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High gradient allows rapid growth rates in directionally-solidified eutectic alloys. Furnace design permits cost reductions in directional solidification process through its increased solidification rates, which reduces melt/mold interaction. It produces structural engineering materials for any application requiring properties directionally-solidified eutectic materials.
Self-healing elastomers provide extended longevity of functional materials, due to their unique adaptability and durability. However, a major scientific challenge remains in developing materials with a rapid healing process combined with decent mechanical properties, that can be prepared by a relatively simple synthesis approach. Herein, we report a versatile design approach on self-healing elastomers by incorporating two different hydrogen bonding containing monomers, i.e., 2-[[(butylamino)carbonyl]oxy]ethyl acrylate (BCOE) and 2-ureido-4[1H]pyrimidinone (UPy) functionalized ethyl methacrylate. Poly(BCOE-r-UPy)s are synthesized by reversible addition-fragmentation chain-transfer (RAFT) polymerization, and controlling the ratio of two monomers enables well-tunable mechanical properties with tensile strength ranging from 0.04 to 6.3 MPa and tensile strain up to 3,000 %. The characteristic dissociation energy is calculated from a temperature dependence of terminal relaxation followed by subtracting the segmental relaxation. The rapid autonomous self-healing is achieved when the molar composition of Poly(BCOE-r-UPy) is tailored to BCOE/UPy = 99/1. The self-healing process is monitored in situ by a helium-ion microscope, and its macroscopic study using tensile tests indicates that Poly(BCOE-r-UPy1) with 1 % molar ratio of UPy recovers 70 % of its original toughness at ambient temperature within 10 mins. 3D printing of Poly(BCOE-r-UPy) affords a self-healable 3D structure, demonstrating the adaptability of Poly(BCOE-r-UPy) for on-demand fabrication. The simplicity of synthesis, well-tunable mechanical properties, unique self-healability, and 3D printing capability of Poly(BCOE-r-UPy)s indicate their potential for a range of applications.
Designing configurations for multicomponent distillation, a ubiquitous process in chemical and petrochemical industries, is often challenging. This is because, as the number of components increases, the number of admissible distillation configurations grows rapidly and these configurations vary substantially in their energy needs. Consequently, if a method could identify a few energy-efficient choices from this large set of alternatives, it would be extremely attractive to process designers. This paper develops here such a method by solving a Mixed Integer Nonlinear Program (MINLP) that is formulated to pick, among the regular-column configurations of Shah and Agrawal (2010b), those configurations that have a low vapor-duty requirement. To compute the minimum vapor-duty requirement for each column within the configuration, we use techniques that rely on the Underwood’s method. The combined difficulty arising from the nonlinearity of Underwood equations and the combinatorial explosion of the choice-set of alternatives poses unmistakable challenges for the branch-and-bound algorithm, the current method of choice to globally solve MINLPs. To address this difficulty, we exploit the structure of Underwood equations and derive valid cuts that expedite the convergence of branch-and-bound by enabling global solvers, such as BARON, infer tighter bounds on Underwood roots. This provides a quick way to identify a few lucrative alternative configurations for separation of a given non-azeotropic mixture. We illustrate the practicality of our approach on a case-study concerning heavy-crude distillation and on various other examples from the literature.
Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.
CAMRAD, program designed for rapid input and retrieval of all data used in engineering-to-manufacturing support operations, aids engineer by automating material-review-disposition process. Facilitates research of material-review histories and provides for efficient processing of material-review documents. Three SmartWare project files control operation. Written in Smart programming language and designed to run on IBM PC compatibles with SmartWave version 3.1.
Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a streamlined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to pro-ceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule, development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor downselection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measurements for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.
Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a stream-lined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to proceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule. Development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor down-selection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measure-ments for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.
Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01–0.1 M), nitric acid (0.1–4 M), and varying temperatures (20–45 °C). Here, spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO 3 concentration by leveraging UV–Vis–NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO 3 , and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.
A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.
