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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A cell-centered AMR-ALE framework for 3D multi-material hydrodynamics. Part I: Lagrangian and indirect Euler AMR algorithms

Many applications of physics and engineering involve wide ranges of time and spatial scales. The numerical simulation of localized small scales such as shock waves and material interfaces requires a large number of computational cells in these regions. For these applications, Lagrangian and Arbitrary-Lagrangian-Eulerian (ALE) related methods are engaging since the moving mesh feature naturally brings mesh cells on shock discontinuities and material interfaces are carefully captured. In addition, Adaptive-Mesh-Refinement (AMR) strategies aim to optimize computational resources by concentrating finer mesh cells only in areas of interest while using coarser cells elsewhere. A key but challenging AMR requirement consists in efficiently distributing the computational effort to achieve high accuracy without the prohibitive computational costs associated with uniformly fine grids. Here, in this document, the coupling of the p4est AMR library with a cell-centered Lagrangian scheme is presented with the goal to perform reliable 3D Lagrangian-AMR and indirect Euler-AMR multi-material simulations. In particular, it is shown that starting from a 3D indirect ALE code, the memory management and load balancing requirements can be delegated to an external library (here the p4est library) to unlock ALE-AMR capabilities. First, we present a strategy to transcribe the octant-based connectivity of the 3D AMR framework with that of an unstructured mesh of polygonal cells used in Lagrangian hydrodynamics. Then, we show how refinement and coarsening operations must be adapted to the particular Lagrangian framework to ensure the conservation of volume during those steps. Finally, several numerical test cases are presented that demonstrate the capabilities of the Lagrangian-AMR and indirect Euler-AMR algorithms.

3D cell-centered Lagrangian numerical scheme↗
DOE OSTI2022

Interpreting the Operando X-ray Absorption Near-Edge Structure of Supported Cu and CuPd Clusters in Conditions of Oxidative Dehydrogenation of Propane: Dynamic Changes in Composition and Size

Supported subnano-cluster catalysts are highly dynamic, developing true active sites only under the pressures and temperatures of reaction conditions. Operando X-ray absorption near-edge structure (XANES) spectroscopy can track changes in the oxidation state and the local environment of cluster atoms, providing insight into the development of these active sites. While bulk metal, oxide, and hydroxide standards are often used for fitting experimental XANES spectra to obtain average oxidation states, we recently showed that computed cluster standards of relevant compositions are a more suitable basis, producing more accurate fits. Here, we theoretically interpret the operando XANES of supported Cu 3 Pd and Cu 4 clusters during temperature-programmed reaction (TPRx) of oxidative dehydrogenation of propane. We use an expanded basis set including both globally optimized computed clusters and bulk standards. Not only can we track reversible composition/oxidation state change with temperature, but also the irreversible growth of the bulk fraction upon heating, which we attribute to cluster sintering. This has important implications for the mechanism of the catalyzed reaction and the nature of the available active sites. Here, we propose that operando XANES provides most significant insight into the nature of supported cluster catalysts in reaction conditions when interpreted using mixed computed cluster and bulk standards.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗
NASA NTRS2015

Optimizing Integrated Arrival, Departure and Surface Operations Under Uncertainty

In airports and surrounding terminal airspaces, the integration of arrival, departure and surface scheduling and routing have the potential to improve the operations efficiency. Recent research had developed mixed-integer-linear programming algorithm-based scheduler for integrated arrival and departure operations in the presence of uncertainty. This paper extends to the surface previous research performed by the authors to integrate taxiway and runway operations. The developed algorithm is capable of computing optimal aircraft schedules and routings that reflects the integration of air and ground operations. A preliminary study case is conducted for a set of thirteen aircraft evolving in a model of the Los Angeles International airport and surrounding terminal areas. Using historical data, a representative traffic scenario is constructed and probabilistic distributions of pushback delay and arrival gate delay are obtained. To assess the benefits of optimization, a First- Come-First-Serve algorithm approach comparison is realized. Evaluation results demonstrate that the optimization can help identifying runway sequencing and schedule that reduce gate waiting time without increasing average taxi times.

