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All sources NASA NTRS DOE OSTI

NASA NTRS1991

Neural-Network Simulator

F77NNS (FORTRAN 77 Neural Network Simulator) computer program simulates popular back-error-propagation neural network. Designed to take advantage of vectorization when used on computers having this capability, also used on any computer equipped with ANSI-77 FORTRAN Compiler. Problems involving matching of patterns or mathematical modeling of systems fit class of problems F77NNS designed to solve. Program has restart capability so neural network solved in stages suitable to user's resources and desires. Enables user to customize patterns of connections between layers of network. Size of neural network F77NNS applied to limited only by amount of random-access memory available to user.

Mitchell, Paul H.↗

NASA NTRS1991

Version 0 EOSDIS - An overview

Attention is given to NASA's Earth Observing System Data and Information System (EOSDIS), which is to be a single, distributed but internally consistent evolutionary system to support the planning and execution of EOS data acquisitions and to process, archive, and distribute EOS data products and selected non-EOS data to enable interdisciplinary studies of the earth. V0 EOSDIS, a logical step in this evolutionary process, is to address both technical and managerial challenges. Technical challenges include developing a multidiscipline, distributed system for searching and ordering data in a heterogeneous environment, and standardizing data formats and distribution techniques among differing communities and organizations. Managerial challenges include establishing and maintaining a structure consisting of geographically distributed entities such that cooperative development is carried out effectively despite organizational differences, maintaining interactions with the scientific community to ensure its close involvement despite its size and diversity, and keeping the expectations for V0 consistent with its schedules and resources.

Ramapriyan, H. K.↗

NASA NTRS2007

Human Space Exploration: The Moon, Mars, and Beyond

America is returning to the Moon in preparation for the first human footprint on Mars, guided by the U.S. Vision for Space Exploration. This presentation will discuss NASA's mission, the reasons for returning to the Moon and going to Mars, and how NASA will accomplish that mission in ways that promote leadership in space and economic expansion on the new frontier. The primary goals of the Vision for Space Exploration are to finish the International Space Station, retire the Space Shuttle, and build the new spacecraft needed, to return people to the Moon and go to Mars. The Vision commits NASA and the nation to an agenda of exploration that also includes robotic exploration and technology development, while building on lessons learned over 50 years of hard-won experience. Why the Moon? Many questions about the Moon's potential resources and how its history is linked to that of Earth were spurred by the brief Apollo explorations of the 1960s and 1970s. This new venture will carry more explorers to more diverse landing sites with more capable tools and equipment for extended expeditions. The Moon also will serve as a training ground before embarking on the longer, more difficult trip to Mars. NASA plans to build a lunar outpost at one of the lunar poles, learn to live off the land, and reduce dePendence on Earth for longer missions. America needs to extend its ability to survive in hostile environments close to our home planet before astronauts will reach Mars, a planet very much like Earth. NASA has worked with scientists to define lunar exploration goals and is addressing the opportunities for a range of scientific study on Mars. In order to reach the Moon and Mars within a lifetime and within budget, NASA is building on common hardware, shared knowledge, and unique experience derived from the Apollo Saturn, Space Shuttle and contemporary commercial launch vehicle programs. The journeys to the Moon and Mars will require a variety of vehicles, including the Ares I Crew Launch Vehicle, which transports the Orion Crew Exploration Vehicle, and the Ares V Cargo Launch Vehicle, which transports the Lunar Surface Access Module. The architecture for the lunar missions will use one launch to ferry the crew into orbit, where it will rendezvous with the Lunar Module in the Earth Departure Stage, which will then propel the combination into lunar orbit. The imperative to explore space with the combination of astronauts and robots will be the impetus for inventions such as solar power and water and waste recycling. This next chapter in NASA's history promises to write the next chapter in American history, as well. It will require this nation to provide the talent to develop tools, machines, materials, processes, technologies, and capabilities that can benefit nearly all aspects of life on Earth. Roles and responsibilities are shared between a nationwide Government and industry team. The Exploration Launch Projects Office at the Marshall Space Flight Center manages the design, development, testing, and evaluation of both vehicles and serves as lead systems integrator. A little over a year after it was chartered, the Exploration Launch Projects team is testing engine components, refining vehicle designs, performing wind tunnel tests, and building hardware for the first flight test of Ares I-l, scheduled for spring 2009. The U.S. Vision for Space Exploration lays out a roadmap for a long-term venture of discovery. This endeavor will inspire and attract the best and brightest students to power this nation successfully to the Moon, Mars, and beyond. If one equates the value proposition for space with simple dollars and cents, the potential of the new space economy is tremendous, from orbital space delivery services for the International Space Station to mining and solar energy collection on the Moon and asteroids. The Vision for Space Exploration is fundamentally about bringing the resources of the solar system within the economic sphere of humaind. Given the immense size of our solar system, the amount of available material and energy within it present an enormous economic opportunity.

