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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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NASA NTRS1992

Protein Crystal Growth Activities on STS-42

The Protein Crystal Growth (PCG) middeck payload is currently manifested to fly on STS-42 in January 1992. This payload is a joint effort between NASA s Office of Commercial Programs (OCP) and Office of Space Science and Applications (OSSA). The PCG experiments are managed by the Center for Macromolecular Crystallography (CMC), a NASA Center for the Commercial Development of Space (CCDS) based at the University of Alabama at Birmingham (UAB). This is the eighth flight of a payload in the PCG program that is jointly sponsored by the OCP and the OSSA. The flight hardware for STS-42 includes six Vapor Diffusion Apparatus (VDA) trays stored in two Refrigerator/Incubator Modules (R/TM s). The VDA trays will simultaneously conduct 120 experiments involving 15 different protein compounds, four of which are sponsored by the OCP, the UAB CCDS, and four co-investigators.

Source record↗

DOE OSTI2025

Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation

The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using the Gauss-Newton method. The computation of the descent direction is expensive since it involves the solution of a large-scale and potentially ill-conditioned linear system, solved using the preconditioned conjugate gradient (PCG) method. Here, to address this cost, we efficiently construct scalable preconditioners using three different randomization techniques, which all rely on a certain low-rank structure involving the Gauss-Newton Hessian. The proposed techniques come with theoretical guarantees on the condition number, and at the same time, are amenable to parallelization. We also develop an adaptive approach to estimate the sketch size and choose between the reuse or recomputation of the preconditioner. We demonstrate the performance and effectiveness of our methodology on two representative model problems—the Burgers and barotropic vorticity equation—showing a drastic reduction in both the number of PCG iterations and the number of Gauss-Newton Hessian products after including the preconditioner construction cost.

Gauss-Newton↗

DOE OSTI2025

Leveraging operator learning to accelerate convergence of the preconditioned conjugate gradient method

We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient (PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specifically the Deep Operator Network (DeepONet). To this aim, we introduce two complementary approaches for assembling the deflation operators. The first approach approximates near-null space vectors of the discrete PDE operator using the basis functions learned by the DeepONet. The second approach directly leverages solutions predicted by the DeepONet. To further enhance convergence, we also propose several strategies for prescribing the sparsity pattern of the deflation operator. Here, a comprehensive set of numerical experiments encompassing steady-state, time-dependent, scalar, and vector-valued problems posed on both structured and unstructured geometries is presented and demonstrates the effectiveness of the proposed DeepONet-based deflated PCG method, as well as its generalization across a wide range of model parameters and problem resolutions.

Deflation↗

NASA NTRS1985

Protein crystal growth results from shuttle flight 51-F

The protein crystal growth (PCG) experiments run on 51-F were analyzed. It was found that: (1) sample stability is increased over that observed during the experiments on flight 51-D; (2) the dialysis experiments produced lysozyme crystals that were significantly larger than those obtained in our identical ground-based studies; (3) temperature fluctuations apparently caused problems during the crystallization experiments on 51-F; (4) it is indicated that teflon tape stabilizes droplets on the syringe tips; (5) samples survived during the reentry and landing in glass tips that were not stoppered with plungers; (6) from the ground-based studies, it was expected that equilibration should be complete within 2 to 4 days for all of these vapor-diffusion experiments, thus it appears that the vapor diffusion rates are somewhat slower under microgravity conditions; (7) drop tethering was highly successful, all four of the tethered drops were stable, even though they contained MPD solutions; (8) the PCG experiments on 51-F were done to assess the hardware and experimental procedures that are developed for future flights, when temperature control will be available. Lysozyme crystals obtained by microdialysis are considerably larger than those obtained on the ground, using the identical apparatus and procedures.

Bugg, C. E.↗

NASA NTRS1988

Error indicators and accuracy improvements of finite element solutions

Practical and reliable estimators of the discretization errors in engineering problems are developed. Error indicators for identifying the regions or elements of the solution domain which are likely to have the largest discretization errors are presented, and a simple computational procedure for improving the accuracy of the finite element solutions for shell problems is given. The similarities between the proposed procedure and a preconditioned conjugate gradient (PCG) technique are identified and exploited to generate pointwise error indicators from the PCG technique. Numerical examples in the linear static analysis of shells are presented.

