Results for “Process modelling” — Page 44

NASA NTRS2012

Use NU-WRF and GCE Model to Simulate the Precipitation Processes During MC3E Campaign

One of major CRM approaches to studying precipitation processes is sometimes referred to as "cloud ensemble modeling". This approach allows many clouds of various sizes and stages of their lifecycles to be present at any given simulation time. Large-scale effects derived from observations are imposed into CRMs as forcing, and cyclic lateral boundaries are used. The advantage of this approach is that model results in terms of rainfall and QI and Q2 usually are in good agreement with observations. In addition, the model results provide cloud statistics that represent different types of clouds/cloud systems during their lifetime (life cycle). The large-scale forcing derived from MC3EI will be used to drive GCE model simulations. The model-simulated results will be compared with observations from MC3E. These GCE model-simulated datasets are especially valuable for LH algorithm developers. In addition, the regional scale model with very high-resolution, NASA Unified WRF is also used to real time forecast during the MC3E campaign to ensure that the precipitation and other meteorological forecasts are available to the flight planning team and to interpret the forecast results in terms of proposed flight scenarios. Post Mission simulations are conducted to examine the sensitivity of initial and lateral boundary conditions to cloud and precipitation processes and rainfall. We will compare model results in terms of precipitation and surface rainfall using GCE model and NU-WRF

Tao, Wei-Kuo↗

NASA NTRS1990

Control-oriented modeling of combustion and flow processes in liquid propellant rocket engines

This paper presents a control-oriented model of the flow, reaction, and transport processes in liquid propellant rocket combustion chambers, based on the multicomponent conservation laws of gas dynamics. This model provides a framework for the inclusion of detailed chemical kinetic relations, viscous and other dissipative effects, a variety of actuators and sensors, as well as process and measurement disturbances. In addition to its potential usefulness to the designer in understanding the dynamical complexity of the system and the sources of model uncertainty, the model provides a rigorous basis for control system design. An appraisal of current and feasible actuators and sensors, and their mathematical representation are included.

Bentsman, Joseph↗

NASA NTRS1993

A thermodynamic approach to obtain materials properties for engineering applications

With the ever increases in the capabilities of computers for numerical computations, we are on the verge of using these tools to model manufacturing processes for improving the efficiency of these processes as well as the quality of the products. One such process is casting for the production of metals. However, in order to model metal casting processes in a meaningful way it is essential to have the basic properties of these materials in their molten state, solid state as well as in the mixed state of solid and liquid. Some of the properties needed may be considered as intrinsic such as the density, heat capacity or enthalpy of freezing of a pure metal, while others are not. For instance, the enthalpy of solidification of an alloy is not a defined thermodynamic quantity. Its value depends on the micro-segregation of the phases during the course of solidification. The objective of the present study is to present a thermodynamic approach to obtain some of the intrinsic properties and combining thermodynamics with kinetic models to estimate such quantities as the enthalpy of solidification of an alloy.

Chang, Y. Austin↗

NASA NTRS2017

Building Thermal Models

This presentation is meant to be an overview of the model building process It is based on typical techniques (Monte Carlo Ray Tracing for radiation exchange, Lumped Parameter, Finite Difference for thermal solution) used by the aerospace industry This is not intended to be a "How to Use ThermalDesktop" course. It is intended to be a "How to Build Thermal Models" course and the techniques will be demonstrated using the capabilities of ThermalDesktop (TD). Other codes may or may not have similar capabilities. The General Model Building Process can be broken into four top level steps: 1. Build Model; 2. Check Model; 3. Execute Model; 4. Verify Results.

Modeling↗

NASA NTRSDate not supplied

Modeling NASA’s Procedural Requirement Processes – Implications for a Digital Future

The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions).

