Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “limited data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Space Radiation Induced Cytogenetic Damage in the Blood Lymphocytes of Astronauts

Cytogenetic analysis of astronauts blood lymphocytes provides a direct in vivo measurement of space radiation damage, which takes into account individual radiosensitivity and considers the influence of microgravity and other stress conditions. We present our latest analyses of chromosome damage in astronauts blood lymphocytes assessed by fluorescence in situ hybridization (FISH) chromosome painting and collected at various times beginning directly after return from space to several years after flight. Dose was derived from frequencies of chromosome exchanges using preflight calibration curves, and the Relative Biological Effect (RBE) was estimated by comparison with individually measured physically absorbed doses. Values for average RBE were compared to the average quality factor (Q), from direct measurements of the lineal energy spectra using a tissue-equivalent proportional counter (TEPC) and radiation transport codes. Results prove that cytogenetic biodosimetry analyses on blood collected within a week or two of return from space provides a reliable estimate of equivalent radiation dose and risk after protracted exposure to space radiation of a few months or more. However, data collected several months or years after flight suggests that the yield of chromosome translocations may decline with time after the mission, indicating that retrospective doses may be more difficult to estimate. In addition, limited data on multiple flights show a lack of correlation between time in space and translocation yields. Data from one crewmember, who has participated in two separate long-duration space missions and has been followed up for over 10 years, provide limited information on the effect of repeat flights and show a possible adaptive response to space radiation exposure.

George, K.↗
DOE OSTI2025

Reevaluation of Radiation-Protection Standards for Workers and the Public Based on Current Scientific Evidence

President Trump’s recent executive orders to “Usher in a Nuclear Renaissance,” coupled with the global pledge to triple nuclear energy capacity by 2050, underscore nuclear energy’s importance to national security and economic prosperity. This renewed interest has prompted efforts to spur nuclear-energy deployment, including assessing factors impeding it. One issue that has previously been identified as adding to the cost of nuclear energy is excessively conservative requirements, including those related to radiation protection. This technical review, therefore, examines current radiation-protection standards that were established decades ago when more limited data were available and nuclear-energy expansion was not a national priority. The review focuses on scientific evidence regarding the health effects of ionizing radiation at annual doses of 10,000 mrem or less. The review evaluates epidemiological studies, radiobiological research, and positions of relevant professional organizations to assess whether such doses result in discernable or observable increases in negative health outcomes. The review also surveys the literature related to economic and practical implications of current radiation-protection standards and practices. Based on this assessment, we propose maintaining an annual occupational whole-body dose limit of 5,000 mrem/yr and eliminating all “as low as reasonably achievable” requirements and limits below this threshold. This change could potentially reduce radiation-protection costs by millions of dollars annually for each reactor, as well as decrease the overall costs and correct misconceptions about the risks associated with all nuclear technologies. The evidence further supports future consideration of a 10,000 mrem/yr limit that would maintain appropriate safety margins while further reducing protection costs. Similarly, given the data and that the average annual radiation dose per person in the U.S. is 620 mrem, we believe the public dose limits of 100 mrem/yr are unnecessarily restrictive; increasing to 500 mrem/yr would maintain substantial safety margins—a factor of 10 below the occupational limit—while reducing regulatory burdens and associated bureaucracy. While we acknowledge ongoing scientific debate and encourage continued research on the health effects of ionizing radiation, our review indicates current frameworks are overly conservative. These overly stringent limits not only impose unnecessary economic burdens without corresponding health benefits but also divert safety focus and resources from more important considerations. Although this study was motivated by nuclear-power considerations, reforms to radiation-protection requirements have significant positive implications for other areas, such as nuclear medical applications, environmental remediation, nuclear-waste management and disposal, and industrial applications of nuclear technologies.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1984

The reflection of airborne UV laser pulses from the ocean

It is experimentally shown here for the first time that the normalized laser backscatter cross-section of the sea surface is a function of elevation or height position on teh ocean wave. All data were taken off-nadir, resulting in incidence angles of about 6.5 deg measured relative to the normal to mean sea level (MSL). In the limited data sets analyzed to date, the normalized backscatter cross-section was found to be higher in wave crest regions and lower in wave troughs for a swell-dominated sea over which the wind speed was 5 m/s. The reverse was found to be the case for a sea that was driven by a 14 m/s wind. These isolated results show that the MSL, as measured by an off-nadir and/or multibeam type satellite laser altimeter, will be found above, at, or below the true MSL, depending on the local sea conditions existing in the footprint of the altimeter. Airborne nadir-pointed laser altimeter data for a wide variety of sea conditions are needed before a final determination can be made of the effect of sea state on the backscatter cross-section as measured by a down-looking satellite laser system.

