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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Design Concepts for an Optimized, Modular, and Scalable Liquid Amine Test Stand

The Air Revitalization Labs at NASA Ames Research Center (ARC) is investigating liquid amines to for their performance removing spacecraft carbon dioxide (CO2) for crewed space missions. Development of the current one-crew scale amine test stand stems from several modifications to a test stand initially built at NASA Johnson Space Center (JSC) This sub-scale system has been modified to incorporate a flow spreader for the inlet air stream, an updated contactor design for CO2 capture, a degasser to regenerate the liquid sorbent and recover the captured CO2, and a condenser to reduce water loss. Results from the existing integrated liquid amine test stand have provided valuable insight, but continued performance optimization requires design and build of a new integrated test system. New design concepts will institute a modular configuration to allow for interfacing with novel contactor and degasser architecture, parallel or series flow pathways for the air stream and liquid sorbent, and scaling up to a 4-crew CO2 removal system.

Tiago Faria Costa↗
DOE OSTI2026

Optimal experimental design using eigenvalue-based criteria with Pyomo.DoE

New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Object-Oriented Multi-Disciplinary Design, Analysis, and Optimization Tool

An Object-Oriented Optimization (O3) tool was developed that leverages existing tools and practices, and allows the easy integration and adoption of new state-of-the-art software. At the heart of the O3 tool is the Central Executive Module (CEM), which can integrate disparate software packages in a cross platform network environment so as to quickly perform optimization and design tasks in a cohesive, streamlined manner. This object-oriented framework can integrate the analysis codes for multiple disciplines instead of relying on one code to perform the analysis for all disciplines. The CEM was written in FORTRAN and the script commands for each performance index were submitted through the use of the FORTRAN Call System command. In this CEM, the user chooses an optimization methodology, defines objective and constraint functions from performance indices, and provides starting and side constraints for continuous as well as discrete design variables. The structural analysis modules such as computations of the structural weight, stress, deflection, buckling, and flutter and divergence speeds have been developed and incorporated into the O3 tool to build an object-oriented Multidisciplinary Design, Analysis, and Optimization (MDAO) tool.

Pak, Chan-gi↗
NASA NTRS1994

Performance of a 2-micrometer coherent Doppler lidar for wind measurements

Measurements of boundary layer winds are presented using a 2-micrometer coherent Doppler lidar and the optimal performance of the maximum likelihood estimator. The systematic error for single-shot estimates was estimated as 3.6 cm/s using measurements from a stationary hard target. The estimation error for measurements of the radial component of the wind field was determined, as well as the fraction of the estimates that are randomly distributed over the velocity search space, when the signal power is low and speckle fading is important. The results from actual data are compared with the results from ideal simulations. The first direct estimation of the spatial structure function of the radial wind field and of the energy dissipation rate is presented for both horizontal and vertical directions of propagation. The rms estimation error of the velocity estimates is found to be within 30% of ideal performance based on simulation.

Frehlich, Rod↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
NASA NTRS2012

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

Bole, Brian↗
DOE OSTI2024

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Ramôa, Mafalda (ORCID:0000000302187801)↗
NASA NTRS2000

Robust Control of Uncertain Systems via Dissipative LQG-Type Controllers

Optimal controller design is addressed for a class of linear, time-invariant systems which are dissipative with respect to a quadratic power function. The system matrices are assumed to be affine functions of uncertain parameters confined to a convex polytopic region in the parameter space. For such systems, a method is developed for designing a controller which is dissipative with respect to a given power function, and is simultaneously optimal in the linear-quadratic-Gaussian (LQG) sense. The resulting controller provides robust stability as well as optimal performance. Three important special cases, namely, passive, norm-bounded, and sector-bounded controllers, which are also LQG-optimal, are presented. The results give new methods for robust controller design in the presence of parametric uncertainties.

Joshi, Suresh M.↗
DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1983

Optimal design of gas adsorption refrigerators for cryogenic cooling

The design of gas adsorption refrigerators used for cryogenic cooling in the temperature range of 4K to 120K was examined. The functional relationships among the power requirement for the refrigerator, the system mass, the cycle time and the operating conditions were derived. It was found that the precool temperature, the temperature dependent heat capacities and thermal conductivities, and pressure and temperature variations in the compressors have important impacts on the cooling performance. Optimal designs based on a minimum power criterion were performed for four different gas adsorption refrigerators and a multistage system. It is concluded that the estimates of the power required and the system mass are within manageable limits in various spacecraft environments.

Chan, C. K.↗
NASA NTRS2013

SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community.We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures.In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction.