Sexton, Jeffrey D.↗

NASA NTRS2008

Figure of Merit Characteristics Compared to Engineering Parameters

Current NASA lunar architecture calls for permanent human habitation of the moon by the year 2020. Due to the expense of delivering materials into orbit, technologies are being developed to use lunar regolith for building and as a material resource for fabrication, oxygen production, and other needs. Additionally, constant exposure to the finest size fraction of lunar regolith may present hazards to human health. Towards developing these technologies and mitigating hazards, lunar regolith simulants are becoming an increasingly important part of the development paradigm.

Rickman, Doug L.↗

NASA NTRSDate not supplied

Differential Dynamic Microscopy: A Novel Approach for Dynamics and Micro-rheology of Active Soft Matter

Differential dynamic microscopy (DDM) is a highly sensitive and cost-effective technique for studying the dynamics and micro-rheology of active soft matter in real time and at high resolution. In this work, we extend the capabilities of DDM, notably its size, to suit to space research and overcome challenges such as the environmental limited resources (e.g. Artemis, ISS, Cube sat, etc.).

Amine Missaoui↗

DOE OSTI2026

Accessing and Understanding REopt's Federal Assumptions

REopt is a techno-economic analysis platform accessible as a user-friendly web tool that facilitates life cycle cost analysis of distributed energy resources. It is typically used for preliminary assessments to identify the least-cost technology mix, system sizing, and operations strategies towards agency cost savings and resilience goals. This guide identifies and explains REopt inputs for federal life cycle cost analyses, modified from their commercial default values. These federal input defaults are based on statutory requirements for life cycle cost analyses of energy conservation measures at federal facilities per 10 CFR 436 Subpart A.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2005

The NASA Bed Rest Project

NASA s National Vision for Space Exploration includes human travel beyond low earth orbit and the ultimate safe return of the crews. Crucial to fulfilling the vision is the successful and timely development of countermeasures for the adverse physiological effects on human systems caused by long term exposure to the microgravity environment. Limited access to in-flight resources for the foreseeable future increases NASA s reliance on ground-based analogs to simulate these effects of microgravity. The primary analog for human based research will be head-down bed rest. By this approach NASA will be able to evaluate countermeasures in large sample sizes, perform preliminary evaluations of proposed in-flight protocols and assess the utility of individual or combined strategies before flight resources are requested. In response to this critical need, NASA has created the Bed Rest Project at the Johnson Space Center. The Project establishes the infrastructure and processes to provide a long term capability for standardized domestic bed rest studies and countermeasure development. The Bed Rest Project design takes a comprehensive, interdisciplinary, integrated approach that reduces the resource overhead of one investigator for one campaign. In addition to integrating studies operationally relevant for exploration, the Project addresses other new Vision objectives, namely: 1) interagency cooperation with the NIH allows for Clinical Research Center (CRC) facility sharing to the benefit of both agencies, 2) collaboration with our International Partners expands countermeasure development opportunities for foreign and domestic investigators as well as promotes consistency in approach and results, 3) to the greatest degree possible, the Project also advances research by clinicians and academia alike to encourage return to earth benefits. This paper will describe the Project s top level goals, organization and relationship to other Exploration Vision Projects, implementation strategy, address Project deliverables, schedules and provide a status of bed rest campaigns presently underway.

