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At least 775 records · Page 43

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NASA NTRS1997

Efficient GO2/GH2 Injector Design: A NASA, Industry and University Cooperative Effort

Developing new propulsion components in the face of shrinking budgets presents a significant challenge. The technical, schedule and funding issues common to any design/development program are complicated by the ramifications of the continuing decrease in funding for the aerospace industry. As a result, new working arrangements are evolving in the rocket industry. This paper documents a successful NASA, industry, and university cooperative effort to design efficient high performance GO2/GH2 rocket injector elements in the current budget environment. The NASA Reusable Launch Vehicle (RLV) Program initially consisted of three vehicle/engine concepts targeted at achieving single stage to orbit. One of the Rocketdyne propulsion concepts, the RS 2100 engine, used a full-flow staged-combustion cycle. Therefore, the RS 2100 main injector would combust GO2/GH 2 propellants. Early in the design phase, but after budget levels and contractual arrangements had been set the limitations of the current gas/gas injector database were identified. Most of the relevant information was at least twenty years old. Designing high performance injectors to meet the RS 2100 requirements would require the database to be updated and significantly enhanced. However, there was no funding available to address the need for more data. NASA proposed a teaming arrangement to acquire the updated information without additional funds from the RLV Program. A determination of the types and amounts of data needed was made along with test facilities with capabilities to meet the data requirements, budget constraints, and schedule. After several iterations a program was finalized and a team established to satisfy the program goals. The Gas/Gas Injector Technology (GGIT) Program had the overall goal of increasing the ability of the rocket engine community to design efficient high-performance, durable gas/gas injectors relevant to RLV requirements. First, the program would provide Rocketdyne with data on preliminary gas/gas injector designs which would enable discrimination among candidate injector designs. Secondly, the program would enhance the national gas/gas database by obtaining high-quality data that increases the understanding of gas/gas injector physics and is suitable for computational fluid dynamics (CFD) code validation. The third program objective was to validate CFD codes for future gas/gas injector design in the RLV program.

Tucker, P. K.↗

DOE OSTI2026

Causality-respecting adaptive refinement for PINNs: enabling precise interface evolution in phase field modeling

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in dynamical systems, particularly those involving sharp moving boundaries with complex initial morphologies, remains a challenge. Here, this study introduces an approach combining residual-based adaptive refinement (RBAR) with causality-informed training to enhance the performance of PINNs in solving spatio-temporal PDEs. Our method employs a three-step iterative process: initial causality-based training, RBAR-guided domain refinement, and subsequent causality training on the refined mesh. Applied to the Allen-Cahn equation, a widely-used model in phase field simulations, our approach demonstrates significant improvements in solution accuracy and computational efficiency over traditional PINNs. Notably, we observe an ‘overshoot and relocate’ phenomenon in dynamic cases with complex morphologies, showcasing the method’s adaptive error correction capabilities. This synergistic interaction between RBAR and causality training enables accurate capture of interface evolution, even in challenging scenarios where traditional PINNs fail. Our framework not only resolves the limitations of uniform refinement strategies but also provides a generalizable methodology for solving a broad range of spatio-temporal PDEs. The enhanced performance of the RBAR–causality combined framework demonstrates its strong potential for advancing PINN-based modeling of physical systems characterized by complex, evolving interfaces.

Allen-Cahn equations↗

DOE OSTI2021

$\mathrm{REX}$: An analytical tool for reactor operating envelope expansion through fuel-clad thermo-mechanics

Here REX, an analytical reactor design tool for the identification and expansion of reactor operating envelopes, is introduced and detailed. Written in MATLAB, REX takes core geometries and axially discretized detector and depletion output from the Serpent neutron transport code and performs full-core, pin-specific thermal-hydraulic and thermo-mechanical calculations. REX identifies the operating envelopes of advanced, solid-fuel nuclear reactors on the basis of their fuel-cladding, fuel-gas, and coolant-cladding interfacial thermo-mechanics. It iteratively pushes coolant inlet temperatures and modifies fuel pin geometries to induce mechanical failure in their original cladding materials at the fullest extent of their fuel cycle lengths and permissible coolant inlet temperatures. It then attempts to expand their operating envelopes by determining the mechanical responses of alternative cladding materials under the same geometric conditions. The result of the REX calculation sequence is a set of 5-D variables describing the temperatures, pressures, geometries, and mechanics of the core as functions of assembly, fuel pin, axial zone, depletion step, and coolant inlet temperature for each candidate cladding material with the limiting fuel pin geometry for the original material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms

