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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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NASA NTRS1998

In-Space Transportation with Tethers

The annual report covers the research conducted on the following topics related to the use of spaceborne tethers for in-space transportation: ProSEDS tether modeling (current collection analyses, influence of a varying tether temperature); proSEDS mission analysis and system dynamics (tether thermal model, thermo-electro-dynamics integrated simulations); proSEDS-tether development and testing (tether requirements, deployment test plan, tether properties testing, deployment tests); and tethers for reboosting the space-based laser (mission analysis, tether system preliminary design, evaluation of attitude constraints).

Lorenzini, Enrico↗

NASA NTRS2003

Effective Team Support: From Modeling to Software Agents

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and engineers and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in modeling infrastructure and task infrastructure. Work is continuing under a different contract to complete empirical data collection, cognitive modeling, and the building of software agents to support the teams task.

Remington, Roger W.↗

NASA NTRS1998

Predicting lettuce canopy photosynthesis with statistical and neural network models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Non-NASA Center↗

NASA NTRS1996

Improved Supportability Analysis

The University of Dayton is pleased to submit this report to the National Aeronautics and Space Administration (NASA), Langley Research Center, which updates recently developed computer models and data bases used in determining operational capabilities and support requirements during the conceptual design of proposed space systems. This research has resulted in some revisions of the reliability and maintainability (R&M) model, the development of several electronic data bases which capture the original data used in the R&M model, the collection of more recent R&M aircraft data and the integration of new shuttle data into the electronic data base and the R&M model. Descriptions of the model updates and of the electronic data bases are the subject of this report. Other details concerning the R&M model and the O&S costing model may be found in previous reports accomplished under this grant (NASA Research Grant NAG- 1-1327) which are referenced in paragraph 1.3.

Ebeling, Charles E.↗

NASA NTRS2000

The Mathematics of Navigating the Solar System

In navigating spacecraft throughout the solar system, the space navigator relies on three academic disciplines - optimization, estimation, and control - that work on mathematical models of the real world. Thus, the navigator determines the flight path that will consume propellant and other resources in an efficient manner, determines where the craft is and predicts where it will go, and transfers it onto the optimal trajectory that meets operational and mission constraints. Mission requirements, for example, demand that observational measurements be made with sufficient precision that relativity must be modeled in collecting and fitting (the estimation process) the data, and propagating the trajectory. Thousands of parameters are now determined in near real-time to model the gravitational forces acting on a spacecraft in the vicinity of an irregularly shaped body. Completing these tasks requires mathematical models, analyses, and processing techniques. Newton, Gauss, Lambert, Legendre, and others are justly famous for their contributions to the mathematics of these tasks. More recently, graduate students participated in research to update the gravity model of the Saturnian system, including higher order gravity harmonics, tidal effects, and the influence of the rings. This investigation was conducted for the Cassini project to incorporate new trajectory modeling features in the navigation software. The resulting trajectory model will be used in navigating the 4-year tour of the Saturnian satellites. Also, undergraduate students are determining the ephemerides (locations versus time) of asteroids that will be used as reference objects in navigating the New Millennium's Deep Space 1 spacecraft autonomously.

Hintz, Gerald↗

NASA NTRS1993

Mass storage system reference model, Version 4

The high-level abstractions that underlie modern storage systems are identified. The information to generate the model was collected from major practitioners who have built and operated large storage facilities, and represents a distillation of the wisdom they have acquired over the years. The model provides a common terminology and set of concepts to allow existing systems to be examined and new systems to be discussed and built. It is intended that the model and the interfaces identified from it will allow and encourage vendors to develop mutually-compatible storage components that can be combined to form integrated storage systems and services. The reference model presents an abstract view of the concepts and organization of storage systems. From this abstraction will come the identification of the interfaces and modules that will be used in IEEE storage system standards. The model is not yet suitable as a standard; it does not contain implementation decisions, such as how abstract objects should be broken up into software modules or how software modules should be mapped to hosts; it does not give policy specifications, such as when files should be migrated; does not describe how the abstract objects should be used or connected; and does not refer to specific hardware components. In particular, it does not fully specify the interfaces.