Noor, Ahmed K.↗

NASA NTRS1992

Iterative methods for large scale static analysis of structures on a scalable multiprocessor supercomputer

A parallel Preconditioned Conjugate Gradient (PCG) iterative solver has been developed and implemented on the iPSC-860 scalable hypercube. This new implementation makes use of the Parallel Automated Runtime Toolkit at ICASE (PARTI) primitives to efficiently program irregular communications patterns that exist in general sparse matrices and in particular in the finite element sparse stiffness matrices. The iterative PCG has been used to solve the finite element equations that result from discretizing large scale aerospace structures. In particular, the static response of the High Speed Civil Transport (HSCT) finite element model is solved on the iPSC-860.

Sobh, Nahil Atef↗

NASA NTRS1991

Comparing direct and iterative equation solvers in a large structural analysis software system

Two direct Choleski equation solvers and two iterative preconditioned conjugate gradient (PCG) equation solvers used in a large structural analysis software system are described. The two direct solvers are implementations of the Choleski method for variable-band matrix storage and sparse matrix storage. The two iterative PCG solvers include the Jacobi conjugate gradient method and an incomplete Choleski conjugate gradient method. The performance of the direct and iterative solvers is compared by solving several representative structural analysis problems. Some key factors affecting the performance of the iterative solvers relative to the direct solvers are identified.

Poole, E. L.↗

NASA NTRS1990

On a concurrent element-by-element preconditioned conjugate gradient algorithm for multiple load cases

Element-by-element preconditioned conjugate gradient (EBE-PCG) algorithms have been advocated for use in parallel/vector processing environments as being superior to the conventional LDL(exp T) decomposition algorithm for single load cases. Although there may be some advantages in using such algorithms for a single load case, when it comes to situations involving multiple load cases, the LDL(exp T) decomposition algorithm would appear to be decidedly more cost-effective. The authors have outlined an EBE-PCG algorithm suitable for multiple load cases and compared its effectiveness to the highly efficient LDL(exp T) decomposition scheme. The proposed algorithm offers almost no advantages over the LDL(exp T) algorithm for the linear problems investigated on the Alliant FX/8. However, there may be some merit in the algorithm in solving nonlinear problems with load incrementation, but that remains to be investigated.

Watson, Brian↗

NASA NTRS1995

STS-67 Space Shuttle mission report

The STS-67 Space Shuttle Program Mission Report provides the results of the orbiter vehicle performance evaluation during this sixty-eighth flight of the Shuttle Program, the forty-third flight since the return to flight, and the eighth flight of the Orbiter vehicle Endeavour (OV-105). In addition, the report summarizes the payload activities and the performance of the External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engines (SSME). The serial numbers of the other elements of the flight vehicle were ET-69 for the ET; 2012, 2033, and 2031 for SSME's 1, 2, and 3, respectively; and Bl-071 for the SRB's. The left-hand RSRM was designated 360W043A, and the right-hand RSRM was designated 360L043B. The primary objective of this flight was to successfully perform the operations of the ultraviolet astronomy (ASTRO-2) payload. Secondary objectives of this flight were to complete the operations of the Protein Crystal Growth - Thermal Enclosure System (PCG-TES), the Protein Crystal Growth - Single Locker Thermal Enclosure System (PCG-STES), the Commercial Materials Dispersion Apparatus ITA Experiments (CMIX), the Shuttle Amateur Radio Experiment-2 (SAREX-2), the Middeck Active Control Experiment (MACE), and two Get-Away Special (GAS) payloads.

Fricke, Robert W., Jr.↗

NASA NTRS1995

STS-66 Space Shuttle mission report

The primary objective of this flight was to accomplish complementary science objectives by operating the Atmospheric Laboratory for Applications and Science-3 (ATLAS-3) and the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere-Shuttle Pallet Satellite (CRISTA-SPAS). The secondary objectives of this flight were to perform the operations of the Shuttle Solar Backscatter Ultraviolet/A (SSBUV/A) payload, the Experiment of the Sun Complementing the Atlas Payload and Education-II (ESCAPE-II) payload, the Physiological and Anatomical Rodent Experiment/National Institutes of Health Rodents (PARE/NIH-R) payload, the Protein Crystal Growth-Thermal Enclosure System (PCG-TES) payload, the Protein Crystal Growth-Single Locker Thermal Enclosure System (PCG-STES), the Space Tissue/National Institutes of Health Cells STL/N -A payload, the Space Acceleration Measurement Systems (SAMS) Experiment, and Heat Pipe Performance Experiment (HPPE) payload. The 11-day plus 2 contingency day STS-66 mission was flown as planned, with no contingency days used for weather avoidance or Orbiter contingency operations. Appendix A lists the sources of data from which this report was prepared, and Appendix B defines all acronyms and abbreviations used in the report.

