Results for “Safety parameters” — Page 15

NASA NTRS1976

A composite system approach to aircraft cabin fire safety

The thermochemical and flammability characteristics of two polymeric composites currently in use and seven others being considered for use as aircraft interior panels are described. The properties studied included: (1) limiting oxygen index of the composite constituents; (2) fire containment capability of the composite; (3) smoke evolution from the composite; (4) thermogravimetric analysis; (5) composition of the volatile products of thermal degradation; and (6) relative toxicity of the volatile products of pyrolysis. The performance of high-temperature laminating resins such as bismaleimides is compared with the performance of phenolics and epoxies. The relationship of increased fire safety with the use of polymers with high anaerobic char yield is shown. Processing parameters of one of the bismaleimide composites is detailed.

Kourtides, D. A.↗

NASA NTRS1979

Orbit Transfer Vehicle (OTV) engine, phase A study. Volume 2: Study

The hydrogen oxygen engine used in the orbiter transfer vehicle is described. The engine design is analyzed and minimum engine performance and man rating requirements are discussed. Reliability and safety analysis test results are presented and payload, risk and cost, and engine installation parameters are defined. Engine tests were performed including performance analysis, structural analysis, thermal analysis, turbomachinery analysis, controls analysis, and cycle analysis.

Mellish, J. A.↗

NASA NTRS1979

Thermal response of composite panels

The thermochemical and flammability characteristics of laminating resins and composites currently in use and others being considered for use as aircraft interior panels are described. The properties studied included: (1) limiting oxygen index of the composite constituents; (2) fire containment capability of the composite; (3) smoke evolution from the composite; (4) thermogravimetric analysis; (5) composition of the volatile products of thermal degradation; and (6) relative toxicity of the volatile products of pyrolysis. The performance of high-temperature laminating resins such as modified phenolics, polyimides and bismaleimides is compared with the performance of epoxies. The relationship of increased fire safety with the use of polymers with high anaerobic char yield is shown. Processing parameters of the state-of-the-art epoxy resin and the advanced resin composites are detailed.

Kourtides, D. A.↗

NASA NTRS1991

Calorimetric determination of the thermoneutral potential for Li/BrCl in SOCl2 (BCX) cells

Proliferation of lithium cells into large modular battery packs are projected for future space applications. Assuring battery design safety while maintaining high energy density requires accurate and precise knowledge of the thermal parameters of the battery cell. Specifically, the thermoneutral potential was determined using heat conduction calorimetry on Li/BrCl in SOCl2 (BCX) DD-cells and compared to measurements obtained on Li/SOCl2 D-cells. Over 20 to 60 C, the Li/BCX cells were found to have a thermoneutral potential significantly higher (near 4.0 volts) than that for the Li/SOCl2 cells tested. The higher heat generation measured during discharge reflects the higher electrochemical polarization observed with the BCX cells.

Darcy, Eric C.↗

NASA NTRS2007

Microbial Extremophiles in Aspect of Limits of Life

During Earth's evolution accompanied by geophysical and climatic changes a number of ecosystems have been formed. These ecosystems differ by the broad variety of physicochemical and biological factors composing our environment. Traditionally, pH and salinity are considered as geochemical extremes, as opposed to the temperature, pressure and radiation that are referred to as physical extremes (Van den Burg, 2003). Life inhabits all possible places on Earth interacting with the environment and within itself (cross species relations). In nature it is very rare when an ecotope is inhabited by a single species. As a rule, most ecosystems contain the functionally related and evolutionarily adjusted communities (consortia and populations). In contrast to the multicellular structure of eukaryotes (tissues, organs, systems of organs, whole organism), the highest organized form of prokaryotic life in nature is the benthic colonization in biofilms and microbial mats. In these complex structures all microbial cells of different species are distributed in space and time according to their functions and to physicochemical gradients that allow more effective system support, self-protection, and energy distribution. In vitro, of course, the most primitive organized structure for bacterial and archaeal cultures is the colony, the size, shape, color, consistency, and other characteristics of which could carry varies specifics on species or subspecies levels. In table 1 all known types of microbial communities are shown (Pikuta et a]., 2005). In deep underground (lithospheric) and deep-sea ecosystems an additional factor - pressure, and irradiation - could also be included in the list of microbial communities. Currently the beststudied ecosystems are: human body (due to the medical importance), and fresh water and marine ecosystems (due to the reason of an environmental safety). For a long time, extremophiles were terra incognita, since the environments with aggressive parameters (compared to the human body temperature, pH, mineralization, and pressure) were considered a priori as a dead zone.

