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Quantitative measurements of direct injection fuel spray density and mixing are difficult to achieve using optical diagnostics, due to the substantial scattering of light and high optical density of the droplet field. For multi-hole sprays, the problem is even more challenging, as it is difficult to isolate a single spray plume along a single line of sight. Time resolved x-ray radiography diagnostics developed at Argonne's Advanced Photon Source have been used for some time to study diesel fuel sprays, as x-rays have high penetrating power in sprays and scatter only weakly. Traditionally, radiography measurements have been conducted along any single line of sight, and have been applied to single-hole and group-hole nozzles with few plumes. In this new work, we extend the technique to multi-hole gasoline direct injection sprays.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

We consider the heat transfer characteristics of an ideal concentric disk used in the Wiped-Film Rotating-Disk (WFRD) evaporator for the Vapor Phase Catalytic Ammonia Removal (VPCAR) water recovery system. A mathematical model is derived to predict the radial temperature distribution and its average over the surface of the disk as a function of system parameters. The model shows self-similarity of the temperature distribution and the existence of a dimensionless parameter S (ratio of heat flux to convection) that can be used as a criterion to optimize the thermal characteristics of the disk in order to approach uniform surface temperature. Comparison of the model to experimental data using global (infrared imager) and local (resistive temperature devices) measurements shows that agreement with the model depends on the ambient condition denoted by the local heat transfer coefficient.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

Design, operations and maintenance activities in aviation involve analysis of variety of aviation data. This data is typically in disparate formats making it difficult to use with different software packages. Use of a self-describing and extensible standard called XML provides a solution to this interoperability problem. While self-describing nature of XML makes it easy to reuse, it also increases the size of data significantly. A natural solution to the problem is to compress the data using suitable algorithm and transfer it in the compressed form. We found that XML-specific compressors such as Xmill and XMLPPM generally outperform traditional compressors. However, optimal use of Xmill requires of discovery of optimal options to use while running Xmill. Manual discovery of optimal setting can require an engineer to experiment for weeks.

Thermal control systems for space application plant growth chambers offer unique challenges. The ability to control temperature and humidity independently gives greater flexibility for optimizing plant growth. Desired temperature and relative humidity range vary widely from 15°C to 35°C and 65% to 85% respectively. On top of all of these variables, the thermal control system must also be conservative in power and mass. These requirements to develop and test a robust thermal control system for space applications led to the design and development of the Environmental System Test Stand (ESTS) at NASA Johnson Space Center (JSC). The ESTS was designed to be a size constrained, environmental control system test stand with the flexibility to allow for a variety of thermal and lighting technologies. To give greater understanding to the environmental control system, the development of the ESTS included both mathematical models and the physical test stand.

An energy-efficient lyophilization technique is being developed to recover water from highly contaminated spacecraft waste streams. In the lyophilization process, water in an aqueous waste is frozen and then sublimed, separating the waste into a dried solid material and liquid water. This technology is ideally suited to applications such as the Mars Reference Mission, where water recovery rates approaching 100% are desirable but production of CO2 is not. Candidate wastes include feces, concentrated brines from water processors, and other solid wastes that contain water. To operate in microgravity, and to minimize power consumption, thermoelectric heat pumps can be used in place of traditional fluid cycle heat pumps. A mathematical model of a thermoelectric lyophilizer is described and used to generate energy use and processing rate estimates.

The quantitative analysis of the food system for long-term space missions is a crucial factor for the comparison of different food plans and for the evaluation of the food system as part of the overall mission. Such analysis should include important factors such as nutrition, palatability, diet cycle length, and psychological issues related to food. This paper will give the details of a mathematical model that was developed during the first author's participation as a Summer Faculty Fellow at Johnson Space Center. The model includes nutrition, palatability, diet cycle length, and psychological issues as important components. The model is compatible with the Equivalent System Mass (ESM) metric previously developed as the Advance Life Support (ALS) Research and Technology Metric.

Knock integrals and corresponding ignition delay (τ) correlations are often used in model-based control algorithms in order to predict ignition timing for kinetically modulated combustion regimes such as HCCI and PCCI. They can also be used to estimate knock-inception during conventional SI operation. The purpose of this study is to investigate the performance of various τ correlations proposed in the literature, including those developed based on fundamental data from shock tubes and rapid compression machines, those based on predictions from isochoric simulations using detailed chemical kinetic mechanisms, and those deduced from data of operating engines. A 0D engine simulation framework is used to compare the correlation performance where evaluations are based on the temperatures required at intake valve closure (TIVC) in order to achieve a fixed CA50 point over a range of conditions.

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory's (Argonne's) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles.

While spark-ignition (SI) engine technology is aggressively moving towards challenging (dilute and boosted) combustion regimes, advanced ignition technologies generating non-equilibrium types of plasma are being considered by the automotive industry as a potential replacement for the conventional spark-plug technology. However, there are currently no models that can describe the low-temperature plasma (LTP) ignition process in computational fluid dynamics (CFD) codes that are typically used in the multi-dimensional engine modeling community. A key question for the engine modelers that are trying to describe the non-equilibrium ignition physics concerns the plasma characteristics. A key challenge is also represented by the plasma formation timescale (nanoseconds) that can hardly be resolved within a full engine cycle simulation.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states.

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

Research committed by the Langley Research Center through 1995 resulting in the HZETRN code provides the current basis for shield design methods according to NASA STD-3000 (2005). With this new prominence, the database, basic numerical procedures, and algorithms are being re-examined with new methods of verification and validation being implemented to capture a well defined algorithm for engineering design processes to be used in this early development phase of the Bush initiative. This process provides the methodology to transform the 1995 HZETRN research code into the 2005 HZETRN engineering code to be available for these early design processes. In this paper, we will review the basic derivations including new corrections to the codes to insure improved numerical stability and provide benchmarks for code verification.

The NASA Johnson Space Center has plans to integrate a Solid Amine Water Desorbed (SAWD II) carbon dioxide removal subsystem into the Variable Pressure Growth Chamber (VPGC). The SAWD II subsystem will be used to remove any excess carbon dioxide (CO2) input into the VPGC which is not assimilated by the plants growing in the chamber. An analysis of the integrated VPGC-SAWD II system was performed using a mathematical model of the system implemented in the Computer-Aided System Engineering and Analysis (CASE/A) package. The analysis consisted of an evaluation of the SAWD II subsystem configuration within the VPGC, the planned operations for the subsystem, and the overall performance of the subsystem and other VPGC subsystems. Based on the model runs, recommendations were made concerning the SAWD II subsystem configuration and operations, and the chambers' automatic CO2 injection control subsystem.

In an earlier paper, the authors described how Model-Based System Engineering could be utilized to provide a virtual Hardware-in-the-Loop simulation capability, which creates a framework for the development of virtual ECU software by providing a platform upon which embedded control algorithms may be developed, tested, updated, and validated. The development of virtual ECU software is increasingly valuable in automotive control system engineering because vehicle systems are becoming more complex and tightly integrated, which requires that interactions between subsystems be evaluated during the design process. Variational analysis and robustness studies are also important and become more difficult to perform with real hardware as system complexity increases. The methodology described in this paper permits algorithm development to be performed prior to the availability of vehicle and control system hardware by providing what is essentially a virtual integration vehicle.

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster, simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods.

This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.