Bosson, Christabelle↗
NASA NTRS2013

Flight Test Results of an Angle of Attack and Angle of Sideslip Calibration Method Using Output-Error Optimization

As part of a joint partnership between the NASA Aviation Safety Program (AvSP) and the University of Tennessee Space Institute (UTSI), research on advanced air data calibration methods has been in progress. This research was initiated to expand a novel pitot-static calibration method that was developed to allow rapid in-flight calibration for the NASA Airborne Subscale Transport Aircraft Research (AirSTAR) facility. This approach uses Global Positioning System (GPS) technology coupled with modern system identification methods that rapidly computes optimal pressure error models over a range of airspeed with defined confidence bounds. Subscale flight tests demonstrated small 2-σ error bounds with significant reduction in test time compared to other methods. Recent UTSI full scale flight tests have shown airspeed calibrations with the same accuracy or better as the Federal Aviation Administration (FAA) accepted GPS 'four-leg' method in a smaller test area and in less time. The current research was motivated by the desire to extend this method for inflight calibration of angle of attack (AOA) and angle of sideslip (AOS) flow vanes. An instrumented Piper Saratoga research aircraft from the UTSI was used to collect the flight test data and evaluate flight test maneuvers. Results showed that the output-error approach produces good results for flow vane calibration. In addition, maneuvers for pitot-static and flow vane calibration can be integrated to enable simultaneous and efficient testing of each system.

Siu, Marie-Michele↗
NASA NTRS1998

A Framework for a Supervisory Expert System for Robotic Manipulators with Joint-Position Limits and Joint-Rate Limits

This report addresses the problem of path planning and control of robotic manipulators which have joint-position limits and joint-rate limits. The manipulators move autonomously and carry out variable tasks in a dynamic, unstructured and cluttered environment. The issue considered is whether the robotic manipulator can achieve all its tasks, and if it cannot, the objective is to identify the closest achievable goal. This problem is formalized and systematically solved for generic manipulators by using inverse kinematics and forward kinematics. Inverse kinematics are employed to define the subspace, workspace and constrained workspace, which are then used to identify when a task is not achievable. The closest achievable goal is obtained by determining weights for an optimal control redistribution scheme. These weights are quantified by using forward kinematics. Conditions leading to joint rate limits are identified, in particular it is established that all generic manipulators have singularities at the boundary of their workspace, while some have loci of singularities inside their workspace. Once the manipulator singularity is identified the command redistribution scheme is used to compute the closest achievable Cartesian velocities. Two examples are used to illustrate the use of the algorithm: A three link planar manipulator and the Unimation Puma 560. Implementation of the derived algorithm is effected by using a supervisory expert system to check whether the desired goal lies in the constrained workspace and if not, to evoke the redistribution scheme which determines the constraint relaxation between end effector position and orientation, and then computes optimal gains.

Mutambara, Arthur G. O.↗
DOE OSTI2023

Design and additive manufacturing of optimized electrodes for energy storage applications

Supercapacitors exhibit fast charging/discharging ability and have attracted considerable attention within the automotive, aerospace, and telecommunication industries. Porous carbons, prized for their high electrical conductivity and high surface area, have been attractive candidates for supercapacitor electrodes. Moving to thick electrodes is one strategy to further increase energy density due to a higher volume fraction of active material. However, thick electrodes suffer from sluggish charged species transport, which is why thin electrodes are currently favored. In this work, we investigate the use of computational optimization and additive manufacturing to design and fabricate thick porous electrodes with improved performance. Electrode performance was maximized by designing their morphologies via topology optimization and printing by projection micro stereolithography (PμSL) using commercial resin (PR48). The PR48 resin was then pyrolyzed (PR48-P) to create the final conductive electrode. The optimized PR48-P electrodes exhibited 99% improvement in capacitance compared to control electrodes printed with cubic lattice morphologies. To further improve performance, we formulated a resin combining graphene oxide (GO) and trimethylolpropane triacrylate (TMPTA). Electrodes printed with 3 wt% GO in TMPTA exhibited improved capacitance retention after pyrolysis compared to the PR48-P electrodes. Finally, this work demonstrates the benefits of using topology optimization to design electrodes and material development to improve functional properties of 3D printable electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

GFCCLib: Scalable and efficient coupled-cluster Green's function library for accurately tackling many-body electronic structure problems

Coupled-cluster Green’s function (GFCC) calculation has drawn much attention in the recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. However, GFCC calculations on scientific computing clusters usually suffer from expensive higher di- mensional tensor contractions in the complex space, expensive inter-process communi- cation, and severe load imbalance, which limits it’s routine use for tackling electronic structure problems. Here we present a numerical library prototype that is specifically designed for large-scale GFCC calculations. The design of the library is focused on a systematically optimal computing strategy to improve its scalability and efficiency. The performance of the library is demonstrated by the relevant profiling analysis of running GFCC calculations on remote giant computing clusters. The capability of the library is highlighted by computing a wide near valence band of a fullerene C60 molecule for the first time at the GFCCSD level that shows excellent agreement with the experimental spectrum.

