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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
NASA NTRSDate not supplied

Envisioning the Future Role of an Exploration Clinical Decision Support System

The Exploration Medical Capability Element of the NASA Human Research Program seeks to fuse new and existing technologies with practical mission goals into feasible and fiscally realizable human missions to the Moon and Mars. Expected communication delays with Earth-based medical experts will require unprecedented crew self-reliance to rapidly identify and treat anticipated and unforeseen medical conditions using constrained onboard resources with limited crew clinical skill. During this session, the current status of this project will be presented. We will discuss how current modes of decision support (e.g., alerts of critical values, reminders of overdue preventive health tasks, guided clinical workflows, advice for drug prescribing, critiques of existing health care orders, and suggestions for various active care issues) can be tailored to exploration crew needs.

human-system integration↗
NASA NTRS2019

Collecting and Processing Earth Science Data Metrics at NASA ESDIS

Since the launch of Terra satellite in 1999, the number of Earth Science remote sensing data products created and distributed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) has increased from a few hundred to nearly ten thousand. NASA's Earth Science Data and Information System (ESDIS) Metrics System (EMS) collects metrics on data ingest, archive, and distribution by its Distributed Active Archive Centers (DAACs) and the Science Investigator-led Systems (SIPS), known as Data Providers. These metrics are critical in helping NASA management as well as data producers in resource planning and gaining a wide range of knowledge of data users and data usage.EMS receives flat files, or log files of data archive, ingest, and distribution either in their raw format, such as Apache web logs, or text files of log records formatted by the Data Providers. Tens of millions of records are processed each day to extract metrics on data products, user information, distribution protocols and services, and so on. The metrics are then made available to designated parties.This presentation provides an overview of the EMS processing workflow and improvement efforts made in recent years to handle ever-increasing number of data records and new metrics requirements, discusses several key steps including mapping log records to data products and identifying user communities along with geo-distribution, and demonstrates typical metrics capabilities produced by the EMS system. Challenges and potential approaches to improve the system are also discussed.

Pan, Jianfu↗
NASA NTRS2006

LDCM Grid Prototype (LGP)

The LGP successfully demonstrated that grid technology could be used to create a collaboration among research scientists, their science development machines, and distributed data to create a science production system in a nationally distributed environment. Grid technology provides a low cost and effective method of enabling production of science products by the science community. To demonstrate this, the LGP partnered with NASA GSFC scientists and used their existing science algorithms to generate virtual Landsat-like data products using distributed data resources. LGP created 48 output composite scenes with 4 input scenes each for a total of 192 scienes processed in parallel. The demonstration took 12 hours, which beat the requirement by almost 50 percent, well within the LDCM requirement to process 250 scenes per day. The LGP project also showed the successful use of workflow tools to automate the processing. Investing in this technology has led to funding for a ROSES ACCESS proposal. The proposal intends to enable an expert science user to produce products from a number of similar distributed instrument data sets using the Land Cover Change Community-based Processing and Analysis System (LC-ComPS) Toolbox. The LC-ComPS Toolbox is a collection of science algorithms that enable the generation of data with ground resolution on the order of Landsat-class instruments.

Weinstein, Beth↗
DOE OSTI2024

A Workflow to Optimize Fast Neutron Irradiation in A Thermal Neutron Spectrum Test Reactor Leveraging Open-Source Tools

The Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) is one of the key nuclear engineering research and testing facilities within the US Department of Energy (DOE). The ATR is one of few high-power research reactors in the world with different application including accelerated testing of nuclear fuel, materials irradiation in a very high neutron flux environment, and medical radioisotope production [1]. Also, the ATR offers opportunities for testing fast spectrum fission and fusion reactor materials. The key challenges in this area are in further detailing and optimizing a fast spectrum environment within a thermal test reactor. This challenge involves researching, developing, and testing novel concepts for the multiplying of neutron populations into ever higher energy spectra in high flux test reactors like ATR. The main objective of this work is to investigate candidate materials for establishing a fast neutron experiment irradiation in thermal neutron spectrum test reactors which can be accomplished by filtering thermal and epithermal neutrons and boosting fast neutrons at designated irradiation positions. However, adding these filters will render the neutron spectrum and the criticality of the system. The selection of the thickness and material layers should be accomplished by developing an optimization design algorithm that is applicable for ATR to enhance the fast neutron spectrum irradiation utilizing high-fidelity Monte Carlo methods along with advanced machine learning capabilities. This paper presents workflow for design optimization to enhance fast neutron irradiation in the ATR. The workflow leverages open-source tools to develop an algorithm that is viable to ATR and can be leveraged in other reactors. The following sections discuss the development of the experiment design optimization workflow and its application to ATR irradiation positions.

