SEARCH · Search NASA

Results for “Design Space Exploration”

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.

SourceResearch connectionFrom yearThrough year

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

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Extending High-Level Synthesis with AI/ML Methods

Artificial Intelligence (AI) and Machine Learning (ML) methods provide significant opportunities of improving quality of results when performing high-level synthesis (HLS). For example, they can be used to model and predict metrics of the final design (e.g., area, considering aspects such as interconnect overhead for different device technologies), facilitating exploration when searching for the best design trade-offs. They can also enable identifying hidden correlations across the various phases of the synthesis and the various optimizations performed, identifying the most effective pipelines. Finally, in more general terms, bio-inspired heuristic algorithms can improve the design space exploration for the synthesis process in terms of time and quality of the result. This paper discusses opportunities and challenges to augment HLS with AI/ML using as example flow the SODA Synthesizer, an open-source hardware generation toolchain which includes SODA-OPT, a hardware/software partitioning and pre-optimization tool developed with the MLIR framework, and PandA-Bambu, a state-of-the art HLS tool. SODA interfaces with OpenROAD to provide a complete end-to-end toolchain.

artificial intelligence↗

DOE OSTI2024

A novel design optimization framework to sustain remanufacturability

The ever-increasing global carbon emissions have urged the need for environmentally conscious/sustainable product design, for which the design for remanufacturing (DfRem) is one potential approach. DfRem targets at designing products that have multiple life cycles, thus significantly reducing raw material usage, energy consumption, and carbon emissions. In this paper, we develop a three-stage framework that consists of (1) systematic design space exploration and a multi-objective optimization formulation to minimize the likelihood of failure causes (such as fatigue and wear) and environmental footprint, (2) topology optimization to further reduce material usage without significantly affecting the load-carrying capability of the product, and (3) post-topology optimization design verification to ensure the proposed design satisfies all design constraints. The environmental impact can be assessed at varying comprehensiveness levels (e.g., design and manufacturing phase, use phase) and in terms of carbon or GHG emission, energy use, and waste generation. Because the novel design framework predominantly adjusted the geometry, we focused on mass-based change and energy savings due to sustained remanufacturability. The multi-objective optimization formulation in the first step results in a Pareto optimal set of possible design solutions that the designer can use for the second step. Finally, we demonstrate the utility of this framework through a case study of an engine cylinder head subjected to thermo-mechanical loads, where we find that about 5% of the product mass can be conserved with only about a 3% increase in surface area that has a fatigue life less than 10,000 cycles.

42 ENGINEERING↗

NASA NTRSDate not supplied

GASP Advancements With Symbolic Computations, Optimization, and Decoupled Numerical Methods

NASA is exploring advancements in hybrid-electric propulsion concepts for aircraft design. A new analysis capability has been deployed at NASA’s Ames Research Center using a new mathematical modeling framework in Python that leverages symbolic representation of mathematical expressions, optimization-based problem formulations, and standalone numerical methods. This capability, called Gascon, has been verified against NASA’s General Aviation Synthesis Program (GASP) results for several vehicles that exercise the analysis capability for a variety of aircraft classes and engine models, including both turbofans and turboprops. We believe this capability will set the stage for more rapid development of novel aircraft models with more flexible design assessments using optimization, and more traceable analysis for reproducibility. Additionally, a novel Pareto optimization-based design space exploration that shows the trade between range flown and fuel burn by manipulating the thrust split during cruise for a true parallel hybrid aircraft was developed by leveraging the unique features of Gascon.

aircraft design↗

DOE OSTI2026

An Integrated Framework for Memory-Centric Analysis: From Trace Collection to Co-Design