The computational capabilities of near-term quantum computers are limited by the noisy execution of gate operations and a limited number of physical qubits. Hybrid variational algorithms are well-suited to near-term quantum devices because they allow for a wide range of tradeoffs between the amount of quantum and classical resources used to solve a problem. This paper investigates tradeoffs available at both the algorithmic and hardware levels by studying a specific case – applying the Quantum Approximate Optimization Algorithm (QAOA) to instances of the Maximum Independent Set (MIS) problem. We consider three variants of the QAOA which offer different tradeoffs at the algorithmic level in terms of their required number of classical parameters, quantum gates, and iterations of classical optimization needed. Since MIS is a constrained combinatorial optimization problem, the QAOA must respect the problem constraints. This can be accomplished by using many multi-controlled gate operations which must be decomposed into gates executable by the target hardware. We study the tradeoffs available at this hardware level, combining the gate fidelities and decomposition efficiencies of different native gate sets into a single metric called the gate decomposition cost .

Tomesh, Teague↗

NASA NTRS2014

EVA Suit R and D for Performance Optimization

Designing a planetary suit is very complex and often requires difficult trade‐offs between performance, cost, mass, and system complexity. To verify that new suit designs meet requirements, full prototypes must be built and tested with human subjects. However, numerous design iterations will occur before the hardware meets those requirements. Traditional draw‐prototype‐test paradigms for R&D are prohibitively expensive with today's shrinking Government budgets. Personnel at NASA are developing modern simulation techniques which focus on human‐centric designs by creating virtual prototype simulations and fully adjustable physical prototypes of suit hardware. During the R&D design phase, these easily modifiable representations of an EVA suit's hard components will allow designers to think creatively and exhaust design possibilities before they build and test working prototypes with human subjects. It allows scientists to comprehensively benchmark current suit capabilities and limitations for existing suit sizes and sizes that do not exist. This is extremely advantageous and enables comprehensive design down‐selections to be made early in the design process, enables the use of human performance as design criteria, and enables designs to target specific populations

Cowley, Matthew S.↗

DOE OSTI2020

Multiscale formulation of frictional contact mechanics at the pore scale

Direct numerical simulation (DNS) yields the highest fidelity predictions of mechanical deformation at the pore scale, but is prohibitively expensive for analyzing large or many samples. Discrete element methods (DEM) are an efficient alternative, but are limited to granular media and incapable of estimating or controlling prediction errors. We present a pore-level multiscale method (PLMM) that approximates DNS efficiently and with controllable accuracy. We focus on the linear elastic response of a consolidated geologic porous medium with arbitrary microstructure, heterogeneous mineralogy, containing cracks or defects. PLMM decomposes the solid phase into non-overlapping subdomains, on which local basis functions are constructed. The bases are then coupled with a global interface problem that accounts for slip or stick contact conditions between the subdomains. PLMM produces an initial, but accurate, approximation to DNS that can be iteratively improved. It is amenable to parallelism and allows for different mesh, models, and physics in each subdomain. An algebraic interpretation of PLMM as a preconditioner is also presented to allow non-intrusive implementation into existing solvers. Lastly, this work extends previous developments of PLMM in fluid dynamics to solid mechanics and enables future extensions towards modeling coupled flow and mechanics problems.

42 ENGINEERING↗

DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗

NASA NTRS2009

Progressive Classification Using Support Vector Machines

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Wagstaff, Kiri↗

NASA NTRS1977

Investigation and evaluation of shuttle/GPS navigation system

Iterative procedures were used to analyze the performance of two preliminary shuttle/GPS navigation system configurations: an early OFT experimental system and a more sophisticated system which consolidates several separate navigation functions thus permitting net cost savings from decreased shuttle avionics weight and power consumption, and from reduced ground data processing. The GPS system can provide on-orbit navigation accuracy an order of magnitude better than the baseline system, with very adequate link margins. The worst-case link margin is 4.3 dB. This link margin accounts for shuttle RF circuit losses which were minimized under the constraints of program schedule and environmental limitations. Implicit in the link analyses are the location trade-offs for preamplifiers and antennas.