42 - ENGINEERING↗
DOE OSTI2026

Synthetic Digital Environments for Training Robots on Earth & Beyond [Poster]

Creating physical replicas of real-world environments to train robots for challenging outdoor tasks, whether constructing energy infrastructure like solar farms on Earth or on the Moon and Mars, is prohibitively expensive. This project will prototype a high-fidelity digital twin framework using NVIDIA IsaacSim to create realistic digital representations of robotic systems and their operating conditions, including varied terrains and environmental factors, allowing robots to learn and adapt in a faster, safer, and more affordable way to tackle unpredictable challenges in terrestrial and extraterrestrial applications. The Robotic Space Exploration (RoSE) Lab at Colorado School of Mines focused on the development and testing of synthetic digital twins to explore multi-physics interactions between robots and unstructured environments, with emphasis on lunar conditions such as deformable regolith, reduced gravity, and terrain-robot contact dynamics. The Industrialized Construction Innovation (ICI) team at National laboratory of the Rockies (NLR), simulated robotic apparatus and construction workflows using synthetic digital twins to inform real-world deployment, targeting application-driven use cases such as robotic construction of a scaled prototype of a photovoltaic energy infrastructure. Joint efforts between RoSE and ICI are continuing to explore how environment-scale multi-physics modeling and application-level robotic system simulation could be integrated to support robotic construction of energy infrastructure in highly unstructured environments, including scenarios relevant to the Lunar South Pole.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hoobas: A highly object-oriented builder for molecular dynamics

Polydispersity and random sequences are ubiquitous features of polymers, and molecular dynamics simulations can help elucidate the impact of disorder in polymer systems. However, currently available packages for building polymer topologies do not enable the user to include randomness in a straightforward fashion. Here, we introduce Hoobas, a molecular builder package that easily handles polydispersity using a prototype-builder design pattern. This enables fast and easy building of systems comprised of thousands of distinct objects. It is written in the Python programming language, which ensures compatibility with a wide range of molecular dynamics packages and tools, as well as easy integration into most workflows.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

The Open Data Repositorys Data Publisher

Data management and data publication are becoming increasingly important components of researcher's workflows. The complexity of managing data, publishing data online, and archiving data has not decreased significantly even as computing access and power has greatly increased. The Open Data Repository's Data Publisher software strives to make data archiving, management, and publication a standard part of a researcher's workflow using simple, web-based tools and commodity server hardware. The publication engine allows for uploading, searching, and display of data with graphing capabilities and downloadable files. Access is controlled through a robust permissions system that can control publication at the field level and can be granted to the general public or protected so that only registered users at various permission levels receive access. Data Publisher also allows researchers to subscribe to meta-data standards through a plugin system, embargo data publication at their discretion, and collaborate with other researchers through various levels of data sharing. As the software matures, semantic data standards will be implemented to facilitate machine reading of data and each database will provide a REST application programming interface for programmatic access. Additionally, a citation system will allow snapshots of any data set to be archived and cited for publication while the data itself can remain living and continuously evolve beyond the snapshot date. The software runs on a traditional LAMP (Linux, Apache, MySQL, PHP) server and is available on GitHub (http://github.com/opendatarepository) under a GPLv2 open source license. The goal of the Open Data Repository is to lower the cost and training barrier to entry so that any researcher can easily publish their data and ensure it is archived for posterity.

Astrobiology data↗
DOE OSTI2021

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗
DOE OSTI2022

IDAES-PSE 1.13.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

IDAES↗
NASA NTRS2011

Automated X-ray and Optical Analysis of the Virtual Observatory and Grid Computing

We are developing a system to combine the Web Enabled Source Identification with X-Matching (WESIX) web service, which emphasizes source detection on optical images,with the XAssist program that automates the analysis of X-ray data. XAssist is continuously processing archival X-ray data in several pipelines. We have established a workflow in which FITS images and/or (in the case of X ray data) an X-ray field can be input to WESIX. Intelligent services return available data (if requested fields have been processed) or submit job requests to a queue to be performed asynchronously. These services will be available via web services (for non-interactive use by Virtual Observatory portals and applications) and through web applications (written in the Django web application framework). We are adding web services for specific XAssist functionality such as determining .the exposure and limiting flux for a given position on the sky and extracting spectra and images for a given region. We are improving the queuing system in XAssist to allow for "watch lists" to be specified by users, and when X-ray fields in a user's watch list become publicly available they will be automatically added to the queue. XAssist is being expanded to be used as a survey planning 1001 when coupled with simulation software, including functionality for NuStar, eRosita, IXO, and the Wide Field Xray Telescope (WFXT), as part of an end to end simulation/analysis system. We are also investigating the possibility of a dedicated iPhone/iPad app for querying pipeline data, requesting processing, and administrative job control.

