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At least 1,387 records · Page 77

Integration of Solid Oxide Fuel Cell Systems Into Artificial Intelligence Data Centers

This report presents the results of a techno-economic analysis (TEA) that evaluates the economic benefits of integrating solid oxide fuel cell (SOFC) systems with artificial intelligence (AI) data centers. The analysis was completed in two phases: a scoping-level analysis was performed to identify impactful integration opportunities, followed by a more detailed TEA. Results show that, due to their modularity, SOFC can meet the 99.999% availability requirement of data centers with minimal additional costs. Heat integration via absorption chillers decreases data center electricity consumption at the tradeoff of increased water consumption. Higher SOFC exhaust temperatures are important for achieving larger electricity savings. Finally, power electronics integration with SOFC direct current electricity can reduce electricity consumption by 9 percent and reduce water consumption by 6.4 percent.

20 FOSSIL-FUELED POWER PLANTS↗

Integrating Resilience Planning in Distribution System Planning

Electric utilities, regulators, and stakeholders face increasing risks of severe storms, freezes, floods, and heat waves damaging grid infrastructure and causing power outages—and increasing risks of utility equipment igniting wildfires. At the same time, customer electricity rates have risen substantially in recent years, due in part to replacing aging infrastructure and improving resilience to natural hazards and physical threats. To address these challenges, utilities are beginning to move beyond traditional, siloed planning processes to balance resilience with other fundamental grid objectives such as affordability, reliability, safety, and serving new loads. This study presents a framework for states and utilities that want to advance integration of resilience and distribution planning processes to improve planning efficiency, better prioritize cost-effective grid expenditures, and balance planning objectives. The framework includes 7 key integration points between these planning processes: -Strategy process -Data -Threat assessments -Solution identification and prioritization -Optimization opportunities -Consideration of other grid needs -Metrics Lawrence Berkeley National Laboratory reviewed utility distribution system plans and interviewed subject matter experts to identify emerging practices for each of the 7 integration points. This report presents these practices, which can be used as a guide toward more holistic planning and cohesive investment strategies. It also includes 3 case studies to provide practical examples of how utilities apply such integrated planning processes: two pole hardening programs and one microgrid planning effort. The report concludes by identifying opportunities for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrating Carbon Capture, Utilization, & Sequestration into Chemical Pulp Mills

The U.S. pulp and paper industry presents a unique and largely untapped opportunity for large- scale carbon dioxide removal (CDR). Unlike most industrial sectors, pulp mills rely heavily on biomass, meaning that much of their carbon emissions originate from atmospheric CO₂ that was recently captured by plants. If this biogenic CO₂ can be captured and permanently stored, pulp mills can be transformed from carbon emitters into net carbon removal facilities. This project was motivated by that opportunity and aimed to develop and evaluate integrated, low-cost strategies for capturing, utilizing, and sequestering CO₂ within existing chemical pulping operations. The scope of this work focused on four complementary innovations designed to integrate seamlessly into kraft pulp mill infrastructure: (1) in situ CO₂ capture within the recovery cycle, (2) oxy-fuel retrofitting of the rotary lime kiln to produce a high-purity CO₂ stream, (3) ex situ CO₂ capture and mineralization using pulp mill residues (dregs, grits, and lime mud), and (4) beneficial reuse of these residues as mineral carbonate fertilizers. The project combined process modeling, laboratory experimentation, life cycle assessment (LCA), and field trials to evaluate the technical feasibility, economic viability, and environmental impact of these approaches. The results demonstrate that pulp mills can serve as effective platforms for carbon removal when equipped with integrated carbon capture systems. Process modeling showed that combining sodium spiking with oxy-fuel calcination significantly enhances CO₂ capture efficiency while reducing costs by up to 31% compared to conventional configurations. Experimental work further revealed that calcination behavior in high-CO₂ environments differs substantially from traditional systems, leading to the development of a new kinetic model that predicts reaction rates under these conditions. This model provides essential design guidance for next-generation decarbonized lime kilns. In parallel, the project demonstrated that alkaline mineral residues generated during pulping operations can be repurposed as a sustainable alternative to agricultural lime. Across a wide range of soils in the southeastern United States, these materials performed equivalently to commercial lime in adjusting soil pH while offering lower greenhouse gas emissions and reduced cost. Field and greenhouse studies confirmed that crop and tree growth responses were comparable, supporting their viability as a drop-in replacement. This co-product pathway provides a practical utilization strategy that offsets costs and improves overall system economics. A major contribution of this project is the first comprehensive life cycle assessment of carbon removal in pulp and paper systems across multiple system boundaries. Results show that retrofitted mills can achieve carbon removal efficiencies ranging from 12% to 92%, depending on how the system is defined. This finding highlights a critical issue in carbon accounting: reported performance is highly sensitive to methodological choices. By explicitly quantifying these differences, this work provides valuable guidance for policymakers, carbon registries, and project developers working to standardize carbon removal metrics. From a commercialization perspective, the technologies investigated in this project are well- aligned with existing industrial infrastructure, minimizing the need for entirely new facilities. 3 DE-EE0009413 Industry engagement throughout the project—including collaboration with pulp and paper companies, equipment manufacturers, and carbon removal developers—has accelerated the transition from research to deployment. Notably, a commercial developer is actively pursuing carbon capture projects at pulp mills in the southeastern United States and has cited this research as a contributing foundation. The emergence of voluntary carbon markets and long-term offtake agreements further strengthens the business case for implementation. The broader public benefits of this work are significant. By enabling large-scale carbon removal using existing industrial systems, this approach offers a near-term pathway to reduce atmospheric CO₂ concentrations while supporting domestic manufacturing and rural economies. The reuse of industrial residues as fertilizers reduces reliance on mined materials, lowers costs for farmers, and decreases environmental impacts associated with conventional lime production. In addition, the project has supported workforce development by training graduate students and researchers in carbon capture technologies, helping to build capacity in a critical area of national interest. In conclusion, this project demonstrates that integrated carbon capture, utilization, and sequestration in pulp mills is both technically feasible and economically promising. By combining process innovation, experimental validation, and systems-level analysis, the work advances the understanding of how biomass-based industries can contribute to climate mitigation. The findings provide a strong foundation for commercial deployment and offer a scalable solution for transforming a major U.S. industry into a source of durable carbon removal.