Nilsen, P. W.↗

NASA NTRS1979

DONBOL: A computer program for predicting axisymmetric nozzle afterbody pressure distributions and drag at subsonic speeds

A Neumann solution for inviscid external flow was coupled to a modified Reshotko-Tucker integral boundary-layer technique, the control volume method of Presz for calculating flow in the separated region, and an inviscid one-dimensional solution for the jet exhaust flow in order to predict axisymmetric nozzle afterbody pressure distributions and drag. The viscous and inviscid flows are solved iteratively until convergence is obtained. A computer algorithm of this procedure was written and is called DONBOL. A description of the computer program and a guide to its use is given. Comparisons of the predictions of this method with experiments show that the method accurately predicts the pressure distributions of boattail afterbodies which have the jet exhaust flow simulated by solid bodies. For nozzle configurations which have the jet exhaust simulated by high-pressure air, the present method significantly underpredicts the magnitude of nozzle pressure drag. This deficiency results because the method neglects the effects of jet plume entrainment. This method is limited to subsonic free-stream Mach numbers below that for which the flow over the body of revolution becomes sonic.

Putnam, L. E.↗

NASA NTRS2003

Protein Crystal Growth With the Aid of Microfluidics

Protein crystallography is one of three well-known methods to obtain the structure of proteins. A major rate limiting step in protein crystallography is protein crystal nucleation and growth, which is still largely a process conducted by trial-and-error methods. Many attempts have been made to improve protein crystal growth by performing growth in microgravity. Although the use of microgravity appears to improve crystal quality in some attempts, this method has been inefficient because several reasons: we lack a fundamental understanding of macromolecular crystal growth in general and of the influence of microgravity in particular, we have to start with crystal growth conditions in microgravity based on conditions on the ground and finally the hardware does not allow for experimental iteration without reloading samples on the ground. To partially accommodate the disadvantages of the current hardware, we have used microfluidic technology (Lab-on-a-Chip devices) to design the concept of a more efficient crystallization device, suitable for use on the International Space Station and in high-throughput applications on the ground. The concept and properties of microfluidics, the application design process, and the advances in protein crystal growth hardware will be discussed in this presentation. Some examples of proteins crystallized in the new hardware will be discussed, including the differences between conventional crystallization versus crystallization in microfluidics.

vanderWoerd, Mark↗

NASA NTRS2013

Extended Analytic Device Optimization Employing Asymptotic Expansion

Analytic optimization of a thermoelectric junction often introduces several simplifying assumptionsincluding constant material properties, fixed known hot and cold shoe temperatures, and thermallyinsulated leg sides. In fact all of these simplifications will have an effect on device performance,ranging from negligible to significant depending on conditions. Numerical methods, such as FiniteElement Analysis or iterative techniques, are often used to perform more detailed analysis andaccount for these simplifications. While numerical methods may stand as a suitable solution scheme,they are weak in gaining physical understanding and only serve to optimize through iterativesearching techniques. Analytic and asymptotic expansion techniques can be used to solve thegoverning system of thermoelectric differential equations with fewer or less severe assumptionsthan the classic case. Analytic methods can provide meaningful closed form solutions and generatebetter physical understanding of the conditions for when simplifying assumptions may be valid.In obtaining the analytic solutions a set of dimensionless parameters, which characterize allthermoelectric couples, is formulated and provide the limiting cases for validating assumptions.Presentation includes optimization of both classic rectangular couples as well as practically andtheoretically interesting cylindrical couples using optimization parameters physically meaningful toa cylindrical couple. Solutions incorporate the physical behavior for i) thermal resistance of hot andcold shoes, ii) variable material properties with temperature, and iii) lateral heat transfer through legsides.

Thermoelectric Generators↗

DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗

DOE OSTI2025

Global stellarator coil optimization with quadratic constraints and objectives

Most present stellarator designs are produced by costly two-stage optimization: the first for an optimized equilibrium, and the second for a coil design reproducing its magnetic configuration. Few proxies for coil complexity and forces exist at the equilibrium stage. Rapid initial state finding for both stages is a topic of active research. Most present convex coil optimization codes use the least square winding surface method by Merkel (NESCOIL), with recent improvements in conditioning, regularization, sparsity, and physics objectives. While elegant, the method is limited to modeling the norms of linear functions in coil current. We present QUADCOIL, a global coil optimization method that targets combinations of linear and quadratic functions of the current. It can directly constrain and/or minimize a wide range of physics objectives unavailable in NESCOIL and REGCOIL, including the Lorentz force, magnetic energy, curvature, field-current alignment, and the maximum density of a dipole array. QUADCOIL requires no initial guess and runs nearly $10$ 2 x faster than filament optimization. Integrating it in the equilibrium optimization stage can potentially exclude equilibria with difficult-to-design coils, without significantly increasing the computation time per iteration. QUADCOIL finds the exact, global minimum in a large parameter space when possible, and otherwise finds a well-performing approximate global minimum. It supports most regularization techniques developed for NESCOIL and REGCOIL. We demonstrate QUADCOIL’s effectiveness in coil topology control, minimizing non-convex penalties, and predicting filament coil complexity with three numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