Pikuta, Elena V.↗

NASA NTRS2012

Prevention of Over-Pressurization During Combustion in a Sealed Chamber

The combustion of flammable material in a sealed chamber invariably leads to an initial pressure rise in the volume. The pressure rise is due to the increase in the total number of gaseous moles (condensed fuel plus chamber oxygen combining to form gaseous carbon dioxide and water vapor) and, most importantly, the temperature rise of the gas in the chamber. Though the rise in temperature and pressure would reduce with time after flame extinguishment due to the absorption of heat by the walls and contents of the sealed spacecraft, the initial pressure rise from a fire, if large enough, could lead to a vehicle over-pressure and the release of gas through the pressure relief valve. This paper presents a simple lumped-parameter model of the pressure rise in a sealed chamber resulting from the heat release during combustion. The transient model considers the increase in gaseous moles due to combustion, and heat transfer to the chamber walls by convection and radiation and to the fuel-sample holder by conduction, as a function of the burning rate of the material. The results of the model are compared to the pressure rise in an experimental chamber during flame spread tests as well as to the pressure falloff after flame extinguishment. The experiments involve flame spread over thin solid fuel samples. Estimates of the heat release rate profiles for input to the model come from the assumed stoichiometric burning of the fuel along with the observed flame spread behavior. The sensitivity of the model to predict maximum chamber pressure is determined with respect to the uncertainties in input parameters. Model predictions are also presented for the pressure profile anticipated in the Fire Safety-1 experiment, a material flammability and fire safety experiment proposed for the European Space Agency (ESA) Automated Transfer Vehicle (ATV). Computations are done for a range of scenarios including various initial pressures and sample sizes. Based on these results, various mitigation approaches are suggested to prevent vehicle over-pressurization and help guide the definition of the space experiment.

Gokoglu, Suleyman A.↗

NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Laws for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss-estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗

NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Trends for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss- estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗

DOE OSTI2025

Restructuring and Optimizing Reactor Building Lifting Processes & Designing a Solution for an Overhead Door Handling Forklift Attachment

This poster presents two innovative projects aimed at enhancing operational efficiency and safety at the Advanced Test Reactor (ATR) Complex. The first project focuses on restructuring the existing lift book used for hoisting operations within the ATR Reactor Building. The current lift book, a cumbersome 150+ page document, is being transformed into a more efficient format using charts that allow for quick identification of maximum lift heights based on object footprint and weight. This new method also considers various parameters such as floor integrity and reactor status, dividing entries into four distinct charts to improve usability and safety during lifts. The second project involves designing a specialized forklift attachment for handling an overhead door at the ATR Reactor Building. The attachment is engineered to securely lift and lower a 1200lb, 14ft long door, ensuring safe replacement operations. The design process included research on forklift attachment codes, modeling in Autodesk Inventor, and testing through Finite Element Analysis (FEA) and hand calculations to confirm the attachment's structural integrity. Future work includes completing detailed drawings and an Engineering Calculations Analysis and Review (ECAR) document to proceed with manufacturing and assembly. Together, these projects demonstrate a commitment to optimizing reactor building operations through innovative engineering solutions and safety considerations.

42 - ENGINEERING↗

NASA NTRS2006

A Novel Lithium-ion Laminated Pouch Cell Tested For Performance And Safety

A new Li-ion 4.0 Ah pouch cell from GS Yuasa has been tested to determine its performance and safety. The cell is of a laminate pouch design with liquid electrolyte. The rate, thermal and vacuum performance capabilities have been tested to determine the optimum parameters. Under vacuum conditions, the cells were cycled under restrained and unrestrained configurations. The burst pressure of the laminate pouch was also determined. The overcharge, overdischarge into reversal and external short circuit safety tests were also performed to determine the cell s tolerance to abuse. Key Words: Li-ion, safety, vacuum test, abuse, COTS batteries, rate capability

Jeevarajan, Judith A.↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRSDate not supplied