The reactive multilayer thin films are a class of energetic materials that continue to attract attention for use in joining applications and as igniters. Generally composed of two reactants, these heterogeneous solids can be stimulated by an external source to promptly release stored chemical energy in a sudden emission of light and heat. In our critical review article, results from recent investigations of these materials are discussed. Discussion begins with a brief description of the vapor deposition techniques that provide accurate control of layer thickness and film composition. More than 50 reactive film compositions have been reported to date, with most multilayers fabricated by magnetron sputter deposition or electron-beam evaporation. In later sections, we review how multilayer ignition threshold, reaction rate, and total heat are tailored via thin film design. For example, planar multilayers with nanometer-scale periodicity exhibit rapid, self-sustained reactions with wavefront velocities up to 100 m/s. Numeric and analytical models have elucidated many of the fundamental processes that underlie propagating exothermic reactions while demonstrating how reaction rates vary with multilayer design. Recent, time-resolved diffraction and imaging studies have further revealed the phase transformations and the wavefront dynamics associated with propagating chemical reactions. Many reactive multilayers (e.g., Co/Al) form product phases that are consistent with published equilibrium phase diagrams, yet a few systems, such as Pt/Al, develop metastable products. The final section highlights current and emerging applications of reactive multilayers. Examples include reactive Ni(V)/Al and Pd/Al multilayers which have been developed for localized soldering of heat-sensitive components.
One of the simpler methods available to accomplish rapid solidification processing is free jet melt spinning. With only a modest expenditure of time, effort, and capital, an apparatus suitable for preliminary experimentation can be assembled. Wheel and crucible materials, process atmospheres, crucible design, heating methods, and process parameters and their relationship to melt composition are described. Practical solutions to processing problems, based on 'hands-on' experience, are offered. Alloys with melting points up to 3000 F have been rapidly solidified using the techniques described.
NASA's Advanced Mirror Technology Development (AMTD) program has been developing the means to design and build the future generations of space based telescopes. With the nearing completion of the James Webb Space Telescope (JWST), the astrophysics community is already starting to define the requirements for follow on observatories. The restrictions of available launch vehicles and the possibilities of planned future vehicles have fueled the competition between monolithic primaries (with better optical quality) and segmented primaries (with larger apertures, but with diffraction, costs and figure control issues). Regardless of the current shroud sizes and lift capacities, these competing architectures share the need for rapid design tools. As part of the AMTD program a number of tools have been developed and tested to speed up the design process. Starting with the Arnold Mirror Modeler (which creates Finite Element Models (FEM) for structural analysis) and now also feeds these models into thermal stability analyses. They share common file formats and interchangeable results. During the development of the program, numerous trade studies were created for 4 meter and 8 meter monolithic primaries, complete with support systems. Evaluation of these results has led to a better understanding of how the specification drives the results. This paper will show some of the early trade studies for typical specification requirements such as lowest mirror bending frequency and suspension system lowest frequency. The results use representative allowable stress values for each mirror substrate material and construction method and generic material properties. These studies lead to some interesting relationships between feasible designs and the realities of actually trying to build these mirrors. Much of the traditional specifications were developed for much smaller systems, where the mass and volume of the primary where a small portion of the overall satellite. JWST shows us that as the aperture grows, the primary takes up the majority of the mass and volume and the established rules need to be adjusted. For example, a small change in lowest frequency requirement can change the cost by millions of dollars.
Based on guidelines from the Federal Aviation Administration, research supported by the NASA Advanced Composites Project is investigating methods to improve process control for surface preparation and pre-bond surface characterization on aerospace composite structures. The overall goal is to identify high fidelity, rapid, and reproducible surface treatments and surface characterization methods to reduce the uncertainty associated with the bonding process. The desired outcome is a more reliable bonded airframe structure, and to reduce time to achieve certification. In this work, a design of experiments (DoE) approach was conducted to determine optimum laser ablation conditions using a pulsed laser source with a nominal pulse width of 10 picoseconds. The laser power, frequency, scan speed, and number of passes (1 or 2) were varied within the laser system operating boundaries. Aerospace structural carbon fiber reinforced composites (Torayca® 3900-2/T800H) were laser treated, then characterized for contamination, and finally bonded for mechanical testing. Pre-bond characterization included water contact angle (WCA) using a handheld device, ablation depth measurement using scanning electron microscopy (SEM), and silicone contamination measurement using laser induced breakdown spectroscopy (LIBS). In order to accommodate the large number of specimens in the DoE, a rapid-screening, double cantilever beam (DCB) test specimen configuration was devised based on modifications to ASTM D5528. Specimens were tested to assess the failure modes observed under the various laser surface treatment parameters. The models obtained from this DoE indicated that results were most sensitive to variation in the average laser power. Excellent bond performance was observed with nearly 100% cohesive failure for a wide range of laser parameters. Below about 200 mW, adhesive failure was observed because contamination was left on the surface. For laser powers greater than about 600 mW, large amounts of fiber were exposed, and the failure mode was predominately fiber tear.