Peng, Bo↗
NASA NTRS1977

Low cost Ku-band earth terminals for voice/data/facsimile

A Ku-band satellite earth terminal capable of providing two way voice/facsimile teleconferencing, 128 Kbps data, telephone, and high-speed imagery services is proposed. Optimized terminal cost and configuration are presented as a function of FDMA and TDMA approaches to multiple access. The entire terminal from the antenna to microphones, speakers and facsimile equipment is considered. Component cost versus performance has been projected as a function of size of the procurement and predicted hardware innovations and production techniques through 1985. The lowest cost combinations of components has been determined in a computer optimization algorithm. The system requirements including terminal EIRP and G/T, satellite size, power per spacecraft transponder, satellite antenna characteristics, and link propagation outage were selected using a computerized system cost/performance optimization algorithm. System cost and terminal cost and performance requirements are presented as a function of the size of a nationwide U.S. network. Service costs are compared with typical conference travel costs to show the viability of the proposed terminal.

Kelley, R. L.↗
DOE OSTI2022

Dynamic, resilient sensing system for automatic cyber-attack neutralization

An industrial asset may have monitoring nodes that generate current monitoring node values. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing fault. A dynamic, resilient estimator constructs, using normal monitoring node values, a latent feature space (of lower dimensionality as compared to a temporal space) associated with latent features. The system also constructs, using normal monitoring node values, functions to project values into the latent feature space. Responsive to an indication that a node is currently being attacked or experiencing fault, the system may compute optimal values of the latent features to minimize a reconstruction error of the nodes not currently being attacked or experiencing a fault. The optimal values may then be projected back into the temporal space to provide estimated values and the current monitoring node values from the abnormal monitoring node are replaced with the estimated values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Network-Aware Distributed Energy Resource Aggregation Framework for Flexible, Cost-Optimal, and Resilient Operation

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems

Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.

08 HYDROGEN↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1974

Control system design using frequency domain models and parameter optimization, with application to supersonic inlet controls

A technique is described for designing feedback control systems using frequency domain models, a quadratic cost function, and a parameter optimization computer program. FORTRAN listings for the computer program are included. The approach is applied to the design of shock position controllers for a supersonic inlet. Deterministic or random system disturbances, and the presence of random measurement noise are considered. The cost function minimization is formulated in the time domain, but the problem solution is obtained using a frequency domain system description. A scaled and constrained conjugate gradient algorithm is used for the minimization. The approach to a supersonic inlet included the calculations of the optimal proportional-plus integral (PI) and proportional-plus-integral-plus-derivative controllers. A single-loop PI controller was the most desirable of the designs considered.

Seidel, R. C.↗
NASA NTRS1987

Strategies for concurrent processing of complex algorithms in data driven architectures

The results of ongoing research directed at developing a graph theoretical model for describing data and control flow associated with the execution of large grained algorithms in a spatial distributed computer environment is presented. This model is identified by the acronym ATAMM (Algorithm/Architecture Mapping Model). The purpose of such a model is to provide a basis for establishing rules for relating an algorithm to its execution in a multiprocessor environment. Specifications derived from the model lead directly to the description of a data flow architecture which is a consequence of the inherent behavior of the data and control flow described by the model. The purpose of the ATAMM based architecture is to optimize computational concurrency in the multiprocessor environment and to provide an analytical basis for performance evaluation. The ATAMM model and architecture specifications are demonstrated on a prototype system for concept validation.

Stoughton, John W.↗
NASA NTRSDate not supplied

Flight Test Design and Implementation for Airspace Independent Surveillance Through a Distributed Ground Based Sensor Network

The paper presents a system architecture for distributed sensing, networking and computing, its hardware implementation, and execution of initial flight experiments to validate theoretical findings. It induces development of distributed sensing requirements, framework, and architecture, development of distributed ground node hardware prototypes, integration of all nodes and testing of baseline functionalities, integration of in-house developed perception, migration and tracking software packages, establishing flight scenario and flyable path for a selected UAS, flying the air vehicle along the path, recording sensors measurements, pre-processing them and transferring the resulting data to an optimal computing center. It also addresses the challenges related to pre-flight hardware calibration, clock synchronization, sensor registration and establishing a communication network. Sensors data processing results demonstrate the functionality of the presented distributed architecture and satisfactory performance of the applied technologies.

Target tracking↗