Hoge, F. E.↗
NASA NTRS2002

A Miniature Mineralogical Instrument for In-Situ Characterization of Ices and Hydrous Minerals at the Lunar Poles

Lunar missions over the past few years have provided new evidence that water may be present at the lunar poles in the form of cold-trapped ice deposits, thereby rekindling interest in sampling the polar regions. Robotic landers fitted with mineralogical instrumentation for in-situ analyses could provide unequivocal answers on the presence of crystalline water ice and/or hydrous minerals at the lunar poles. Data from Lunar Prospector suggest that any surface exploration of the lunar poles should include the capability to drill to depths of more than 40 cm. Limited data on the lunar geotherm indicate temperatures of approximately 245-255 K at regolith depths of 40 cm, within a range where water may exist in the liquid state as brine. A relevant terrestrial analog occurs in Antarctica, where the zeolite mineral chabazite has been found at the boundary between ice-free and ice-cemented regolith horizons, and precipitation from a regolith brine is indicated. Soluble halogens and sulfur in the lunar regolith could provide comparable brine chemistry in an analogous setting. Regolith samples collected by a drilling device could be readily analyzed by CheMin, a mineralogical instrument that combines X-ray diffraction (XRD) and X-ray fluorescence (XRF) techniques to simultaneously characterize the chemical and mineralogical compositions of granular or powdered samples. CheMin can unambiguously determine not only the presence of hydrous alteration phases such as clays or zeolites, but it can also identify the structural variants or types of clay or zeolite present (e.g., well-ordered versus poorly ordered smectite; chabazite versus phillipsite). In addition, CheMin can readily measure the abundances of key elements that may occur in lunar minerals (Na, Mg, Al, Si, K, Ca, Fe) as well as the likely constituents of lunar brines (F, Cl, S). Finally, if coring and analysis are done during the lunar night or in permanent shadow, CheMin can provide information on the chemistry and structure of any crystalline ices that might occur in the regolith samples.

Sarrazin, P.↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
NASA NTRS2012

Summary of 2011 Direct and Nearby Lightning Strikes to Launch Complex 39B, Kennedy Space Center, Florida

A Lightning Protection System (LPS) was designed and built at Launch Complex 39B (LC39B), at the Kennedy Space Center (KSC), Florida in 2009. This LPS was instrumented with comprehensive meteorological and lightning data acquisition systems that were deployed from late 2010 until mid 2011. The first direct strikes to the LPS were recorded in March of 2011, when a limited number of sensors had been activated. The lightning instrumentation system detected a total of 70 nearby strokes and 19 direct strokes to the LPS, 2 of the 19 direct strokes to the LPS had two simultaneous ground attachment points (in both instances one channel terminated on the LPS and the other on the nearby ground). Additionally, there are more unaccounted nearby strokes seen on video records for which limited data was acquired either due to the distance of the stroke or the settings of the data acquisition system. Instrumentation deployment chronological milestones, a summary of lightning strikes (direct and nearby), high speed video frames, downconductor currents, and dH/dt and dE/dt typical waveforms for direct and nearby strokes are presented.

Mata, C.T.↗
NASA NTRS2013

Dawn Orbit Determination Team: Trajectory Modeling and Reconstruction Processes at Vesta

The Dawn spacecraft spent over a year in orbit around Vesta from July 2011 through August 2012. In order to maintain the designated science reference orbits and enable the transfers between those orbits, precise and timely orbit determination was required. Challenges included low-thrust ion propulsion modeling, estimation of relatively unknown Vesta gravity and rotation models, track-ing data limitations, incorporation of real-time telemetry into dynamics model updates, and rapid maneuver design cycles during transfers. This paper discusses the dynamics models, filter configuration, and data processing implemented to deliver a rapid orbit determination capability to the Dawn project.

low thrust↗
NASA NTRS2013

A Post-Processing Receiver for the Lunar Laser Communications Demonstration Project