Coleman, Sam↗

DOE OSTI2025

Hitting the Thermal Target for Leptophilic Dark Matter at Future Lepton Colliders

We study future lepton collider prospects for testing predictive models of leptophilic dark matter (DM) candidates with a thermal origin. We calculate experimental milestones for testing the parameter space compatible with freeze-out and the associated collider signals at past, present, and future facilities. This analysis places new limits on such models by leveraging the utility of lepton colliders. At 𝑒+⁢𝑒− machines, we make projections using precision 𝑍 -pole observables from 𝑒+⁢𝑒−→ℓ+⁢ℓ−+𝐸 signatures at large electron-positron collider and future projections for future circular collider (ee) in these channels. Additionally, a muon collider could also probe new thermal relic parameter space in this scenario via 𝜇+⁢𝜇−→𝑋+𝐸 , where 𝑋 is any easily identifiable standard model object. Collectively, these processes can probe much of the parameter space for which DM direct annihilation to ℓ+⁢ℓ− yields the observed relic density in Higgs-like models with mass-proportional couplings to charged leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1975

Modular biowaste monitoring system

The objective of the Modular Biowaste Monitoring System Program was to generate and evaluate hardware for supporting shuttle life science experimental and diagnostic programs. An initial conceptual design effort established requirements and defined an overall modular system for the collection, measurement, sampling and storage of urine and feces biowastes. This conceptual design effort was followed by the design, fabrication and performance evaluation of a flight prototype model urine collection, volume measurement and sampling capability. No operational or performance deficiencies were uncovered as a result of the performance evaluation tests.

Fogal, G. L.↗

NASA NTRS1995

Polarimetric signatures of sea ice. 2: Experimental observations

Experimental observations of polarimetric signatures are presented for sea ice in the Beaufort Sea under cold winter conditions and interpreted with the composite model developed in Part 1. Polarimetric data were acquired in March 1988 with the Jet Propulsion Laboratory multifrequency airborne synthetic aperture radar (SAR) during the Beaufort Sea Flight Campaign. The experimental area was located near 75 N latitude and spanned 140 deg-145 deg W longitude. Selected sea ice scenes contain various ice types, including multiyear, thick first-year, and thin lead ice. Additionally, the C band SAR on the first European Remote Sensing Satellite provides supplementary backscattering data of winter Beaufort Sea ice for small incident angles (20 deg-26 deg) at vertical polarization. Sea ice characterization and environment data used in the model were collected at the Applied Physics Laboratory drifting ice station to the northeast of Prudhoe Bay; additional data from field and laboratory experiments are also utilized in this analysis. The model related sea ice polarimetric backscattering signatures to physical, structural, and electromagnetic properties of sea ice. Scattering mechanisms contributing to sea ice signatures are explained, and sensitivies of polarimetric signatures to sea ice characterization parameters are studied.

Nghiem, S. V.↗

NASA NTRS1985

Discrete-object modeling of remotely sensed scenes

Remotely sensed scenes can be modeled as collections of discrete, three-dimensional objects that cast shadows on a background. Approaching scenes from this perspective has led to two related lines of research. First is the geometric/optical modeling of a forest canopy, in which conifers are modeled as cones whose size and spacing vary according to functions established by field measurements. This canopy model is 'L-resolution' in nature - the objects are smaller than the resolution cells of the image and cannot be resolved individually. Second is scene modeling in which image variance is taken as a function of the relationship between the size, shape and spacing of objects and the resolution cell size of the digital image derived from the scene. This modeling is 'H-resolution' in nature - the objects in the scene are assumed to be larger than the resolution cells of the image, and thus can be individually distinguished. Both approaches illustrate the utility of the discrete-object scene model in extracting information from remotely sensed scenes.

