Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computational performance”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Multidisciplinary Design Optimization of a Full Vehicle with High Performance Computing

Multidisciplinary design optimization (MDO) of a full vehicle under the constraints of crashworthiness, NVH (Noise, Vibration and Harshness), durability, and other performance attributes is one of the imperative goals for automotive industry. However, it is often infeasible due to the lack of computational resources, robust simulation capabilities, and efficient optimization methodologies. This paper intends to move closer towards that goal by using parallel computers for the intensive computation and combining different approximations for dissimilar analyses in the MDO process. The MDO process presented in this paper is an extension of the previous work reported by Sobieski et al. In addition to the roof crush, two full vehicle crash modes are added: full frontal impact and 50% frontal offset crash. Instead of using an adaptive polynomial response surface method, this paper employs a DOE/RSM method for exploring the design space and constructing highly nonlinear crash functions. Two NMO strategies are used and results are compared. This paper demonstrates that with high performance computing, a conventionally intractable real world full vehicle multidisciplinary optimization problem considering all performance attributes with large number of design variables become feasible.

Yang, R. J.↗
NASA NTRS1995

Design geometry and design/off-design performance computer codes for compressors and turbines

This report summarizes some NASA Lewis (i.e., government owned) computer codes capable of being used for airbreathing propulsion system studies to determine the design geometry and to predict the design/off-design performance of compressors and turbines. These are not CFD codes; velocity-diagram energy and continuity computations are performed fore and aft of the blade rows using meanline, spanline, or streamline analyses. Losses are provided by empirical methods. Both axial-flow and radial-flow configurations are included.

Glassman, Arthur J.↗
NASA NTRS1920

Slip-stream corrections performance computation

This report is an analysis of experiments performed by Eiffel on the air velocity in slip stream of a propeller, and also includes a theoretical discussion of the magnitude of the velocity in different propellers.

Warner, Edward P↗
NASA NTRS1994

Program Computes Performances Of Scramjets

SRGULL, a scramjet-cycle-analysis computer code, engineer's software tool capable of nose-to-tail simulation of hydrogen-fueled, scramjet engine integrated with air-frame. Simulated flow that of real gas with equilibrium thermodynamic properties. Program facilities initial estimates of performance of scramjet cycle by linking code for two-dimensional forebody, inlet, and nozzle with code for one-dimensional combustor. Five computer codes (SCRAM, SEAGUL, INLET, Program HUD, and GASH) integrated in program to provide capability for analysis of changing flow conditions. Written in FORTRAN 77.

Walton, J. T.↗
NASA NTRSDate not supplied

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shephard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing and Hazard Avoidance↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
NASA NTRS1972

A workload model and measures for computer performance evaluation

A generalized workload definition is presented which constructs measurable workloads of unit size from workload elements, called elementary processes. An elementary process makes almost exclusive use of one of the processors, CPU, I/O processor, etc., and is measured by the cost of its execution. Various kinds of user programs can be simulated by quantitative composition of elementary processes into a type. The character of the type is defined by the weights of its elementary processes and its structure by the amount and sequence of transitions between its elementary processes. A set of types is batched to a mix. Mixes of identical cost are considered as equivalent amounts of workload. These formalized descriptions of workloads allow investigators to compare the results of different studies quantitatively. Since workloads of different composition are assigned a unit of cost, these descriptions enable determination of cost effectiveness of different workloads on a machine. Subsequently performance parameters such as throughput rate, gain factor, internal and external delay factors are defined and used to demonstrate the effects of various workload attributes on the performance of a selected large scale computer system.

Kerner, H.↗
NASA NTRS1992

Users manual and modeling improvements for axial turbine design and performance computer code TD2-2

Computer code TD2 computes design point velocity diagrams and performance for multistage, multishaft, cooled or uncooled, axial flow turbines. This streamline analysis code was recently modified to upgrade modeling related to turbine cooling and to the internal loss correlation. These modifications are presented in this report along with descriptions of the code's expanded input and output. This report serves as the users manual for the upgraded code, which is named TD2-2.