Ptak, A.↗
DOE OSTI2026

Netload Range Cost Curves for Coordinated Transmission-Distribution Planning Under DER Growth Uncertainty

The increasing penetration of distributed energy resources (DERs) requires better coordination between transmission and distribution (T&D) planning to ensure system security and cost efficiency. However, misaligned planning horizons, computational burdens, and privacy concerns hinder effective coordination, leading to either underutilized resources caused by overinvestments or reliability risks due to underinvestment. To address this challenge, we introduce netload range cost curves (NRCCs), a novel approach for managing long-term DER growth uncertainty through T&D coordination, while preserving existing data-sharing and regulatory structures. NRCCs provide pairs of (i) peak substation netload guarantees and (ii) corresponding distribution upgrade options and costs, enabling their seamless integration into transmission planning workflows. To compute NRCCs efficiently, we develop a transmission-aware distribution network planning (TADNP), which is subsequently integrated to an iterative computation procedure. These NRCCs are then embedded into an NRCC-informed transmission planning model to enable resource-efficient coordination. We illustrate our proposed approach with a case study based on realistic distribution and transmission systems in the San Francisco Bay Area, California. Our results indicate the possibility of dramatic savings in transmission investments by incorporating the proposed NRCC-integrated T&D coordination framework.

Li, Yujia↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
NASA NTRSDate not supplied

Updates in Developing a Prototype Science Pipeline and Full-Volume, Global Hyperspectral Synthetic Data Sets for NASA’s Earth System Observatory’s Upcoming Surface, Biology and Geology Mission

The Surface Biology and Geology (SBG) mission recently passed mission confirmation review and has entered phase A – design and development. SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~2.5 TB/day and generate products at ~40 TB/day. Given that the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing, disseminating, and exploiting the SBG data present new challenges. To meet these challenges, we have developed a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight (see poster GC42D-0730). Our science pipeline is based on the science processing technology developed for NASA’s Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or a supercomputer. Ziggy is compliant with NASA Procedural Requirement (NPR) 7150.2C, is at a technical readiness level (TRL) of 7 and has been released to github.com/nasa/ziggy. We integrated Ziggy with EO-1/Hyperion workflows to build a prototype pipeline and ingested the 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We fully implemented the first stage and processed the entire 55 TB Hyperion data set from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We are currently evaluating the ISOFIT atmospheric correction module to convert the L1R data to surface reflectance (Level 2) before reprocessing the full data set to L2. Crosschecks are being performed with RadCalNet as well as with coincident observations by AVIRIS. We are also investigating modern methods for georectifying the Hyperion scenes. Finally, we describe an analysis of the cost to conduct forward processing and reprocessing campaigns for SBG on HECC with dedicated compute and storage resources using the resurrected Hyperion pipeline as a proxy for full-volume SBG data. The analysis demonstrates that SBG L0 data can be processed to L2 on HECC with full reprocessing campaigns every two years for ~$2.6M over a 7-year lifespan. Moreover, 69% of the system capacity would be available for other activities, possibly enabling future open-source science activities, including algorithm development, L3+ processing, .etc.

ESD↗
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
NASA NTRS2016

Geocam Space: Enhancing Handheld Digital Camera Imagery from the International Space Station for Research and Applications