Application of a Rapid Design Tool to a 3D Woven Structural Joint

The optimization of composite structural joints is an iterative process and a multiscale problem. High fidelity finite element modeling of joints with 3D woven and laminated materials can become computationally expensive. The aim of this paper is to establish a reliable analysis process for the optimization of a composite Y-joint (curved Pi-joint), to be used in an aircraft fuselage, using commercial rapid joint design, analysis, and optimization software. The rapid joint design tool was investigated as a substitute and/or complement to a finite element analysis software. A composite Pi-joint and a composite Y-joint were evaluated using the rapid joint design tool to determine the applicability and limits of the software. Furthermore, three main preliminary parametric studies were performed to better understand the capability of the tool in predicting the stress distributions and trends in the Y-joint. The parameters investigated were the joint curvature, the laminated skin thickness, the adhesive systems, and the ply composition. Lastly, trends in predicted failure load were produced as a function of skin thickness (16ply, 24ply and 32ply). Failure loads were found with varying joint curvature and skin thickness using stress-based adherend failure criterion. The rapid joint design tool was also validated against existing experimental results.

Bonded Joints↗

DOE OSTI2022

Linking the singularities of cosmological correlators

Much of the structure of cosmological correlators is controlled by their singularities, which in turn are fixed in terms of flat-space scattering amplitudes. An important challenge is to interpolate between the singular limits to determine the full correlators at arbitrary kinematics. This is particularly relevant because the singularities of correlators are not directly observable, but can only be accessed by analytic continuation. In this paper, we study rational correlators — including those of gauge fields, gravitons, and the inflaton — whose only singularities at tree level are poles and whose behavior away from these poles is strongly constrained by unitarity and locality. We describe how unitarity translates into a set of cutting rules that consistent correlators must satisfy, and explain how this can be used to bootstrap correlators given information about their singularities. We also derive recursion relations that allow the iterative construction of more complicated correlators from simpler building blocks. In flat space, all energy singularities are simple poles, so that the combination of unitarity constraints and recursion relations provides an efficient way to bootstrap the full correlators. In many cases, these flat-space correlators can then be transformed into their more complex de Sitter counterparts. As an example of this procedure, we derive the correlator associated to graviton Compton scattering in de Sitter space, though the methods are much more widely applicable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Consistent inclusion of triple substitutions within a coupled cluster based static quantum embedding theory

We have previously proposed the MPCC static embedding framework for quantum chemistry that self-consistently couples a high-level coupled cluster (CC) treatment of the fragment (active region) with a lower level, Møller–Plesset perturbation treatment of the environment. Our initial implementation was limited to single and double (SD) substitutions, with CCSD for the fragment and first-order perturbative SD amplitudes for the environment. Here, we extend the MPCC embedding treatment to triple substitutions, which is essential for achieving chemical accuracy in energy differences. To this end, we employ a CCSDT solver for the fragment subsystem. For the environment subsystem, we construct a perturbative estimate of the triples amplitudes, explicitly accounting for feedback from all fragment amplitudes. The resulting approach is denoted MPCCSDT(pt). We further introduce a more complete formulation in which feedback from the environment amplitudes to the fragment amplitudes is also included. This scheme involves an iterative treatment of the environment triples amplitudes and is denoted MPCCSDT(it). In addition, we assess the accuracy of the previously proposed low-level method by introducing a modified low-level approach that incorporates a lowest-order treatment of selected long-range effects, including spin fluctuations and charge polarization. All resulting approaches may be viewed as post-CCSD(T) methods. We therefore consider test cases for which CCSD(T) exhibits substantial deviations from CCSDT. These include (i) single- and triple-bond stretching in F 2 and N 2 , (ii) bond dissociation energies of selected molecules from the W4-11 dataset, and (iii) total atomization energies of transition metal hydrides. Our results demonstrate that inclusion of triples amplitudes at the fragment level alone is insufficient; a perturbative treatment of the environment triples amplitudes is required. For many energy-difference applications, feedback from the environment triples amplitudes to the fragment amplitudes is not essential, but it does play a role in the very challenging CoH and FeH molecules. A very interesting finding from our study is that in some challenging cases, we need an improved (second-order) perturbative method for the SD amplitudes, going beyond the first-order one used in our earlier work. In conclusion, considering both cost and accuracy, the MP2CCSDT(pt) model is the most promising for future applications among the candidates considered here.

Shee, Avijit [University of California, Berkeley, ↗

DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