Glassman, Arthur J.↗
NASA NTRS2018

Evaluating the Suitability of Commercial Clouds for NASA's High Performance Computing Applications: A Trade Study

NASA’s High-End Computing Capability (HECC) Project is periodically asked if it could be more cost effective through the use of commercial cloud resources. To answer the question, HECC’s Application Performance and Productivity (APP) team undertook a performance and cost evaluation comparing three domains: two commercial cloud providers, Amazon and Penguin, and HECC’s in-house resources—the Pleiades and Electra systems. In the study, the APP team used a combination of the NAS Parallel Benchmarks (NPB) and six full applications from NASA’s workload on Pleiades and Electra to compare performance of nodes based on three different generations of Intel Xeon processors—Haswell, Broadwell, and Skylake. Because of export control limitations, the most heavily used applications on Pleiades and Electra could not be used in the cloud; therefore, only one of the applications, OpenFOAM, represents work from the Aeronautics Research Mission Directorate and the Human and Exploration Mission Directorate. The other five applications are from the Science Mission Directorate.

High Performance↗
DOE OSTI2025

Classical Preoptimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications

The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work, we report the implementation and performance of ADAPT-VQE with our recently developed sparse wave function circuit solver (SWCS) in terms of accuracy and efficiency for molecular systems with up to 52 spin orbitals. The SWCS can be tuned to balance computational cost and accuracy, which extends the application of ADAPT-VQE for molecular electronic structure calculations to larger basis sets and a larger number of qubits. Using this tunable feature of the SWCS, we propose an alternative optimization procedure for ADAPT-VQE to reduce the computational cost of the optimization. Furthermore, by preoptimizing a quantum simulation with a parametrized ansatz generated with ADAPT-VQE/SWCS, we aim to utilize the power of classical high-performance computing in order to minimize the work required on noisy intermediate-scale quantum hardware, which offers a promising path toward demonstrating quantum advantage for chemical applications.

ADAPT-VQE↗
NASA NTRS1991

Supercomputing '91; Proceedings of the 4th Annual Conference on High Performance Computing, Albuquerque, NM, Nov. 18-22, 1991

Various papers on supercomputing are presented. The general topics addressed include: program analysis/data dependence, memory access, distributed memory code generation, numerical algorithms, supercomputer benchmarks, latency tolerance, parallel programming, applications, processor design, networks, performance tools, mapping and scheduling, characterization affecting performance, parallelism packaging, computing climate change, combinatorial algorithms, hardware and software performance issues, system issues. (No individual items are abstracted in this volume)

Source record↗
NASA NTRS2012

Investigating the Mobility of Light Autonomous Tracked Vehicles using a High Performance Computing Simulation Capability

This paper is concerned with the physics-based simulation of light tracked vehicles operating on rough deformable terrain. The focus is on small autonomous vehicles, which weigh less than 100 lb and move on deformable and rough terrain that is feature rich and no longer representable using a continuum approach. A scenario of interest is, for instance, the simulation of a reconnaissance mission for a high mobility lightweight robot where objects such as a boulder or a ditch that could otherwise be considered small for a truck or tank, become major obstacles that can impede the mobility of the light autonomous vehicle and negatively impact the success of its mission. Analyzing and gauging the mobility and performance of these light vehicles is accomplished through a modeling and simulation capability called Chrono::Engine. Chrono::Engine relies on parallel execution on Graphics Processing Unit (GPU) cards.

tracked vehicles↗
DOE OSTI2025

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

high-performance computing↗
NASA NTRS1981

Elliptical orbit performance computer program

A FORTRAN coded computer program which generates and plots elliptical orbit performance capability of space boosters for presentation purposes is described. Orbital performance capability of space boosters is typically presented as payload weight as a function of perigee and apogee altitudes. The parameters are derived from a parametric computer simulation of the booster flight which yields the payload weight as a function of velocity and altitude at insertion. The process of converting from velocity and altitude to apogee and perigee altitude and plotting the results as a function of payload weight is mechanized with the ELOPE program. The program theory, user instruction, input/output definitions, subroutine descriptions and detailed FORTRAN coding information are included.

Myler, T. R.↗