09 BIOMASS FUELS↗

3D Integration of Sensors and Electronics

A three dimensional integrated circuit is composed of multiple tiers of integrated electronics and sensors integrated vertically by wafer bonding, thinning and insertion of through-silicon-vias. The technologies associated with three dimensional integrated circuits can provide new capabilities for high energy physics and x-ray imaging experiments. These include finer pixel pitch, lower interconnect capacitance, the ability to separate analog and digital functions, and better power distribution and connectivity. In this paper we review the status of 3DIC demonstration projects at Fermilab, discuss several possible applications, and summarize the current availability of commercial processes. Results of a three-tier demonstration project that includes designs for x-ray imaging, CMS track triggering, and ILC vertex are described. We discuss the status of several ongoing 3D projects and prospects for future evolution of the technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Quantized State Integrator With Second Order Errors Over Monotonic Segments

We introduce a novel quantized state integration method that is analogous to the explicit, second order Runge-Kutta technique. A feature of this method is that, for a single differential equation over an interval where the solution is monotonic, it exhibits an error proportional to the square of the integration quantum. We offer a theoretical proof of this behavior and demonstrate it with a numerical example. At the same time, an extension of the method to a system with input causes the errors to become proportional to the integration quantum. The latter observation fits established theory; the former appears to be a new result that might offer new insights into the construction of quantized state integration methods.

Mahawattege, Rasika↗

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗

Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Array↗

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

Hossain, Md Amjad↗

Techno-Economic Analysis and Optimization of a Compressed-Air Energy Storage System Integrated with a Natural Gas Combined-Cycle Plant

To address the rising electricity demand and greenhouse gas concentration in the environment, considerable effort is being carried out across the globe on installing and operating renewable energy sources. However, the renewable energy production is affected by diurnal and seasonal variability. To ensure that the electric grid remains reliable and resilient even for the high penetration of renewables into the grid, various types of energy storage systems are being investigated. In this paper, a compressed-air energy storage (CAES) system integrated with a natural gas combined-cycle (NGCC) power plant is investigated where air is extracted from the gas turbine compressor or injected back into the gas turbine combustor when it is optimal to do so. First-principles dynamic models of the NGCC plant and CAES are developed along with the development of an economic model. The dynamic optimization of the integrated system is undertaken in the Python/Pyomo platform for maximizing the net present value (NPV). NPV optimization is undertaken for 14 regions/cases considering year-long locational marginal price (LMP) data with a 1 h interval. Design variables such as the storage capacity and storage pressure, as well as the operating variables such as the power plant load, air injection rate, and air extraction rate, are optimized. Results show that the integrated CAES system has a higher NPV than the NGCC-only system for all 14 regions, thus indicating the potential deployment of the integrated system under the assumption of the availability of caverns in close proximity to the NGCC plant. The levelized cost of storage is found to be in the range of 136–145 $/MWh. Roundtrip efficiency is found to be between 74.6–82.5%. A sensitivity study with respect to LMP shows that the LMP profile has a significant impact on the extent of air injection/extraction while capital expenditure reduction has a negligible effect.

