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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

On-the-shaft data systems for rotating engine components

Two rotating data systems for engine component testing, which demonstrate the techniques of on-the-shaft microelectronic signal conditioning and rotary transformer power- and data-transfer, are described. These systems are a digital data system which provides 69 channels of 1100 C maximum thermocouple data with less than 0.5% error from a turbine test rig rotating at speeds up to 9000 rpm and an analog data system which amplifies and transfers 72 channels of dynamic strain data with less than 5% error from a compressor rig at speeds above 14,000 rpm.

Lesco, D. J.↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT - Poster

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRS1972

Digital-computer normal shock position and restart control of a Mach 2.5 axisymmetric mixed-compression inlet

Digital computer control of a mixed-compression inlet is discussed. The inlet was terminated with a choked orifice at the compressor face station to dynamically simulate a turbojet engine. Inlet diffuser exit airflow disturbances were used. A digital version of a previously tested analog control system was used for both normal shock and restart control. Digital computer algorithms were derived using z-transform and finite difference methods. Using a sample rate of 1000 samples per second, the digital normal shock and restart controls essentially duplicated the inlet analog computer control results. At a sample rate of 100 samples per second, the control system performed adequately but was less stable.

Neiner, G. H.↗
NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗
NASA NTRS2004

Low-Cost, User-Friendly, Rapid Analysis of Dynamic Data System Established

An issue of primary importance to the development of new jet and certain other airbreathing combined-cycle powered aircraft is the advancement of airframe-integrated propulsion technologies. Namely, engine inlets and their systems and subsystems are required to capture, convert, and deliver the atmospheric airflow demanded by such engines across their operating envelope in a form that can be used to provide efficient, stable thrust. This must be done while also minimizing aircraft drag and weight. Revolutionary inlet designs aided by new technologies are needed to enable new missions. An unwanted byproduct of pursuing these inlet technologies is increased time-variant airflow distortion. Such distortions reduce propulsion system stability, performance, operability, and life. To countermand these limitations and fully evaluate the resulting configurations, best practices dictate that this distortion be experimentally measured at large scale and analyzed. The required measurements consist of those made by an array of high-response pressure transducers located in the flow field at the aerodynamic interface plane (AIP) between the inlet and engine. Although the acquisition of the necessary pitot-pressure time histories is relatively straight-forward, until recent years, the analysis has proved to be very time-consuming, tedious, and expensive. To transform the analysis of these data into a tractable and timely proposition, researchers at the NASA Glenn Research Center created and established the Rapid Analysis of Dynamic Data (RADD) system. The system provides complete, near real-time analysis of time-varying inlet airflow distortion datasets with report quality output. This fully digital approach employs Institute of Electrical and Electronics Engineers (IEEE) binary data file format standardization to establish data-acquisition-system-independent processing on low cost personal computers. Features include invalid instrumentation code-out, logging, and multiple replacement schemes as needed for each channel of instrumentation. The AIP pressure distribution can be interpolated to simulate measurements by alternate AIP probe arrays, if desired. In addition, the RADD system provides for the application of filters that can be used to focus the analysis on the frequency range of interest.

Arend, David J.↗
NASA NTRS2024

NASA Methodology for Physics of Failure-Based Reliability Assessments Handbook

At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to develop and share best practices and methodologies across NASA. One such cutting edge methodology is Physics of Failure. Therefore NASA OSMA's R&M office is developing a Reliability Physics of Failure Handbook in order to keep our engineers current and on the cutting edge of analysis methodologies to help ensure NASA’s optimize and achieve continued success.

Reliability Engineering↗
NASA NTRS2023

Harnessing the Digital Transformation for Development of Electrified Aircraft Propulsion Control Systems

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion. The three MATLAB®-based software packages are the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and the Thermal Systems Analysis Toolbox (TSAT). These tools are interactive, complementary, and compatible with each other. T-MATS is a modular thermodynamic modeling framework designed for creating custom component level models of jet engines. EMTAT is a modeling framework used to simulate a variety of power electronic devices, using both physics-based and power flow calculations. TSAT is a framework for modeling and analysis of dynamic heat transfer. These packages all consist of graphical, drag-and-drop, parameterizable building blocks representing various components of the system to be modeled, e.g., compressors, turbines, motors, energy storage devices, etc. They are designed to enable the user to model and simulate the end-to-end dynamic operation of a hybrid electric gas turbine engine powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper demonstrates through multiple examples how these tools have been used successfully in a variety of applications, including several of the early stages of hybrid electric gas turbine engine propulsion system development, from the initial system modeling to real-time interactive pilot-in-the-loop simulation to physical hardware-in-the-loop testing, each step bringing the technology closer to fruition.