NASA's Advanced Mirror Technology Development (AMTD) program has been developing the means to design and build the future generations of space based telescopes. With the nearing completion of the James Webb Space Telescope (JWST), the astrophysics community is already starting to define the requirements for follow-on observatories. The restrictions of available launch vehicles and the possibilities of planned future vehicles have fueled the competition between monolithic primaries (with better optical quality) and segmented primaries (with larger apertures, but with diffraction, costs and figure control issues). Regardless of the current shroud sizes and lift capacities, these competing architectures share the need for rapid design tools. As part of the AMTD program a number of tools have been developed and tested to speed up the design process. Starting with the Arnold Mirror Modeler (which creates Finite Element Models (FEM) for structural analysis) and now also feeds these models into thermal stability analyses. They share common file formats and interchangeable results. During the development of the program, numerous trade studies were created for 4-meter and 8-meter monolithic primaries, complete with support systems. Evaluation of these results has led to a better understanding of how the specification drives the results. This paper will show some of the early trade studies for typical specification requirements such as lowest mirror bending frequency and suspension system lowest frequency. The results use representative allowable stress values for each mirror substrate material and construction method and generic material properties. These studies lead to some interesting relationships between feasible designs and the realities of actually trying to build these mirrors. Much of the traditional specifications were developed for much smaller systems, where the mass and volume of the primary where a small portion of the overall satellite. JWST shows us that as the aperture grows, the primary takes up the majority of the mass and volume and the established rules need to be adjusted. For example, a small change in lowest frequency requirement can change the cost by millions of dollars. The paper uses numerous trade studies created during the software development phase of the Arnold Mirror Modeler to illustrate the influences of system specifications on the design space. The future telescopes will require better performance, stability and documented feasibility to meet the hurdles of today's budget and schedules realities. AMTD is developing the tools, but the basic system planning mentality also has to adopt to the requirements of these very large and complex physical structures.
The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.
The human computer interface (HCI) prototyping environment is designed to allow developers to rapidly prototype systems so that the interface and functionality of a system can be evaluated and iteratively refined early in the development process. This keeps development costs down by modifying the interface during the requirements definition phase, thus minimizing changes that need to be made during and after flight code development. Problems occur within a system when the user interface is not adequately developed and when designers and developers have an incomplete understanding of the system requirements. A process has been developed for prototyping on-board payload displays for Space Station Freedom. This prototyping process consists of five phases: identification of known requirements, analysis of the requirements, development of a formal design representation and specification, development of the prototype, and evaluation of the prototype. The actual development of the prototype involves prototyping the displays, developing a low fidelity simulator, building of an interface (or communication) between the displays and the simulator, integration of these components, and testing to ensure that the interface does what the developer expects. This research integrates and evaluates a software tool which has been developed to serve as a simulator within the prototyping environment. The tool is being evaluated to determine whether or not it meets the basic requirements which are needed for a low fidelity simulator within this environment. In order to evaluate the architecture and its components, a human computer interface for and a simulator of an automobile have been developed as a prototype. The individual components (i.e., the interface and simulator) have been developed, and the current research was designed to integrate and test the complete working system within the prototyping environment. The following sections will describe the architecture and components of the rapid prototyping environment, the development of a system to assess the environment, and the integration and evaluation of PERCNET.
There is a strong rationale for increasing the rate of information return from imaging class experiments aboard both terrestrial and planetary spacecraft. Future imaging systems will be designed with increased spatial resolution, broader spectral range and more spectral channels (or higher spectral resolution). The data rate implied by these improved performance characteristics can be expected to grow more rapidly than the projected telecommunications capability. One solution to this dilemma is the use of improved onboard data processing. The use of onboard classification processing in a multispectral imager can result in orders of magnitude increase in information transfer for very specific types of imaging tasks. Several of these processing functions are included in the conceptual design of an Infrared Multispectral Imager which would map the spatial distribution of characteristic geologic features associated with deposits of economic minerals.
Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES‐CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open‐source platform for optimization of process flowsheets utilizing state‐of‐the‐art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure‐based conceptual design and optimization under uncertainty.