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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Optimal Recursive Digital Filters for Active Bending Stabilization

In the design of flight control systems for large flexible boosters, it is common practice to utilize active feedback control of the first lateral structural bending mode so as to suppress transients and reduce gust loading. Typically, active stabilization or phase stabilization is achieved by carefully shaping the loop transfer function in the frequency domain via the use of compensating filters combined with the frequency response characteristics of the nozzle/actuator system. In this paper we present a new approach for parameterizing and determining optimal low-order recursive linear digital filters so as to satisfy phase shaping constraints for bending and sloshing dynamics while simultaneously maximizing attenuation in other frequency bands of interest, e.g. near higher frequency parasitic structural modes. By parameterizing the filter directly in the z-plane with certain restrictions, the search space of candidate filter designs that satisfy the constraints is restricted to stable, minimum phase recursive low-pass filters with well-conditioned coefficients. Combined with optimal output feedback blending from multiple rate gyros, the present approach enables rapid and robust parametrization of autopilot bending filters to attain flight control performance objectives. Numerical results are presented that illustrate the application of the present technique to the development of rate gyro filters for an exploration-class multi-engined space launch vehicle.

Orr, Jeb S.↗
NASA NTRS1994

Portable parallel stochastic optimization for the design of aeropropulsion components

This report presents the results of Phase 1 research to develop a methodology for performing large-scale Multi-disciplinary Stochastic Optimization (MSO) for the design of aerospace systems ranging from aeropropulsion components to complete aircraft configurations. The current research recognizes that such design optimization problems are computationally expensive, and require the use of either massively parallel or multiple-processor computers. The methodology also recognizes that many operational and performance parameters are uncertain, and that uncertainty must be considered explicitly to achieve optimum performance and cost. The objective of this Phase 1 research was to initialize the development of an MSO methodology that is portable to a wide variety of hardware platforms, while achieving efficient, large-scale parallelism when multiple processors are available. The first effort in the project was a literature review of available computer hardware, as well as review of portable, parallel programming environments. The first effort was to implement the MSO methodology for a problem using the portable parallel programming language, Parallel Virtual Machine (PVM). The third and final effort was to demonstrate the example on a variety of computers, including a distributed-memory multiprocessor, a distributed-memory network of workstations, and a single-processor workstation. Results indicate the MSO methodology can be well-applied towards large-scale aerospace design problems. Nearly perfect linear speedup was demonstrated for computation of optimization sensitivity coefficients on both a 128-node distributed-memory multiprocessor (the Intel iPSC/860) and a network of workstations (speedups of almost 19 times achieved for 20 workstations). Very high parallel efficiencies (75 percent for 31 processors and 60 percent for 50 processors) were also achieved for computation of aerodynamic influence coefficients on the Intel. Finally, the multi-level parallelization strategy that will be needed for large-scale MSO problems was demonstrated to be highly efficient. The same parallel code instructions were used on both platforms, demonstrating portability. There are many applications for which MSO can be applied, including NASA's High-Speed-Civil Transport, and advanced propulsion systems. The use of MSO will reduce design and development time and testing costs dramatically.

Sues, Robert H.↗
NASA NTRS2015

Alternative to Nitric Acid Passivation

The Ground Systems Development and Operations (GSDO) Program at NASA John F. Kennedy Space Center (KSC), Florida, has the primary objective of modernizing and transforming the launch and range complex at KSC to benefit current and future NASA programs along with other emerging users. Described as the launch support and infrastructure modernization program in the NASA Authorization Act of 2010, the GSDO Program will develop and implement shared infrastructure and process improvements to provide more flexible, affordable, and responsive capabilities to a multi-user community. In support of NASA and the GSDO Program, the objective of this project is to qualify citric acid as an environmentally-preferable alternative to nitric acid for passivation of stainless steel alloys. This project is a direct follow-on to United Space Alliance (USA) work at KSC to optimize the parameters for the use of citric acid and verify effectiveness. This project will build off of the USA study to further evaluate citric acids effectiveness and suitability for corrosion protection of a number of stainless steels alloys used by NASA, the Department of Defense (DoD), and the European Space Agency (ESA).

stainless steel↗
NASA NTRS2016

SARDA Surface Schedulers

Provide an overview of algorithms used in SARDA (Spot and Runway Departure Advisor) HITL (Human-in-the-Loop) simulation for Dallas Fort-Worth International Airport and Charlotte Douglas International airport. Outline a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the single runway scheduling (SRS) problem, and discuss heuristics to restrict the search space for the DP based algorithm and provide improvements.