The memory wall phenomenon—where advances in processor performance significantly outpace those in memory subsystems—poses a fundamental challenge for contemporary computing systems. In memory-bound applications, memory subsystem behavior dominates performance, yet existing analysis approaches present significant limitations: detailed microarchitectural simulators require days to weeks to simulate modest workloads; hardware performance counters provide only aggregate statistics that obscure temporal and spatial access patterns; and scaled simulation approaches face challenges in capturing certain behaviors that emerge at larger scales. These limitations reflect a processor-centric design philosophy increasingly misaligned with memory-bound workloads where detailed understanding of memory access patterns, cache hierarchy interactions, and contention is critical for effective optimization. This paper presents an integrated framework for memory-centric analysis that enables effective hardware-software co-design. We describe practical trace collection techniques, including hardware-assisted processor tracing with minimal overhead and portable software-based instrumentation with statistical sampling. We present multi-perspective analysis methods that examine memory behavior from temporal, sequential, spatial, and relational viewpoints, revealing distinct optimization opportunities invisible in aggregate metrics. We detail an architectural modeling framework that uses sampled traces with temporal interpolation and confidence-based filtering to evaluate cache and memory configurations. Evaluation on representative benchmarks demonstrates that this framework achieves practical accuracy (L2 cache errors of 2.64\%, confidence-filtered L3 errors of 9.92\%, bandwidth errors of 7.33\%) while providing substantial speedup (26.8×) over cycle-accurate simulation, enabling rapid design space exploration. We demonstrate how this integrated framework enables systematic identification of both hardware optimizations (memory controller tuning, bank partitioning, NUMA configuration) and software optimizations (data layout restructuring, prefetching strategies, memory-aware scheduling). Through this comprehensive treatment of the memory-centric analysis pipeline—from trace collection through architectural modeling to co-design application—we provide researchers and practitioners with practical techniques for addressing memory bottlenecks in contemporary computing systems.

Gajaria, Dhruv Mayur↗

DOE OSTI2025

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

In this work, we performed an initial design space exploration of an accelerated processing unit (APU)—a hybrid CPU+GPU architecture that integrates both compute units (CUs) and memory into a unified system. This integration aims to reduce data movement, enhance memory locality, and improve energy efficiency by enabling the CPU and GPU to share memory directly. This effort focused on the interplay of key design components—cache line size, the number of CUs, and main memory technology—and the trade-offs of each configuration were analyzed. This paper highlights the various configurations’ impact on memory accesses, data reuse, and power utilization. The results provide valuable insights that can be leveraged to optimize APU architectures for high-performance and energy-efficient computing and thus create a balanced architecture. This optimization can be achieved by adopting dynamic cache management, runtime CU scaling, and advanced memory integration, highlighting the potential of APUs to address critical challenges in compute, data movement, and memory power consumption.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗

NASA NTRS2005

Innovative Robot Archetypes for In-Space Construction and Maintenance

The space environment presents unique challenges and opportunities in the assembly, inspection and maintenance of orbital and transit spaceflight systems. While conventional Extra-Vehicular Activity (EVA) technology, out of necessity, addresses each of the challenges, relatively few of the opportunities have been exploited due to crew safety and reliability considerations. Extra-Vehicular Robotics (EVR) is one of the least-explored design spaces but offers many exciting innovations transcending the crane-like Space Shuttle and International Space Station Remote Manipulator System (RMS) robots used for berthing, coarse positioning and stabilization. Microgravity environments can support new robotic archetypes with locomotion and manipulation capabilities analogous to undersea creatures. Such diversification could enable the next generation of space science platforms and vehicles that are too large and fragile to launch and deploy as self-contained payloads. Sinuous manipulators for minimally invasive inspection and repair in confined spaces, soft-stepping climbers with expansive leg reach envelopes and free-flying nanosatellite cameras can access EVA worksites generally not accessible to humans in spacesuits. These and other novel robotic archetypes are presented along with functionality concepts

Rehnmark, Fredrik↗

NASA NTRS2012

Environmental Controls and Life Support System Design for a Space Exploration Vehicle

Engineers at Johnson Space Center (JSC) are developing an Environmental Control and Life Support System (ECLSS) design for the Space Exploration Vehicle (SEV). The SEV will aid to expand the human exploration envelope for Geostationary Transfer Orbit (GEO), Near Earth Object (NEO), or planetary missions by using pressurized surface exploration vehicles. The SEV, formerly known as the Lunar Electric Rover (LER), will be an evolutionary design starting as a ground test prototype where technologies for various systems will be tested and evolve into a flight vehicle. This paper will discuss the current SEV ECLSS design, any work contributed toward the development of the ECLSS design, and the plan to advance the ECLSS design based on the SEV vehicle and system needs.