hybrid electric propulsion↗
NASA NTRS1991

System Decommutes And Displays Telemetry Data

TDPlus computer program software system for decommutation of pulse-code-modulation (PCM) telemetry signals. Provides synchronization, conversion into engineering units, and display of serial bit streams. Transforms IBM PC-compatible computer into PCM-telemetry-decommutation system. Synchronizes telemetric signals data and enables conversion back into such meaningful forms as voltage, current, pressure, and the like. Also controls operation of digital-to-analog converters to ship data to paper strip charts or to parallel digital ports for offloading to other computers. Software used to process actual data only when telemetry-data-processing computer modified in accordance with specifications contained in "TDPlus TM Data Processor" (GSC-13291). Written in Turbo C and 8088 Assembly language.

Massey, D. E.↗
NASA NTRS2016

Multidisciplinary Model Transformation Through Simplified Intermediate Representations

There has long been a challenge of making engineering tools from multiple disciplines interoperate. This problem extends to system modeling practices. This challenge has been confronted with a wide variety of techniques. These techniques include attempting to interface tools together into combined suites, attempting to find underlying commonalities in mathematics, supporting connections through semantic encoding, various graph mappings and transformations, and code wrappers. All of these approaches have strengths and weaknesses. These are measured in multiple areas: relative freedom of action of individual domain engineers in developing their own tools, speed of execution, ease of creation, traceability, fidelity of information transfer, and degree of alignment between the concepts of different domains. This paper presents an approach to this interoperation problem currently being used in the World-Wide Web. The approach is to develop easy-to-parse formats that allow flexibility to both the file author and file interpreter. Many of the formats that are currently deployed sacrifice runtime performance for the ability of third parties to easily understand what to do with the data. XML became popular earlier as a de-facto standard format for many web applications, but is now being replaced by JSON to enhance human readability and provide a simpler data model. This is the basis for work in this paper. Our approach, which provides the key to interoperation, is a simplified “shrapnel” intermediate collection of objects and relationships that is the result of a breakdown of the system model into minimal pieces. It is then reassembled on the destination side, forming a two-step transformation. Previous efforts with single-step transformations have proven too difficult to create efficiently. In contrast, the use of this approach leads to an almost automatic procedure for transformation development. The Europa project is a large engineering project that must coordinate the efforts of many different teams with different specialties. The traditional form of exchanging engineering information has been documentation. The vision of model-based systems engineering is to make this information exchange much more digital. This paper presents the application of our simplified format to connecting two different engineering tools to the system model, with a focus on a dynamic mission simulation encoded in Modelica.

Cole, Bjorn↗
NASA NTRS2019

2040 Vision Study: an Enlargement of Model Based Engineering

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will briefly review the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations). The study, NASA CR 2018- 219771, envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that marries two non-mutually exclusive paradigms _ "design of the materials" (material scientist viewpoint) and "design with the materials" (structural analyst viewpoint) _ into a concurrent transformational paradigm that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.

Arnold, Steven M.↗
NASA NTRS2004

More About the Phase-Synchronized Enhancement Method

A report presents further details regarding the subject matter of "Phase-Synchronized Enhancement Method for Engine Diagnostics" (MFS-26435), NASA Tech Briefs, Vol. 22, No. 1 (January 1998), page 54. To recapitulate: The phase-synchronized enhancement method (PSEM) involves the digital resampling of a quasi-periodic signal in synchronism with the instantaneous phase of one of its spectral components. This resampling transforms the quasi-periodic signal into a periodic one more amenable to analysis. It is particularly useful for diagnosis of a rotating machine through analysis of vibration spectra that include components at the fundamental and harmonics of a slightly fluctuating rotation frequency. The report discusses the machinery-signal-analysis problem, outlines the PSEM algorithms, presents the mathematical basis of the PSEM, and presents examples of application of the PSEM in some computational simulations.