Strahler, A. H.↗

DOE OSTI2025

Gaussian-process generative model for the QCD equation of state

We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian process regression method. We impose first-principles theoretical constraints from lattice quantum chromodynamics and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian process regression model to vary freely near the phase transition region, we generate random smooth crossover equations of state with different speeds of sound that do not rely on specific parametrizations. Here, we explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗

NASA NTRS2005

Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.

Remington, Roger W.↗

DOE OSTI2024

Collective excitations and low-energy ionization signatures of relativistic particles in silicon detectors

Abstract Solid-state detectors with a low energy threshold have several applications, including searches of non-relativistic halo dark-matter particles with sub-GeV masses. When searching for relativistic, beyond-the-Standard-Model particles with enhanced cross sections for small energy transfers, a small detector with a low energy threshold may have better sensitivity than a larger detector with a higher energy threshold. In this paper, we calculate the low-energy ionization spectrum from high-velocity particles scattering in a dielectric material. We consider the full material response including the excitation of bulk plasmons. We generalize the energy-loss function to relativistic kinematics, and benchmark existing tools used for halo dark-matter scattering against electron energy-loss spectroscopy data. Compared to calculations commonly used in the literature, such as the Photo-Absorption-Ionization model or the free-electron model, including collective effects shifts the recoil ionization spectrum towards higher energies, typically peaking around 4–6 electron-hole pairs. We apply our results to the three benchmark examples: millicharged particles produced in a beam, neutrinos with a magnetic dipole moment produced in a reactor, and upscattered dark-matter particles. Our results show that the proper inclusion of collective effects typically enhances a detector’s sensitivity to these particles, since detector backgrounds, such as dark counts, peak at lower energies.

Physics↗

NASA NTRS1989

Proton production in relativistic heavy ion collisions; comparison with a thermodynamical model

Experimental results concerning proton production in nuclear collisions, obtained at Saturne with the Diogene 4 pi facility, are compared with the predictions of a thermodynamical model, using collective velocity distributions combined with a statistical thermodynamics in local rest frames. Experimental differential cross sections for alpha + nucleus and Neon + nucleus central collisions at incident energies between 200 and 800 MeV per nucleon are well reproduced by the model, for an angular range 30-110 degrees in the laboratory system. Extracted values of the temperatures are compared with those given by other authors.

NASA Program Biomedical Research↗

NASA NTRSDate not supplied

Implementing Geometric Surface Imperfections into Sandwich Composite Cylinder Finite Element Method Models

The buckling responses of certain cylindrical shell structures are extremely sensitive to geometric surface imperfections. The NASA Engineering and Safety Center (NESC) Shell Buckling Knockdown Factor Project (SBKF) is conducting research to develop analysis-based buckling design recommendations. Experiments are used to verify the analysis-based factors, but the sensitivity of the test articles to geometric imperfections requires implementing as-manufactured imperfections into high-fidelity finite element method models. Data collection methods such as structured light scanning are used for all geometric surface data used in this work. Common preprocessing and visualization steps used in SBKF are discussed, and steps on how surface scans are prepared for implementation into a finite element model is described. The Python Tool for Implementing Geometric Imperfections in Reduced Structures (Py_TIGIRS), written specifically for the use with SBKF, is briefly described and uses eight functions to extract, modify, and write geometric imperfections into Abaqus input files. Results of the pre-processing methods and results from Py_TIGIRS are provided and compared for Composite Test Article (CTA) 8.2B. Excellent agreement between the visualized scan data and the FEM-extracted geometry is demonstrated. A brief example of why geometric surface imperfections are significant in nonlinear numerical analyses for thin cylinders in axial compression is provided as motivation to use tools such as Py_TIGIRS. Future development of Py_TIGIRS including expansion to structures of arbitrary geometry is planned.

Sandwich structures↗

NASA NTRS1993

An information model for use in software management estimation and prediction

This paper describes the use of cluster analysis for determining the information model within collected software engineering development data at the NASA/GSFC Software Engineering Laboratory. We describe the Software Management Environment tool that allows managers to predict development attributes during early phases of a software project and the modifications we propose to allow it to develop dynamic models for better predictions of these attributes.

Li, Ningda R.↗

DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