runways scheduling↗
DOE OSTI2022

maestro

MÆSTRO stands for Multi-fidelity Adaptive Ensemble Stochastic Trust Region Optimization and it is a plug n play derivate fee stochastic optimization solver. The problem being considered in MÆSTRO involves fitting Monte Carlo simulations that describe complex phenomena to experiments. This is done by finding parameters of the resource intensive and noisy simulation that yield the least squares objective function value to the noisy experimental data. This problem is solved using a stochastic trust-region optimization algorithm where in each iteration, a local approximation of the simulation signal and of the simulation noise is constructed over data, which is obtained by running the simulation at strategically placed design points within the trust-region around the current iterate. Then the simulation components of the objective are replaced by their approximations and this analytical and closed-form optimization problem is solved to find the next iterate within the trust-region. Then the trust region is moved and the iterations continue until a satisfactory convergence criteria is met.

KRISHNAMOORTHY, MOHAN↗
NASA NTRS2002

The Aeronautical Data Link: Taxonomy, Architectural Analysis, and Optimization

The future Communication, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) System will rely on global satellite navigation, and ground-based and satellite based communications via Multi-Protocol Networks (e.g. combined Aeronautical Telecommunications Network (ATN)/Internet Protocol (IP)) to bring about needed improvements in efficiency and safety of operations to meet increasing levels of air traffic. This paper will discuss the development of an approach that completely describes optimal data link architecture configuration and behavior to meet the multiple conflicting objectives of concurrent and different operations functions. The practical application of the approach enables the design and assessment of configurations relative to airspace operations phases. The approach includes a formal taxonomic classification, an architectural analysis methodology, and optimization techniques. The formal taxonomic classification provides a multidimensional correlation of data link performance with data link service, information protocol, spectrum, and technology mode; and to flight operations phase and environment. The architectural analysis methodology assesses the impact of a specific architecture configuration and behavior on the local ATM system performance. Deterministic and stochastic optimization techniques maximize architectural design effectiveness while addressing operational, technology, and policy constraints.

Morris, A. Terry↗
NASA NTRS2006

An Overview of the Role of Systems Analysis in NASA's Hypersonics Project

NASA's Aeronautics Research Mission Directorate recently restructured its Vehicle Systems Program, refocusing it towards understanding the fundamental physics that govern flight in all speed regimes. Now called the Fundamental Aeronautics Program, it is comprised of four new projects, Subsonic Fixed Wing, Subsonic Rotary Wing, Supersonics, and Hypersonics. The Aeronautics Research Mission Directorate has charged the Hypersonics Project with having a basic understanding of all systems that travel at hypersonic speeds within the Earth's and other planets atmospheres. This includes both powered and unpowered systems, such as re-entry vehicles and vehicles powered by rocket or airbreathing propulsion that cruise in and accelerate through the atmosphere. The primary objective of the Hypersonics Project is to develop physics-based predictive tools that enable the design, analysis and optimization of such systems. The Hypersonics Project charges the systems analysis discipline team with providing it the decision-making information it needs to properly guide research and technology development. Credible, rapid, and robust multi-disciplinary system analysis processes and design tools are required in order to generate this information. To this end, the principal challenges for the systems analysis team are the introduction of high fidelity physics into the analysis process and integration into a design environment, quantification of design uncertainty through the use of probabilistic methods, reduction in design cycle time, and the development and implementation of robust processes and tools enabling a wide design space and associated technology assessment capability. This paper will discuss the roles and responsibilities of the systems analysis discipline team within the Hypersonics Project as well as the tools, methods, processes, and approach that the team will undertake in order to perform its project designated functions.

Robinson, Jeffrey S.↗
DOE OSTI2023

Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.

Potnis, Abhishek↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
NASA NTRS1984

Model estimation and identification of manual controller objectives in complex tracking tasks

A methodology is presented for estimating the parameters in an optimal control structural model of the manual controller from experimental data on complex, multiinput/multioutput tracking tasks. Special attention is devoted to estimating the appropriate objective function for the task, as this is considered key in understanding the objectives and strategy of the manual controller. The technique is applied to data from single input/single output as well as multi input/multi outpuut experiments, and results discussed.

Schmidt, D. K.↗
NASA NTRS1996

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.