Rhodes, Bradley↗

DOE OSTI2022

Insights into Methodologies and Operational Details of Resource Adequacy Assessment: A Case Study with Application to a Broader Flexibility Framework

Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2025

The Effects of Compounded Model Size Reductions on Adversarial Robustness

Recent advances in Edge AI and Tiny Machine Learning (TinyML) have enabled the deployment of machine learning models on resource-constrained environments. However, deploying these models on edge devices, such as micro-controllers, requires significant model footprint reduction through a variety of techniques such as quantization, pruning, and clustering. While these optimization methods offer considerable advantages, they potentially introduce AI-related security vulnerabilities, particularly concerning model robustness with respect to adversarial AI attacks. Prior research has extensively examined the impact of quantization on adversarial robustness; however, the effects of alternative reduction techniques and their combinations remain understudied. This paper investigates the impact of model size reduction techniques on adversarial robustness, when applied individually and combined. We utilized Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks to generate adversarial perturbations for both training and testing data, and then evaluated the models' accuracy under adversarial training conditions. Our findings revealed that reduction techniques generally diminished robustness; although, combining techniques was not found to make robustness any worse than when applied individually. Moreover, specific techniques can potentially enhance resistance to small size perturbations. This research provides insights into the trade-offs between model size reduction and security, establishing a foundation for future investigations into improving adversarial training techniques and methodologies for maintaining robustness while preserving memory footprint benefits.

Austria, Phillipe [ORNL] (ORCID:0000000236223973)↗

NASA NTRSDate not supplied

Determining True Sensor Spatial Resolution of Very High Resolution Optical Imagery

Some satellite data is delivered in images with gridded pixels. This gridded pixel size is often assumed to be the spatial resolution of the satellite sensor; however, this is not always the case. An image can be grided to any arbitrary pixel size, but the sensor resolution will remain constant. For example, an image with a pixel grid size much smaller than the sensor resolution will appear blurry along what should be sharp transitions. This discrepancy between an image’s pixel size and true sensor spatial resolution can be the source of much confusion and even misinformation among data users, which may lead them to waste time and resources on using images that do not suit their spatial resolution needs. This presentation will highlight our evaluation of the true spatial resolution of various government and commercial images in the pixel size range of 0.3 m to 60 m. Images evaluated include ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels). Our evaluation of true sensor spatial resolution, or ‘footprint size’ is based on the sensor’s line spread function (LSF). We calculate the width at half the height of the LSF to find the full width at half maximum (FWHM). The FWHM is how we report sensor spatial resolution. Different objects are examined for constructing the LSF depending on the sensor spatial resolution. Coarser resolution sensors in this evaluation such as Sentinel-2 and Landsat 8/9 are examined at bridges over a dark water background. The bright bridge acts as a line impulse, giving a sensor’s line spread function (LSF) in one direction. Additionally, we simulate the impacts of bridge width on the apparent LSF to obtain a true LSF without the effects of bridge width for these sensors. Finer resolution sensors will image the irregularities in bridges such as trusses, sidewalks, and in some cases painted lines, interfering with the LSF construction. Instead, these sensors are evaluated at large (60 m – 140 m) black and white checkerboards known as Cal/Val sites. At these locations, the image’s transition from black to white is extracted as an edge spread function (ESF). We calculate the derivative of this ESF to obtain the sensor’s LSF. From there, we find the FWHM as we do for the coarser resolution images. With the FWHM and pixel size, we determine how over- or under-sampled the images are. When the ratio of a sensor’s spatial resolution and the gridded image’s pixel size is less than 1, the image is considered under-sampled. In this case, each pixel’s information is unique but only a portion of that pixel’s ground area has been measured. On the other side, if the ratio is greater than 1, the image is considered over-sampled. That is, each pixel’s information is sourced from within the ground extent of the pixel and some extent outside additionally. We will show the true spatial resolution and the extent of over-/under-sampling in the imagery from ESA’s Sentinel-2 (60 m, 20 m, 10 m pixels), USGS’s Landsat 8/9 (30 m & 15 m pixels), Planet’s SuperDoves (3 m pixels), BlackSky’s Globals (~1 m pixels), and the optical bands of Maxar’s WorldView-2 (2.4 m – 0.41 m pixels) and WorldView-3 (1.38 m – 0.31 m pixels).

