manufactured fences and gaps intended to simulate effects of differential recession, all of which disturb the flow, producing augmented heating locally and downstream. Test calibration data were obtained using slug and Gardon gage stagnation calorimeters, and a sphere-cone calorimeter model with six Gardon heat flux gages and five pressure gages. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested, and comparisons with the experimental measurements. Various issues related to testing are considered: diffuser flow capture, flow characterization based on the calorimeter data, effects of the model surface recession, and prediction of surface quantities for the models with surface features. These simulations take into account nonuniform total enthalpy profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields.
An analysis was performed to determine whether a hydrogen jet flame impinging on a tunnel ceiling composed of multiple prestressed steel reinforced concrete box beams could result in permanent damage to the tunnel. The lower layer of the concrete box beam was modeled to determine whether heat reaches the steel reinforcing bars and whether spalling could occur. Heat transfer analysis shows that the temperature remains constant at the location of the steel reinforcing bars after 1.3 minutes of impingement and reaches a maximum of 130°C after 5 minutes. However, assuming a constant impingement for 5 minutes is an over estimation due the existing fire model which includes conservative assumptions. Explosive spalling may occur at a thin layer (~0.05 in. at 50 seconds, 0.1 in. at 5 minutes) at the bottom surface of the concrete box beam, but the steel reinforcing bars will not be exposed to the hydrogen flame.
Here in this work we present a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that are given in analytic form. In contrast to state-of-the-art model reduction methods that are intrusive and thus require full knowledge of the governing equations and the operators of a full model of the discretized dynamical system, the proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver. The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated on the right-hand side. The least-squares problem is linear and thus can be solved efficiently in practice. The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion–reaction Chafee–Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process. The numerical results provide evidence that the proposed approach learns reduced models that achieve comparable accuracy as models constructed with state-of-the-art intrusive model reduction methods that require full knowledge of the governing equations.
The development of composite wing structures has focused on the use of mechanical fasteners to join heavily-loaded areas, while bonded joints have been used only for select locations. The focus of this paper is the examination of the adhesive layer in a generic bonded wing box that represents a "fastenerless" or unitized structure in order to characterize the general behavior and failure mechanisms. A global/local approach was applied to study the response of the adhesive layer using a global shell model and a local shell/solid model. The wing box was analyzed under load to represent a high-g up-bending condition such that the strains in the composite sandwich face sheets are comparable to an expected design allowable. The global/local analysis indicates that at these wing load levels the strains in the adhesive layer are well within the adhesive's elastic region, such that yielding would not be expected in the adhesive layer. The global/local methodology appears to be a promising approach to evaluate the structural integrity of the adhesively bonded structures.
As Machine Learning (ML) continues to advance, it is being integrated into more systems. Often, the ML component represents a significant portion of the system that reduces the burden on the end user or significantly improves task performance. However, the ML component represents an unknown complex phenomenon that is learned from collected data without the need to be explicitly programmed. Despite the improvement in task performance, the models are often black boxes. Evaluating the credibility and the vulnerabilities of ML models poses a gap in current test and evaluation practice. For high consequence applications, the lack of testing and evaluation procedures represents a significant source of uncertainty and risk. To help reduce that risk, here we present considerations to evaluate systems embedded with an ML component within a red-teaming inspired methodology. We focus on (1) cyber vulnerabilities to an ML model, (2) evaluating performance gaps, and (3) adversarial ML vulnerabilities.