Jong, Jen-Yi↗
NASA NTRSDate not supplied

2040 Vision Study: NASA’s T3 Implementation Activities

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. As a result, NASA’s Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will briefly review the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations) and discuss NASA’s TTT implementation activities, with special emphasis on recent accomplishments. The study, NASA CR 2018- 219771, envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that marries two non-mutually exclusive paradigms – “design of the materials” (material scientist viewpoint) and “design with the materials” (structural analyst viewpoint) – into a concurrent transformational paradigm that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT’s hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United State's global competitiveness

Materials↗
NASA NTRS2008

An Overview of Starfish: A Table-Centric Tool for Interactive Synthesis

Engineering is an interactive process that requires intelligent interaction at many levels. My thesis [1] advances an engineering discipline for high-level synthesis and architectural decomposition that integrates perspicuous representation, designer interaction, and mathematical rigor. Starfish, the software prototype for the design method, implements a table-centric transformation system for reorganizing control-dominated system expressions into high-level architectures. Based on the digital design derivation (DDD) system a designer-guided synthesis technique that applies correctness preserving transformations to synchronous data flow specifications expressed as co- recursive stream equations Starfish enhances user interaction and extends the reachable design space by incorporating four innovations: behavior tables, serialization tables, data refinement, and operator retiming. Behavior tables express systems of co-recursive stream equations as a table of guarded signal updates. Developers and users of the DDD system used manually constructed behavior tables to help them decide which transformations to apply and how to specify them. These design exercises produced several formally constructed hardware implementations: the FM9001 microprocessor, an SECD machine for evaluating LISP, and the SchemEngine, garbage collected machine for interpreting a byte-code representation of compiled Scheme programs. Bose and Tuna, two of DDD s developers, have subsequently commercialized the design derivation methodology at Derivation Systems, Inc. (DSI). DSI has formally derived and validated PCI bus interfaces and a Java byte-code processor; they further executed a contract to prototype SPIDER-NASA's ultra-reliable communications bus. To date, most derivations from DDD and DRS have targeted hardware due to its synchronous design paradigm. However, Starfish expressions are independent of the synchronization mechanism; there is no commitment to hardware or globally broadcast clocks. Though software back-ends for design derivation are limited to the DDD stream-interpreter, targeting synchronous or real-time software is not substantively different from targeting hardware.

Tsow, Alex↗
NASA NTRS1977

Electric prototype power processor for a 30cm ion thruster

An electrical prototype power processor unit was designed, fabricated and tested with a 30 cm mercury ion engine for primary space propulsion. The power processor unit used the thyristor series resonant inverter as the basic power stage for the high power beam and discharge supplies. A transistorized series resonant inverter processed the remaining power for the low power outputs. The power processor included a digital interface unit to process all input commands and internal telemetry signals so that electric propulsion systems could be operated with a central computer system. The electrical prototype unit included design improvement in the power components such as thyristors, transistors, filters and resonant capacitors, and power transformers and inductors in order to reduce component weight, to minimize losses, and to control the component temperature rise. A design analysis for the electrical prototype is also presented on the component weight, losses, part count and reliability estimate. The electrical prototype was tested in a thermal vacuum environment. Integration tests were performed with a 30 cm ion engine and demonstrated operational compatibility. Electromagnetic interference data was also recorded on the design to provide information for spacecraft integration.

Biess, J. J.↗
NASA NTRS2020

Virtual and Augmented Reality Applications in Science and Engineering

The articles in this special section focus on virtual and augmented reality applications in science and engineering. There has been an explosive growth in visually augmenting the spaces around us by creating environments where visual, aural, and kinesthetic immersive experiences afforded by virtual and augmented reality (AR) powerfully engage us in a way no other medium can. Virtual reality (VR) recreates the sensory world around us entirely through computer-generated signals of sight, sound, touch (and in some cases smell and taste). AR overlays the computer-generated sensory signals on the real world allowing the user to experience a rich juxtaposition of the virtual and the real worlds simultaneously. Together, these technologies are transforming the way people from all walks of life—scientists, engineers, educators, industrial workers, health care professionals, artists, and everyday people— see and use the information that matters most to them, in an intuitive embodied way. Just as mobile technology has revolutionized how we communicate with each other and with our digital worlds, ubiquitous VR and AR will fundamentally alter how our society creates, inspires, engages, and learns from the information-rich and enriched cyberspaces around us. While affordable consumer- quality VR and AR hardware is becoming available, significant work is needed to adopt VR and AR for important and difficult scientific and societal applications.

Virtual Reality, Augmented Reality↗
NASA NTRSDate not supplied

Cloud Computing Option for Modeling the Debris Environment

NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies in their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Featuresexclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.

Andrew Vavrin↗