Fricke, Robert W., Jr.↗

NASA NTRS1997

Gradient-Based Aerodynamic Shape Optimization Using ADI Method for Large-Scale Problems

A gradient-based shape optimization methodology, that is intended for practical three-dimensional aerodynamic applications, has been developed. It is based on the quasi-analytical sensitivities. The flow analysis is rendered by a fully implicit, finite volume formulation of the Euler equations.The aerodynamic sensitivity equation is solved using the alternating-direction-implicit (ADI) algorithm for memory efficiency. A flexible wing geometry model, that is based on surface parameterization and platform schedules, is utilized. The present methodology and its components have been tested via several comparisons. Initially, the flow analysis for for a wing is compared with those obtained using an unfactored, preconditioned conjugate gradient approach (PCG), and an extensively validated CFD code. Then, the sensitivities computed with the present method have been compared with those obtained using the finite-difference and the PCG approaches. Effects of grid refinement and convergence tolerance on the analysis and shape optimization have been explored. Finally the new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4. Despite the expected increase in the computational time, the results indicate that shape optimization, which require large numbers of grid points can be resolved with a gradient-based approach.

Pandya, Mohagna J.↗

NASA NTRS2002

Effects of Ordering Strategies and Programming Paradigms on Sparse Matrix Computations

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. For systems that are ill-conditioned, it is often necessary to use a preconditioning technique. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and ILU(O) preconditioned CG (PCG) using different programming paradigms and architectures. Results show that for this class of applications: ordering significantly improves overall performance on both distributed and distributed shared-memory systems, that cache reuse may be more important than reducing communication, that it is possible to achieve message-passing performance using shared-memory constructs through careful data ordering and distribution, and that a hybrid MPI+OpenMP paradigm increases programming complexity with little performance gains. A implementation of CG on the Cray MTA does not require special ordering or partitioning to obtain high efficiency and scalability, giving it a distinct advantage for adaptive applications; however, it shows limited scalability for PCG due to a lack of thread level parallelism.

Oliker, Leonid↗

DOE OSTI2025

A Scalable Interior‐Point Gauss–Newton Method for PDE‐Constrained Optimization With Bound Constraints

Here, we present a scalable approach to solve a class of partial differential equation (PDE)‐constrained optimization problems with bound constraints. This approach utilizes a robust full‐space interior‐point (IP)‐Gauss–Newton optimization method. To cope with the poorly‐conditioned IP‐Gauss–Newton saddle‐point linear systems that need to be solved approximately, once per optimization step, we propose two spectrally related preconditioners. These preconditioners leverage the limited informativeness of data in regularized PDE‐constrained optimization problems. A block Gauss–Seidel preconditioner is proposed for the GMRES‐based solution of the IP‐Gauss–Newton linear systems. It is shown, for a large‐class of PDE‐ and bound‐constrained optimization problems, that the spectrum of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix is asymptotically independent of discretization and is not impacted by the ill‐conditioning that notoriously plagues interior‐point methods. We exploit symmetry of the IP‐Gauss–Newton linear systems and propose a regularization and log‐barrier Hessian preconditioner for the preconditioned conjugate gradient (PCG)‐based solution of the equivalent IP‐Gauss–Newton–Schur complement linear systems. The eigenvalues of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix, that are not equal to one, are identical to the eigenvalues of the regularization and log‐barrier Hessian preconditioned Schur complement matrix. The scalability of the approach is demonstrated on two example problems. The numerical solution of these optimization problems is shown to require a discretization independent number of IP‐Gauss–Newton linear solves. Furthermore, the linear systems are solved in a discretization and IP ill‐conditioning independent number of preconditioned Krylov subspace iterations. The parallel scalability of the preconditioner, achieved via algebraic multigrid component solvers when applicable, and the aforementioned algorithmic scalability permits a parallel scalable means to compute solutions of a large class of PDE‐ and bound‐constrained problems.