Alana Semple↗

DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗

DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗

NASA NTRS2011

History of POIC Capabilities and Limitations to Conduct International Space Station Payload Operations

Payload science operations on the International Space Station (ISS) have been conducted continuously twenty-four hours per day, 365 days a year beginning February, 2001 and continuing through present day. The Payload Operations Integration Center (POIC), located at the Marshall Space Flight Center in Huntsville, Alabama, has been a leader in integrating and managing NASA distributed payload operations. The ability to conduct science operations is a delicate balance of crew time, onboard vehicle resources, hardware up-mass to the vehicle, and ground based flight control team manpower. Over the span of the last ten years, the POIC flight control team size, function, and structure has been modified several times commensurate with the capabilities and limitations of the ISS program. As the ISS vehicle has been expanded and its systems changed throughout the assembly process, the resources available to conduct science and research have also changed. Likewise, as ISS program financial resources have demanded more efficiency from organizations across the program, utilization organizations have also had to adjust their functionality and structure to adapt accordingly. The POIC has responded to these often difficult challenges by adapting our team concept to maximize science research return within the utilization allocations and vehicle limitations that existed at the time. In some cases, the ISS and systems limitations became the limiting factor in conducting science. In other cases, the POIC structure and flight control team size were the limiting factors, so other constraints had to be put into place to assure successful science operations within the capabilities of the POIC. This paper will present the POIC flight control team organizational changes responding to significant events of the ISS and Shuttle programs.

Grimaldi, Rebecca↗

DOE OSTI2026

Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. Here, this paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2% and provide flexibility in managing wind resource uncertainties.

Control Co-design↗

DOE OSTI2025

High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Protein Data Bank (PDB): Fifty-three years young and having a transformative impact on science and society

This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.

bioinformatics↗

DOE OSTI2022

Variational approaches to constructing the many-body nuclear ground state for quantum computing

Here, we explore the preparation of specific nuclear states on gate-based quantum hardware using variational algorithms. Large-scale classical diagonalizations of the nuclear shell model have reached sizes of 10 9 –10 10 basis states but are still severely limited by computational resources. Quantum computing can, in principle, solve such systems exactly with exponentially fewer resources than classical computing. Exact solutions for large systems require many qubits and large gate depth, but variational approaches can effectively limit the required gate depth. We use the unitary coupled cluster approach to construct approximations of the ground-state vectors, later to be used in dynamics calculations. The testing ground is the phenomenological shell model space, which allows us to mimic the complexity of the internucleon interactions. We find that often one needs to minimize over a large number of parameters, using a large number of entanglements that makes the application on existing hardware challenging. Prospects for rapid improvements with more capable hardware are, however, very encouraging.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Comparison of a Full-Scale and a 1:10 Scale Low-Speed Two-Stroke Marine Engine Using Computational Fluid Dynamics

International marine shipping is a growing component of international trade; a vast majority of all the world’s goods are being transported on large ocean-going vessels. The International Maritime Organization (IMO) introduced the Energy Efficiency Design Index in 2013, a regulatory framework of associated metrics for reducing emissions of CO 2 per tonne-mile from shipping by approximately 10% each decade. Therefore, decarbonizing the maritime sector requires the development of new fuel sources. Because of the extremely large physical size of the internal combustion engines present in shipping vessels, experimental iterative development of the engine and fuel system is cost-prohibitive. Thus, the ability to perform combustion system development in a scaled platform that can be more easily operated and modeled computationally is of interest. To that end, scaling relationships are needed to translate the results from a smaller engine to a larger counterpart. Scaling studies to date have been restricted to low scaling ratios, four-stroke light-duty engines, and under-resolved computational fluid dynamic simulations that likely do not accurately capture the physics of scaling. In this work, computational models of a 1:10 scale and a full-scale two-stroke crosshead low-speed marine engine were created and validated against experiments obtained in a real 1:10 scale engine installed at Oak Ridge National Laboratory. Further, due to the large size of the full-scale engine, the model required large high-performance computing resources to be evaluated. The availability of high-performance computing resources at the Department of Energy’s Leadership Computing Facilities is an enabler of the current work. The results of the small- and large-scale engine simulations were compared to analyze the effectiveness of the appropriate scaling laws under these extreme scaling ratio conditions.

33 ADVANCED PROPULSION SYSTEMS↗