Parametric Study of Federated Conflict Resolution for UAM Operations

This work presents a federated conflict resolution algorithm and its parametric study for UAM operations. A federated speed-control-based conflict resolution algorithm is introduced first, including its rules of the road, data exchange requirement, and critical parameters. Two experiments were set up for the parametric study. The first investigates five parameters: look ahead time, resolution update interval, maximum allowed speed reduction, traffic flow interval, and crossing angle. The second experiment studies the uncertainty of departure time. Metrics associated with safety, efficiency, and conflict resolution effort were measured for each scenario. A Design Of Experiment (DOE) analysis was used to perform the multi-factor analysis for the first experiment. It revealed that the crossing angle and flow interval were the most critical parameters across all three metrics, followed by maximum allowed speed reduction. Look ahead time and resolution update interval were of minor significance to safety and conflict resolution effort, but had little to no effect on efficiency. The analysis of the second experiment showed that, given a flow rate, the fluctuation in departure time was absorbed by the conflict resolution algorithm, which resulted in a relatively small fluctuation in airborne delay.

Urban air mobility↗

NASA NTRSDate not supplied

Parametric Study of Federated Conflict Resolution for UAM Operations using DOE Analysis

This work presents a federated conflict resolution algorithm and its parametric study for UAM operations. A federated speed-control-based conflict resolution algorithm is introduced first, including its rules of the road, data exchange requirement, and critical parameters. Two experiments were set up for the parametric study. The first investigates five parameters: look ahead time, resolution update interval, maximum allowed speed reduction, traffic flow interval, and crossing angle. The second experiment studies the uncertainty of departure time. Metrics associated with safety, efficiency, and conflict resolution effort were measured for each scenario. A Design Of Experiment (DOE) analysis was used to perform the multi-factor analysis for the first experiment. It revealed that the crossing angle and flow interval were the most critical parameters across all three metrics, followed by maximum allowed speed reduction. Look ahead time and resolution update interval were of minor significance to safety and conflict resolution effort, but had little to no effect on efficiency. The analysis of the second experiment showed that, given a flow rate, the fluctuation in departure time was absorbed by the conflict resolution algorithm, which resulted in a relatively small fluctuation in airborne delay.

Urban air mobility↗

DOE OSTI2024

Dose Consequence and Probabilistic Risk Assessment Integration into Digital Documented Safety Analysis

This is an intern poster presentation. The current reactor authorization process is complex and error prone. The development of a digital Documented Safety Analysis has been proposed to provide an automated and integrated solution to enhance the design and authorization process. The digital DSA will consist of interlinked models, analyses, and reports, all of which will be updated using automated workflows when design changes are made. This poster examines the integration of the probabilistic risk assessment (PRA) with the transient and dose consequence analyses. A PRA for a generic high temperature gas-cooled reactor (HTGR) has been constructed which will drive the input parameters for a transient analysis model currently being constructed. Dose consequence will be determined using the results of the transient analysis and the Radiation Safety Analysis Computer (RSAC) code. Dose consequence data will then be input back into the PRA to drive design parameters.

42 ENGINEERING↗

DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Distributed fiber optic sensing is a cutting-edge technology that has found extensive applications in the monitoring of Ensuring the safety, integrity, and operational efficiency of underground product pipelines is vital for maintaining the nation’s critical infrastructure. Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensors—were employed to measure key parameters like hoop strain, pressure, and acoustic vibrations. The underground product pipeline's outer diameter is 30 inches, the wall thickness is 1.28 inches, and the 3-foot depth. The fiber deployment strategies, and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety.

distributed fiber sensing↗

DOE OSTI2023

A review on the sizing and selection of control valves for thermal hydraulics for reactor system applications

This study presents an overview regarding how to model/size, choose, and maintain control valves (CVs) for a reactor system thermal hydraulics experimentation—specifically simulating pipe break(s) and a loss of coolant accident (LOCA) analysis. In a thermal hydraulics test loop, CVs modulate fluid flow according to the control signal generated by the controller to control the process parameter. The selection of a CV in industrial applications involves many factors, including process diversity, safety, and reliability. Further, the key issues and challenges for performing proper mathematical modeling, installation, servicing, and calibration are also discussed for the benefit of operators and end-users. Additionally, this research includes case studies illustrating major plant-level accidents associated with the malfunctioning of valves. Therefore, the study will serve as a knowledge base or reference guide for young professionals and operators who wish to better understand process control applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