Pseudospectral (PS)↗
DOE OSTI2025

The Benefits and Weaknesses of Containerizing Software for HPC

Containerization technology has emerged as a transformative tool for software engineers, offering consistent development and deployment environments, simplifying dependency management, and enhancing scalability and portability across diverse systems. However, its application in High-Performance Computing (HPC) presents unique challenges, including the management of virtualization overhead, the need for efficient resource allocation, and the maintenance of optimal performance for compute-intensiv

Ho, Eric Victor [Sandia National Laboratories (SNL↗
NASA NTRS2006

Entropy Generation/Availability Energy Loss Analysis Inside MIT Gas Spring and "Two Space" Test Rigs

The results of the entropy generation and availability energy loss analysis under conditions of oscillating pressure and oscillating helium gas flow in two Massachusetts Institute of Technology (MIT) test rigs piston-cylinder and piston-cylinder-heat exchanger are presented. Two solution domains, the gas spring (single-space) in the piston-cylinder test rig and the gas spring + heat exchanger (two-space) in the piston-cylinder-heat exchanger test rig are of interest. Sage and CFD-ACE+ commercial numerical codes are used to obtain 1-D and 2-D computer models, respectively, of each of the two solution domains and to simulate the oscillating gas flow and heat transfer effects in these domains. Second law analysis is used to characterize the entropy generation and availability energy losses inside the two solution domains. Internal and external entropy generation and availability energy loss results predicted by Sage and CFD-ACE+ are compared. Thermodynamic loss analysis of simple systems such as the MIT test rigs are often useful to understand some important features of complex pattern forming processes in more complex systems like the Stirling engine. This study is aimed at improving numerical codes for the prediction of thermodynamic losses via the development of a loss post-processor. The incorporation of loss post-processors in Stirling engine numerical codes will facilitate Stirling engine performance optimization. Loss analysis using entropy-generation rates due to heat and fluid flow is a relatively new technique for assessing component performance. It offers a deep insight into the flow phenomena, allows a more exact calculation of losses than is possible with traditional means involving the application of loss correlations and provides an effective tool for improving component and overall system performance.

Ebiana, Asuquo B.↗
NASA NTRS1991

Space Transfer Vehicle Concepts and Requirements Study. Volume 2, Book 2: System and Program Requirements Trade Studies

During the 90-day study, support was provided to NASA in defining a point-of-departure space transfer vehicle (STV). The resulting STV concept was performance optimized with a two-stage LTV/LEV configuration. Appendix A reports on the effort during this period of the study. From the end of the 90-day study until the March Interim Review, effort was placed on optimizing the two-stage vehicle approach identified in the 90-day effort. After the March Interim Review, the effort was expanded to perform a full architectural trade study with the intent of developing a decision database to support STV system decisions in response to changing SEI infrastructure concepts. Several of the architecture trade studies were combined in a System Architecture Trade Study. In addition to this trade, system optimization/definition trades and analyses were completed and some special topics were addressed. Program- and system-level trade study and analyses methodologies and results are presented in this section. Trades and analyses covered in this section are: (1) a system architecture trade study; (2) evolution; (3) safety and abort considerations; (4) STV as a launch vehicle upper stage; and (5) optimum crew and cargo split.

Weber, Gary A.↗
NASA NTRS2014

Using SpF to Achieve Petascale for Legacy Pseudospectral Applications

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community. We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. Highlevel abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely inprocessor. The granularity of domain decomposition provided by SpF is only constrained by the datalocality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures. In this presentation, we will describe our experience in porting legacy pseudospectral models, MoSST and DYNAMO, to use SpF as well as present preliminary performance results provided by the improved scalability.

DYNAMO↗
DOE OSTI2024

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO 2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. Here, this study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Advancing the Performance of Anion Exchange Membrane Electrolysis by Employing a Powder-Based Ionomer during Anode Catalyst Layer Fabrication

The performance of anion exchange membrane water electrolysis (AEMWE) can be significantly improved by utilizing powdered ionomers during the fabrication of the anode catalyst layer (CL) to modify the CL properties. When comparing powdered ionomers to dispersed ionomers across various catalysts including cobalt oxide (Co 3 O 4 ), nickel−iron oxide (NiFe 2 O 4 ), and iridium oxide (IrO 2 ) the anode fabricated with powdered ionomers demonstrates improved performance in polarization curves, enhanced charge transfer kinetics, and reduced ohmic and transport losses, as evidenced by voltage breakdown and electrochemical impedance spectroscopy analyses. Optimal performance is achieved using a Co 3 O 4 catalyst with a 10 wt % powdered ionomer via the catalystcoated substrate method. Microscopy analyses reveal that electrodes formed with powdered ionomers during fabrication exhibit a more uniform catalyst and ionomer distribution, increased porosity with smaller pore areas, improved electronic conduction with less catalyst agglomeration isolated by a nonconductive ionomer, and enhanced interfacial contact with the membrane and transport layer. These findings highlight that ionomers in a powdered form can promote beneficial properties and are a promising approach to improving AEMWE efficiency.

08 HYDROGEN↗
DOE OSTI2024

Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network

In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.

Xia, Qianxue↗