PDE-constrained optimization↗

DOE OSTI2022

Biomass Production and Nutrient Removal by Perennial Energy Grasses Produced on a Wet Marginal Land

Growing dedicated bioenergy crops on marginal land can provide beneficial outcomes including biomass production and energy, resource management, and ecosystem services. We investigated the effects of harvest timing (peak standing crop [PEAK] or after killing frost [KF]) and nitrogen (N) fertilizer rates (0, 56, and 112 kg N ha -1 ) on yield, nutrient concentrations, and nutrient removal rates of perennial grasses on a wet marginal land. We evaluated three monocultures, including switchgrass (Panicum virgatum L., SW), Miscanthus x giganteus (MG), prairie cordgrass (Spartina pectinata Link, PCG), and a polyculture mixture of big bluestem (Andropogon gerardii Vitman), Indiangrass (Sorghastrum nutans (L.) Nash), and sideoats grama (Bouteloua curtipendula Torr., MIX). Increasing the application of N did correlate with increased biomass, concentration, and subsequent removal of nutrients across almost all treatment combinations. In all grass treatments except MG, PEAK harvesting increased yield and nutrient removal. At PEAK harvest, switchgrass is ideal for optimizing both biomass production and nutrient removal. While our results also suggest short-term plasticity for farmers when selecting harvest timing for optimal nutrient removal, KF harvest is recommended to ensure long-term stand longevity and adequate nutrient removal. If the KF harvest is adopted, MG would be the ideal option for optimizing biomass yield potential. Additionally, we found that the yield of polyculture did not vary much with harvest timing, suggesting better yield stability. Future studies should give consideration for long-term evaluation of polyculture mixtures to assess their biomass yields and nutrient removal capacities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Simulation toolkit for digital material characterization of large image-based microstructures

In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. Finally, the code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberForm TM , a carbon preform commonly used in thermal protection systems.

36 MATERIALS SCIENCE↗

NASA NTRS1977

Perception of binary acoustic events associated with the first heart sound

The resolving power of the auditory apparatus permits discrete vibrations associated with cardiac activity to be perceived as one or more events. Irrespective of the vibratory combinations recorded by conventional phonocardiography, in normal adults and in most adult patients auscultators tend to discriminate only two discrete events associated with the first heart sound S1. It is stressed that the heart sound S4 may be present when a binary acoustic event associated with S1 occurs in the sequence 'low pitched sound preceding high pitched sound', i.e., its components are perceived by auscultation as 'dull-sharp'. The question of S4 audibility arises in those individuals, normal and diseased, in whom the major components of S1 ought to be, at least clinically, at their customary high pitch and indeed on the PCG appear as high frequency oscillations. It is revealed that the apparent audibility of recorded S4 is not related to P-R interval, P-S4 interval, or relative amplitude of S4. The significant S4-LFC (low frequency component of S1) differences can be related to acoustic modification of the early component of S1.

Spodick, D. H.↗

NASA NTRS1989

Integrated structural analysis and design using 3-D finite elements

The optimum design of the shape of a hole in a plate in tension is obtained with a design algorithm based on partially converged analysis. The plate is modeled by three-dimensional finite elements, and an EBE PCG iterative solution algorithm is used for solving the equations of equilibrium. Several parametrizations of the optimum shape are considered, and it is found that a sine series can describe the optimum shape with only three design variables. The optimum shape compares well with an experimental optimum obtained by Durelli.

Barthelemy, B.↗

NASA NTRS1990

Concurrent Finite-Element Analysis On Hypercube Computers

Improved approach to execution of finite-element codes on hypercube and similar concurrent data processors increases efficiency of computation for many different types of problems. Based on flexible general model of computation on, and communication among, large-node parallel processors. Hybrid combining direct methods within subdomains and preconditioned-conjugate-gradient (PCG) iteration on remaining boundary system to obtain method of solution both robust and efficient. Preserves general structure and function of conventional sequential finite-element-method software. Generalized to both distributed- and shared-memory multicomputers, eliminating degree of machine specificity restricting general usefulness.

Lyzenga, G. A.↗