NPR↗

NASA NTRSDate not supplied

Process Drivers, Inter-model Spread, and a Path Forward: What Do We Know About Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Patrick Charles Taylor↗

NASA NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Arctic amplification↗

NASA NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Patrick C. Taylor↗

NASA NTRS1982

Effect of processing on Polymer/Composite structure and properties

Advances in the vitality and economic health of the field of polymer forecasting are discussed. A consistent and rational point of view which considers processing as a participant in the underlying triad of relationships which comprise materials science and engineering is outlined. This triad includes processing as it influences material structure, and ultimately properties. Methods in processing structure properties, polymer science and engineering, polymer chemistry and synthesis, structure and modification and optimization through processing, and methods of melt flow modeling in processing structure property relations of polymer were developed. Mechanical properties of composites are considered, and biomedical materials research to include polymer processing effects are studied. An analysis of the design technology of advances graphite/epoxy composites is also reported.

Source record↗

NASA NTRS1983

The structure of particle-laden jets and nonevaporating sprays

Mean and fluctuating gas velocities, liquid mass fluxes and drop sizes were in nonevaporating sprays. These results, as well as existing measurements in solid particle-laden jets, were used to evaluate models of these processes. The following models were considered: (1) a locally homogeneous flow (LHF) model, where slip between the phases was neglected; (2) a deterministic separated flow (DSF) model, where slip was considered but effects of particle dispersion by turbulence were ignored; and (3) a stochastic separated flow (SSF) model, where effects of interphase slip and turbulent dispersion were considered using random-walk computations for particle motion. The LHF and DSF models did not provide very satisfactory predictions over the present data base. In contrast, the SSF model performed reasonably well - including conditions in nonevaporating sprays where enhanced dispersion of particles by turbulence caused the spray to spread more rapidly than single-phase jets for comparable conditions. While these results are encouraging, uncertainties in initial conditions limit the reliability of the evaluation. Current work is seeking to eliminate this deficiency.

Shuen, J. S.↗

NASA NTRS1984

Visual Attention to Radar Displays

A model is described which predicts the allocation of attention to the features of a radar display. It uses the growth of uncertainty and the probability of near collision to call the eye to a feature of the display. The main source of uncertainty is forgetting following a fixation, which is modelled as a two dimensional diffusion process. The model was used to predict information overload in intercept controllers, and preliminary validation obtained by recording eye movements of intercept controllers in simulated and live (practice) interception.

Moray, N.↗

NASA NTRS1992

Impact-induced thermal effects in the lunar and Mercurian regoliths

Thermal effects of micrometeoroid impact into the regoliths of the moon and Mercury, and some comparisons between the regoliths of the two bodies are presented. The impact calculations used to estimate the volumes of melt and vapor produced in the regoliths of the two bodies are described. An overview of the process of impact metamorphism in a modeled regolith target is presented, in which the roles played by impact velocity and target temperature in determining the quantities of melt and vapor are evaluated. The model impact process and fluxes are combined to estimate the production rates for impact melt and vapor on the two bodies, and the results are compared with those of previous studies. It is concluded that the rates of impact melting and vaporization on Mercury are much greater than on the moon. In a given period of time, a factor of 14 times more melt and 20 times more vapor are produced on Mercury than on the moon. A 'typical' Mercurian microcratering event produces 2.6 times more melt than its lunar counterpart; the flux calculated for Mercury is 5.5 times higher than it is at the moon.

Cintala, Mark J.↗

NASA NTRS1993

Workshop on the Thermophysical Properties of Molten Materials

The role of accurate thermophysical property data in the process design and modeling of solidification processes was the subject of a workshop held on 22-23 Oct. 1992 in Cleveland, Ohio. The workshop was divided into three sequential sessions dealing with (1) industrial needs and priorities for thermophysical data, (2) experimental capabilities for measuring the necessary data, and (3) theoretical capabilities for predicting the necessary data. In addition, a 2-hour panel discussion of the salient issues was featured as well as a 2-hour caucus that assessed priorities and identified action plans.

Source record↗

NASA NTRS1994

A Cost and Performance System (CAPS) in a Federal agency

Cost and Performance System (CAPS) is an automated system used from the planning phase through implementation to analysis and documentation. Data is retrievable or available for analysis of cost versus performance anomalies. CAPS provides a uniform system across intra- and international elements. A common system is recommended throughout an entire cost or profit center. Data can be easily accumulated and aggregated into higher levels of tracking and reporting of cost and performance.The level and quality of performance or productivity is indicated in the CAPS model and its process. The CAPS model provides the necessary decision information and insight to the principal investigator/project engineer for a successful project management experience. CAPS provides all levels of management with the appropriate detailed level of data.