The Lunar Laser Communications Demonstration Project undertaken by MIT Lincoln Laboratory and NASA's Goddard Space Flight Center will demonstrate high-rate laser communications from lunar orbit to the Earth. NASA's Jet Propulsion Laboratory is developing a backup ground station supporting a data rate of 39 Mbps that is based on a non-real-time software post-processing receiver architecture. This approach entails processing sample-rate-limited data without feedback in the presence high uncertainty in downlink clock characteristics under low signal flux conditions. In this paper we present a receiver concept that addresses these challenges with descriptions of the photodetector assembly, sample acquisition and recording platform, and signal processing approach. End-to-end coded simulation and laboratory data analysis results are presented that validate the receiver conceptual design.

optical communications↗
DOE OSTI2025

AGR-5/6/7 Experiment Preliminary Findings

AGR-5/6/7 in-pile data have been published: Fuel irradiation conditions, Fission gas release-rate-to-birth-rate (R/B) ratios, PIE and safety testing are still in progress and only limited data have been published, and preliminary results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
NASA NTRS2014

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical problem sets and static, limited data samples. In addition, we identify existing gaps and possible solutions for addressing the infrastructure and tools as well as a pedagogical framework through which to implement this inductive approach.

Ramachandran, Rahul↗
NASA NTRS1973

Verification of performance of the Mariner 9 television cameras

Based on limited data, the Mariner 9 performance appears consistent throughout its mission. Several corrections have been provided by inflight verification sequences. A change in photometric response, which will impair precise photometric analysis, has been observed. Hence, only qualified photometric measurement appears feasible under limited conditions. Conversely, certain geometric and electronic parameters appear both measurable and congruent with preflight prediction. The role of inflight verification has been emphasized by these data to assess the in situ camera health and to promote efficient data analysis and interpretation.

Thorpe, T. E.↗
DOE OSTI2024

The expressivity of classical and quantum neural networks on entanglement entropy

Abstract Analytically continuing the von Neumann entropy from Rényi entropies is a challenging task in quantum field theory. While then-th Rényi entropy can be computed using the replica method in the path integral representation of quantum field theory, the analytic continuation can only be achieved for some simple systems on a case-by-case basis. In this work, we propose a general framework to tackle this problem using classical and quantum neural networks with supervised learning. We begin by studying several examples with known von Neumann entropy, where the input data is generated by representing$${\text {Tr}}\rho _A^n$$ Tr ρ A n with a generating function. We adopt KerasTuner to determine the optimal network architecture and hyperparameters with limited data. In addition, we frame a similar problem in terms of quantum machine learning models, where the expressivity of the quantum models for the entanglement entropy as a partial Fourier series is established. Our proposed methods can accurately predict the von Neumann and Rényi entropies numerically, highlighting the potential of deep learning techniques for solving problems in quantum information theory.

Physics↗
DOE OSTI2020

A Survey of Constrained Gaussian Process: Approaches and Implementation Challenges

Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a larger effort in scientific machine learning, many recent works have incorporated physical constraints or other a priori information within Gaussian process regression to supplement limited data and regularize the behavior of the model. We provide an overview and survey of several classes of Gaussian process constraints, including positivity or bound constraints, monotonicity and convexity constraints, differential equation constraints provided by linear PDEs, and boundary condition constraints. We compare the strategies behind each approach as well as the differences in implementation, concluding with a discussion of the computational challenges introduced by constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2021

Underlying each of the Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a Life-Cycle Cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample in order to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process. It is an update to previous reports on estimating commercial discount rates from firm-level financial data (Fujita, 2016). Major topics covered in this report include: Discount rate estimation methods and rationale; -Data sources used and data limitations; -Discount rate distributions for use in standards analysis; -Discount rate estimation methods and distributions specific to the small business subgroup analysis. Going forward, this report will be updated as data allow and analyses necessitate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Shaw Cortez, Wenceslao E.↗
DOE OSTI2023

Mixture Model for Refrigerant Pairs R-32/1234yf, R-32/1234ze(E), R-1234ze(E)/227ea, R-1234yf/152a, and R-125/1234yf

In this work, thermodynamic models based on the corresponding states framework with departure terms are developed for the refrigerant pairs R-32/1234yf, R-32/1234ze(E), R-1234ze(E)/227ea, R-1234yf/152a, and R-125/1234yf. These models are based on new measurements of density, speed of sound, and phase equilibria, combined with the data available in the literature. The model for R-32/1234yf is most comprehensive in its data coverage, with speed of sound deviations within 1%, density deviations within 0.1%, and bubble- and dew-point pressure deviations within 1%. In conclusion, the other mixtures have generally more limited data availability but a similar goodness of fit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