Handheld astronaut photography of the Earth has been collected from the International Space Station (ISS) since 2000, making it the most temporally extensive remotely sensed dataset from this unique Low Earth orbital platform. Exclusive use of digital handheld cameras to perform Earth observations from the ISS began in 2004. Nadir viewing imagery is constrained by the inclined equatorial orbit of the ISS to between 51.6 degrees North and South latitude, however numerous oblique images of land surfaces above these latitudes are included in the dataset. While unmodified commercial off-the-shelf digital cameras provide only visible wavelength, three-band spectral information of limited quality current cameras used with long (400+ mm) lenses can obtain high quality spatial information approaching 2 meters/ground pixel resolution. The dataset is freely available online at the Gateway to Astronaut Photography of Earth site (http://eol.jsc.nasa.gov), and now comprises over 2 million images. Despite this extensive image catalog, use of the data for scientific research, disaster response, commercial applications and visualizations is minimal in comparison to other data collected from free-flying satellite platforms such as Landsat, Worldview, etc. This is due primarily to the lack of fully-georeferenced data products - while current digital cameras typically have integrated GPS, this does not function in the Low Earth Orbit environment. The Earth Science and Remote Sensing (ESRS) Unit at NASA Johnson Space Center provides training in Earth Science topics to ISS crews, performs daily operations and Earth observation target delivery to crews through the Crew Earth Observations (CEO) Facility on board ISS, and also catalogs digital handheld imagery acquired from orbit by manually adding descriptive metadata and determining an image geographic centerpoint using visual feature matching with other georeferenced data, e.g. Landsat, Google Earth, etc. The lack of full geolocation information native to the data makes it difficult to integrate astronaut photographs with other georeferenced data to facilitate quantitative analysis such as urban land cover/land use classification, change detection, or geologic mapping. The manual determination of image centerpoints is both time and labor-intensive, leading to delays in releasing geolocated and cataloged data to the public, such as the timely use of data for disaster response. The GeoCam Space project was funded by the ISS Program in 2015 to develop an on-orbit hardware and ground-based software system for increasing the efficiency of geolocating astronaut photographs from the ISS (Fig. 1). The Intelligent Robotics Group at NASA Ames Research Center leads the development of both the ground and on-orbit systems in collaboration with the ESRS Unit. The hardware component consists of modified smartphone elements including cameras, central processing unit, wireless Ethernet, and an inertial measurement unit (gyroscopes/accelerometers/magnetometers) reconfigured into a compact unit that attaches to the base of the current Nikon D4 camera - and its replacement, the Nikon D5 - and connects using the standard Nikon peripheral connector or USB port. This provides secondary, side and downward facing cameras perpendicular to the primary camera pointing direction. The secondary cameras observe calibration targets with known internal X, Y, and Z position affixed to the interior of the ISS to determine the camera pose corresponding to each image frame. This information is recorded by the GeoCam Space unit and indexed for correlation to the camera time recorded for each image frame. Data - image, EXIF header, and camera pose information - is transmitted to the ground software system (GeoRef) using the established Ku-band USOS downlink system. Following integration on the ground, the camera pose information provides an initial geolocation estimate for the individual film frame. This new capability represents a significant advance in geolocation from the manual feature-matching approach for both nadir and off-nadir viewing imagery. With the initial geolocation estimate, full georeferencing of an image is completed using the rapid tie-pointing interface in GeoRef, and the resulting data is added to the Gateway to Astronaut Photography of Earth online database in both Geotiff and Keyhole Markup Language (kml) formats. The integration of the GeoRef software component of Geocam Space into the CEO image cataloging workflow is complete, and disaster response imagery acquired by the ISS crew is now fully georeferenced as a standard data product. The on-orbit hardware component (GeoSens) is in final prototyping phase, and is on-schedule for launch to the ISS in late 2016. Installation and routine use of the Geocam Space system for handheld digital camera photography from the ISS is expected to significantly improve the usefulness of this unique dataset for a variety of public- and private-sector applications.

Stefanov, William L.↗
DOE OSTI2025

Incorporating corrosion design constraints in desalination process optimization: A case study in mechanical vapor compression

Corrosion is an expensive and complex challenge for desalination, yet current design approaches do not explicitly account for corrosion mechanisms in process modeling and technoeconomic analysis. Here, to address this gap, we present a workflow for incorporating corrosion design constraints directly into desalination process optimization models. We develop surrogate models for general and localized corrosion metrics as functions of temperature, pH, salinity, dissolved oxygen, and material using data from OLI Systems’ Corrosion Analyzer. We then integrate these surrogates as corrosion design constraints in a cost-optimization MVC model that minimizes the levelized cost of water (LCOW). For a case study of mechanical vapor compression (MVC) treating seawater across a range of recoveries, we find dissolved oxygen (DO) is the dominant driver of localized corrosion, and thus of cost-optimal material choice and operating conditions. Reducing the DO from 8 mg/L to 0.5 mg/L reduces the LCOW by 15-35%, informing the breakeven costs for implementing DO removal or selecting highly corrosion-resistant alloys. This framework is broadly applicable across corrosion types, materials, and components and enables desalination process design that minimizes capital costs.

36 MATERIALS SCIENCE↗