Huseonia, W. F.↗

NASA NTRS1994

Models of Human Information Requirements: "When Reasonable Aiding Systems Disagree"

Aircraft flight management and Air Traffic Control (ATC) automation are under development to maximize the economy of flight and to increase the capacity of the terminal area airspace while maintaining levels of flight safety equal to or better than current system performance. These goals are being realized by the introduction of flight management automation aiding and operations support systems on the flight deck and by new developments of ATC aiding systems that seek to optimize scheduling of aircraft while potentially reducing required separation and accounting for weather and wake vortex turbulence. Aiding systems on both the flight deck and the ground operate through algorithmic functions on models of the aircraft and of the airspace. These models may differ from each other as a result of variations in their models of the immediate environment. The resultant flight operations or ATC commands may differ in their response requirements (e.g. different preferred descent speeds or descent initiation points). The human operators in the system must then interact with the automation to reconcile differences and resolve conflicts. We have developed a model of human performance including cognitive functions (decision-making, rule-based reasoning, procedural interruption recovery and forgetting) that supports analysis of the information requirements for resolution of flight aiding and ATC conflicts. The model represents multiple individuals in the flight crew and in ATC. The model is supported in simulation on a Silicon Graphics' workstation using Allegro Lisp. Design guidelines for aviation automation aiding systems have been developed using the model's specification of information and team procedural requirements. Empirical data on flight deck operations from full-mission flight simulation are provided to support the model's predictions. The paper describes the model, its development and implementation, the simulation test of the model predictions, and the empirical validation process. The model and its supporting data provide a generalizable tool that is being expanded to include air/ground compatibility and ATC crew interactions in air traffic management.

Corker, Kevin↗

NASA NTRS2005

Exploring Diverse Data Sets and Developing New Theories and Ideas With Project Integration Architecture

The development of new ideas is the essence of scientific research. This is frequently done by developing models of physical processes and comparing model predictions with results from experiments. With models becoming ever more complex and data acquisition systems becoming more powerful, the researcher is burdened with wading through data ranging in volume up to a level of many terabytes and beyond. These data often come from multiple, heterogeneous sources and usually the methods for searching through it are at or near the manual level. In addition, current documentation methods are generally limited to researchers pen-and-paper style notebooks. Researchers may want to form constraint-based queries on a body of existing knowledge that is, itself, distributed over many different machines and environments and from the results of such queries then spawn additional queries, simulations, and data analyses in order to discover new insights into the problem being investigated. Currently, researchers are restricted to working within the boundaries of tools that are inefficient at probing current and legacy data to extend the knowledge of the problem at hand and reveal innovative and efficient solutions. A framework called the Project Integration Architecture is discussed that can address these desired functionalities.

Benyo, Theresa L.↗

NASA NTRS2011

An Efficient Monte Carlo Method for Modeling Radiative Transfer in Protoplanetary Disks

Monte Carlo methods have been shown to be effective and versatile in modeling radiative transfer processes to calculate model temperature profiles for protoplanetary disks. Temperatures profiles are important for connecting physical structure to observation and for understanding the conditions for planet formation and migration. However, certain areas of the disk such as the optically thick disk interior are under-sampled, or are of particular interest such as the snow line (where water vapor condenses into ice) and the area surrounding a protoplanet. To improve the sampling, photon packets can be preferentially scattered and reemitted toward the preferred locations at the cost of weighting packet energies to conserve the average energy flux. Here I report on the weighting schemes developed, how they can be applied to various models, and how they affect simulation mechanics and results. We find that improvements in sampling do not always imply similar improvements in temperature accuracies and calculation speeds.

radiative transfer↗

NASA NTRS2021

Future Climate Change Significantly Alters Interannual Wheat Yield Variability Over Half of Harvested Areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Climate change↗