Abdi, Frank↗
DOE OSTI2020

Taking stock of national climate policies to evaluate implementation of the Paris Agreement

Many countries have implemented national climate policies to accomplish pledged Nationally Determined Contributions and to contribute to the temperature objectives of the Paris Agreement on climate change. In 2023, the global stocktake will assess the combined effort of countries. Here, based on a public policy database and a multi-model scenario analysis, we show that implementation of current policies leaves a median emission gap of 22.4 to 28.2 GtCO 2 eq by 2030 with the optimal pathways to implement the well below 2 °C and 1.5 °C Paris goals. If Nationally Determined Contributions would be fully implemented, this gap would be reduced by a third. Interestingly, the countries evaluated were found to not achieve their pledged contributions with implemented policies (implementation gap), or to have an ambition gap with optimal pathways towards well below 2 °C. This shows that all countries would need to accelerate the implementation of policies for renewable technologies, while efficiency improvements are especially important in emerging countries and fossilfuel- dependent countries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

IMPACT: Design of Integrated Multiphysics Producible Additive Components for Turbomachinery

The overall objective of the IMPACT program was to enable a dramatic reduction in design maturation time for an additive hot-section turbomachinery component through the following: • A fast crack-risk producibility surrogate model generated from machine learning applied to additive process simulation data generated via exascale computing, • Linking this surrogate model to multi-physics topology optimization (TO) to enable the creation of producible, near-optimal structural/thermal designs for additive hot-section components, • Maturing this toolset to reduce hot-section component design-for-manufacturing iterations by a large fraction, and eventually, • Using these tools to develop more efficient gas turbines in much shorter design cycle times.

42 ENGINEERING↗
NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗
DOE OSTI2023

Modeling and Rapid Prototyping of Integrated Transmission-Distribution OPF Formulations with PowerModelsITD.jl

Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between transmission and distribution networks will be imperative for the optimal operation of the power grid. However, coupling models and formulations between transmission and distribution is non-trivial, in particular due to the common practice of modeling transmission systems as single-phase, and distribution systems as multi-conductor phase-unbalanced. To enable the rapid prototyping of power flow formulations, in particular in the modeling of the boundary conditions between these two seemingly incompatible data models, we introduce PowerModelsITD.jl, a free, open-source toolkit written in Julia for integrated transmission-distribution (ITD) optimization that leverages mature optimization libraries from the InfrastructureModels.jl-ecosystem. The primary objective of the proposed framework is to provide baseline implementations of steady-state ITD optimization problems, while providing a common platform for the evaluation of emerging formulations and optimization problems. In this work, we introduce the nonlinear formulations currently supported in PowerModelsITD.jl, which include AC-polar, AC-rectangular, current-voltage, and a linear network transportation model. Results are validated using combinations of IEEE transmission and distribution networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Enabling Execution of a Legacy CFD Mini Application on Accelerators Using OpenMP

We describe the process and outcome of our efforts to port a legacy Fortran benchmark code to heterogeneous GPU-accelerated computing architectures using OpenMP. The benchmark code is one of the multi-zone NAS Parallel Benchmarks (NPB-MZ) called SP-MZ. This “mini-app” mimics the computation and data movement that is found in popular legacy and modern implicit computational fluid dynamics (CFD)solvers. Our objective was to examine how efficiently legacy Fortran codes can be ported to accelerators by leveraging OpenMP directives. We describe the development and optimization process and demonstrate the performance impact of various code modifications. We show select profiling results from the Nvidia nvvp profiler to help others diagnose and overcome performance issues in their own applications. We present results for two compute systems endowed with Nvidia V100 accelerators.

Ioannis Nompelis↗
NASA NTRS1993

Predesign of diamond turned refractive/diffractive elements for IR objectives

Diffractive lenses have arrived. Literally hundreds of papers have been published and technology impact reports have been written about the exciting addition of a new tool for the lens designer. Sophisticated computer programs have been developed to aid in the optimization of these diffractive phase profiles for a wide variety of applications. Now, several fabrication methods are being pursued to produce these diffractive elements economically. The best known process is the etching of a multi-level relief grating, known as binary optics. This process uses sets of computer generated lithographic masks. Another, more recently developed method is Dry Photopolymer Embossing (DPE). This replication process uses master holograms. And now, diamond turning is being applied for the machining of these elements. Diamond turning is especially well suited for infrared optics. As any process has advantages and limitations, so has diamond turning. These advantages and limitations are discussed and general guidelines are presented to aid the designer and systems engineer in the project predesign stage.

Riedl, Max J.↗
NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