25 ENERGY STORAGE↗

INFRARED HIGH-RESOLUTION INTEGRATED LIGHT SPECTRAL ANALYSES OF M31 GLOBULAR CLUSTERS FROM APOGEE

Chemical abundances are presented for 25 M31 globular clusters (GCs), based on moderately high resolution ( R = 22,500) H -band integrated light (IL) spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Infrared (IR) spectra offer lines from new elements, lines of different strengths, and lines at higher excitation potentials compared to the optical. Integrated abundances of C, N, and O are derived from CO, CN, and OH molecular features, while Fe, Na, Mg, Al, Si, K, Ca, and Ti abundances are derived from atomic features. These abundances are compared to previous results from the optical, demonstrating the validity and value of IR IL analyses. The CNO abundances are consistent with typical tip of the red giant branch stellar abundances but are systematically offset from optical Lick index abundances. With a few exceptions, the other abundances agree between the optical and the IR within the 1 σ uncertainties. The first integrated K abundances are also presented and demonstrate that K tracks the α elements. The combination of IR and optical abundances allows better determinations of GC properties and enables probes of the multiple populations in extragalactic GCs. In particular, the integrated effects of the Na/O anticorrelation can be directly examined for the first time.

79 ASTRONOMY AND ASTROPHYSICS↗

An integrated approach to monitoring the calibration stability of operational dual-polarization radars

The stability of weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically calibration using the Sun and calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between dual-polarization power and phase measurements in rain. This technique allows a reference absolute value to be provided for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) for each radar scan, allowing the polarimetric variables to be monitored and hardware failures to promptly be recognized. The Sun calibration allows monitoring the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is applied using observations collected during the standard operational scans but requires long integration times (several days) in order to accumulate a sufficient amount of useful data. Finally, an intercalibration technique is developed and performed to compare colocated measurements collected in rain by two radars in overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July–October 2014. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. In conclusion, this is considered adequate to automatically detect any unexpected change in the radar system requiring further data analysis or on-site measurements.

54 ENVIRONMENTAL SCIENCES↗

Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model

Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

58 GEOSCIENCES↗

Wearable Sensor Application for Integrated Early Warning and Health Surveillance

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

Early Warning↗

FORMAC integration program: A special applications package used in developing techniques of orbital decay and long term ephemeris prediction for satellites in earth orbit

The symbolic manipulation capabilities of the FORMAC (Formula Manipulation Compiler) language are employed to expand and analytically evaluate integrals. The program integration is effected by expanding the integral(s) into a series of subintegrals and then substituting a pre-derived and pre-coded solution for that particular subintegral. Derivation of the integral solutions necessary for precoding is included, as is a discussion of the FORMAC system limitations encountered in the programming effort.

Rowe, C. K.↗

Telemetry and the integrated avionics approach.

The unique problems encountered in the integrated approach to aircraft and spacecraft avionics system designs are discussed with the emphasis on recent advances in telemetry as a factor facilitating the integration. A listing of subsystems planned for integration on the Space Shuttle vehicle is given, covering auxiliary pumping and propellant, crew controls and displays, computation, communications and radar, electric power, guidance, navigation and control, air-bearing engines, main engines, reaction control, and structural/mechanical elements. Considerations are given for the multiplexed data bus design applicable to integrated avionics.

Frost, W. O.↗

Space shuttle auxiliary propulsion system design study. Phase C report: Oxygen-hydrogen RCS/OMS integration study

A comparison of the concepts of auxiliary propulsion systems proposed for the space shuttle vehicle is discussed. An evaluation of the potential of integration between the reaction control system and the orbit maneuvering system was conducted. Numerous methods of implementing the various levels of integration were evaluated. Preferred methods were selected and design points were developed for two fully integrated systems, one partially integrated system, and one separate system.

Bruns, A. E.↗

CVT/PCS phase 1 integrated testing

Five breadboard experiments representing three Sortie Lab experiment disciplines were installed in a payload carrier simulator. A description of the experiments and the payload carrier simulator was provided. An assessment of the experiment interface with the simulator and an assessment of the simulator experiment support systems were presented. The results indicate that a hardware integrator for each experiment is essential; a crew chief, or mission specialist, for systems management and experimenter liaison is a vital function; a payload specialist is a practical concept for experiment integration and operation; an integration fixture for a complex experiment is required to efficiently integrate the experiment and carrier; simultaneous experiment utilization of simulator systems caused unexpected problems in meeting individual experiment requirements; experimenter traffic inside the dual-floor simulator did not hamper experiment operations; and the requirement for zero-g operation will provide a significant design challenge for some experiments.

Mcbrayer, R. O.↗

A survey of integrated optics with bibliography

The field of integrated optics is surveyed. Light coupling into films, modulation of light propagating in films, and current integrated optics developments and devices are discussed. It is concluded that integrated optics technology is expected to revolutionize the electronics industry as completely as did microcircuit technology. An extensive bibliography for the field of integrated optics is included.

Owen, R. B.↗