Stambaugh, Imelda C.↗

NASA NTRS2010

Environmental Controls and Life Support System (ECLSS) Design for a Space Exploration Vehicle (SEV)

Stambaugh, Imelda↗

NASA NTRS2018

Integrated Optimization of Mars Hybrid Solar-Electric/Chemical Propulsion Trajectories

NASA’s Human Exploration and Operation Mission Directorate is developing a reusable hybrid transportation architecture in which both chemical and solar-electric propulsion systems are used to deliver crew and cargo to the Martian sphere of influence. By combining chemical and solar-electric propulsions into a single spacecraft and applying each where it is the most effective, the hybrid architecture enables a series of Mars trajectories that are more fuel efficient than an all chemical propulsion architecture without significant increase to trip time. Solving the complex problem of low-thrust trajectory optimization coupled with the vehicle sizing requires development of an integrated trajectory analysis frame- work. Previous studies have utilized a more segmented optimization framework due to the limitation of the tools available. A new integrated optimization framework was recently developed to address the deficiencies of the previous methods that enables higher fidelity analysis to be performed and increases the efficiency of large design space explorations.

Chai, Patrick R.↗

NASA NTRSDate not supplied

Parametric Modeling and Mission Performance Analysis of a True Parallel Hybrid Turboprop Aircraft for Freighter Operations

Hybrid-electric propulsion systems for short-haul, cargo carrying aircraft have emerged as promising solutions for an environmentally sustainable future for commercial aviation. The novel propulsion architecture presents significant complexity and requires the development of new methodologies to account for the unique coupling and integration between critical design variables. This paper presents a comprehensive study on the modeling, performance assessment, and design space exploration of a C-130H freighter aircraft retrofitted with a parallel hybrid electric powertrain. Details on the development of parametric models for both the baseline and true parallel hybrid (TPH) aircraft and the integrated electrified aircraft propulsion (EAP) system sizing approach are presented along with detailed performance analyses of payload/range capabilities for short-haul cargo missions. For 2030, 2040, and 2050 EAP technology levels, the TPH C-130H configuration with a ~2.12 MW class EAP system has a range capability of 485-1,028 nautical miles and block fuel savings of 27-44%.

efficiency↗

DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗

DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗

DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Surrogate Model Integration with MOOSE XFEM for Creep Crack Growth

Ferritic-martensitic steels are key structural materials for advanced reactors but experience time-dependent deformation and damage under prolonged high temperature and irradiation, leading to creep-driven crack initiation and growth. High-fidelity models—crystal plasticity with irradiation mechanisms, phase-field for microstructural evolution, and continuum-damage viscoplasticity—capture the underlying physics but are too computationally intensive for broad design-space exploration and uncertainty quantification. This milestone advances a scalable alternative by integrating a microstructure-sensitive surrogate creep model into the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework and extending it to fracture via the extended finite element method (XFEM). The surrogate model, developed with collaborators at Sandia and Los Alamos National Laboratories, maps relevant microstructural descriptors to the viscoplastic response of HT9. We embed this surrogate within a coupled deformation-damage workflow in MOOSE/XFEM to simulate creep-driven crack initiation and propagation. Implementation enhancements include updates to the material interface, a plastic correction phase involving microstructure evolution, and fracture criteria to ensure numerical robustness and compatibility with the surrogate structure. Demonstrations on canonical creep benchmarks spanning uniaxial and multiaxial states show that the surrogate reproduces key trends of high-fidelity models while substantially reducing computational cost. The resulting capability bridges physics fidelity and performance, providing a practical path to a predictive, microstructure-aware assessment of creep and fracture in reactor materials.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware

Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-based approaches often have delays that are too long to be applied in practice. It is paramount to have systems with extremely low delays to quickly and with high fidelity adjust quantum hardware settings, where fidelity is defined as overlap with a target quantum state. Here, we utilize machine learning (ML) models to determine control-pulse parameters for preparing Selective Number-dependent Arbitrary Phase (SNAP) gates in microwave cavity qudits, which are multi-level quantum systems that serve as elementary computation units for quantum computing. The methodology involves data generation using classical optimization techniques, ML model development, design space exploration, and quantization for hardware implementation. Our results demonstrate the efficacy of the proposed approach, with optimized models achieving low gate trace infidelity near $10^{-3}$ and efficient utilization of programmable logic resources.

Sanders, Flor [Columbia U.]↗

NASA NTRS1995

Centrifugal and Axial Pump Design and Off-Design Performance Prediction

A meanline pump-flow modeling method has been developed to provide a fast capability for modeling pumps of cryogenic rocket engines. Based on this method, a meanline pump-flow code PUMPA was written that can predict the performance of pumps at off-design operating conditions, given the loss of the diffusion system at the design point. The design-point rotor efficiency and slip factors are obtained from empirical correlations to rotor-specific speed and geometry. The pump code can model axial, inducer, mixed-flow, and centrifugal pumps and can model multistage pumps in series. The rapid input setup and computer run time for this meanline pump flow code make it an effective analysis and conceptual design tool. The map-generation capabilities of the code provide the information needed for interfacing with a rocket engine system modeling code. The off-design and multistage modeling capabilities of PUMPA permit the user to do parametric design space exploration of candidate pump configurations and to provide head-flow maps for engine system evaluation.

Veres, Joseph P.↗

NASA NTRS2000

Mean Line Pump Flow Model in Rocket Engine System Simulation

A mean line pump flow modeling method has been developed to provide a fast capability for modeling turbopumps of rocket engines. Based on this method, a mean line pump flow code PUMPA has been written that can predict the performance of pumps at off-design operating conditions, given the loss of the diffusion system at the design point. The pump code can model axial flow inducers, mixed-flow and centrifugal pumps. The code can model multistage pumps in series. The code features rapid input setup and computer run time, and is an effective analysis and conceptual design tool. The map generation capability of the code provides the map information needed for interfacing with a rocket engine system modeling code. The off-design and multistage modeling capabilities of the code permit parametric design space exploration of candidate pump configurations and provide pump performance data for engine system evaluation. The PUMPA code has been integrated with the Numerical Propulsion System Simulation (NPSS) code and an expander rocket engine system has been simulated. The mean line pump flow code runs as an integral part of the NPSS rocket engine system simulation and provides key pump performance information directly to the system model at all operating conditions.

Veres, Joseph P.↗

NASA NTRS2015

Rho-Isp Revisited and Basic Stage Mass Estimating for Launch Vehicle Conceptual Sizing Studies

The ideal rocket equation is manipulated to demonstrate the essential link between propellant density and specific impulse as the two primary stage performance drivers for a launch vehicle. This is illustrated by examining volume-limited stages such as first stages and boosters. This proves to be a good approximation for first-order or Phase A vehicle design studies for solid rocket motors and for liquid stages, except when comparing to hydrogen-fueled stages. A next-order mass model is developed that is able to model the mass differences between hydrogen-fueled and other stages. Propellants considered range in density from liquid methane to inhibited red fuming nitric acid. Calculated comparisons are shown for solid rocket boosters, liquid first stages, liquid upper stages, and a balloon-deployed single-stage-to-orbit concept. The derived relationships are ripe for inclusion in a multi-stage design space exploration and optimization algorithm, as well as for single-parameter comparisons such as those shown herein.

Kibbey, Timothy P.↗