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Cost and performance requirements are driving military and commercial systems to become highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits of those complex controls and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop plant models and determine the uncertainty of those models to predict performance and stability of the control algorithms. A process of model and control algorithm validation for a dc-dc boost converter circuit based on acceptance sampling is presented here. The validation process described in this paper is based on MIL-STD 3022 with emphasis on requirements settings and the testing process. The key contribution of this paper is the process for model and control algorithm validation, specifically a method for decomposing the problem into model and control algorithm validation stages.

This paper introduces a method for conducting experimental hardware-in-the-loop (xHIL), in which behavioral-level models are coupled with an advanced power emulator (APE) to emulate an electrical load on a power generation system. The emulator is commanded by behavioral-level models running on an advanced real-time simulator that has the capability to leverage Central Processing Units (CPUs) and field programmable gate arrays (FPGA) to meet strict real-time execution requirements. The paper will be broken down into four topics: 1) the development of a solution to target behavioral-level models to an advanced, real-time simulation device, 2) the development of a high-bandwidth, high-power emulation capability, 3) the integration of the real-time simulation device and the APE, and 4) the application of the emulation system (simulator and emulator) in an xHIL experiment.

This paper presents the details of an engine emulation system utilized within a Hardware-in-the-Loop (HIL) test environment for aircraft power systems. The paper focuses on the software and hardware interfaces that enable the coupling of the engine model and the generator hardware. In particular, the rotor dynamics model that provides the critical link between the modeled dynamics of the engine and the measured dynamics of the generator is described in detail. Careful consideration for the measured torque is included since the measurement contains inertial effects as well as torsional resonances. In addition, the rotor model is equipped with the ability to apply power and speed scaling between the engine and generator.

The cost and complexity of aircraft power systems limit the number of integrated system evaluations that can be performed in hardware. As a result, evaluations are often performed using emulators to mimic components or subsystems. As an example, aircraft generation systems are often tested using an emulator that consists of a bank of resistors that are switched to represent the power draw of one or more actuators. In this research, consideration is given to modern wide bandwidth emulators (WBEs) that use power electronics and digital controls to obtain wide bandwidth control of power, current, or voltage. Specifically, this paper first looks at how well a WBE can emulate the impedance of a load when coupled to a real-time model. Capturing the impedance of loads and sources is important for accurately assessing the small-signal stability of a system.

Increases in on-board heat generation in modern military aircraft have led to a reliance on thermal management techniques using fuel as a primary heat sink. However, recent studies have found that fuel properties, such as specific heat, can vary greatly between batches, affecting the amount of heat delivered to the fuel. With modern aircraft systems utilizing the majority of available heat sink capacity, an improved understanding of the effects of fuel property variability on overall system response is important. One way to determine whether property variability inside a thermal system causes failure is to perform uncertainty analyses on fuel thermophysical properties and compare results to a risk assessment metric. A sensitivity analysis can be performed on any properties that cause inherent system variability to determine which properties contribute the most significant impact.

Although computational fluid dynamics (CFD) simulations have been widely used to successfully resolve turbulence and boundary layer phenomena induced by microscale flow passages in advanced heat exchanger concepts, the expense of such simulations precludes their use within system-level models. However, the effect of component design changes on systems must be better understood in order to optimize designs with little thermal margin, and CFD simulations greatly enhance this understanding. A method is presented to introduce high resolution, 3-D conjugate CFD calculations of candidate heat exchanger cores into dynamic aerospace subsystem models. The significant parameters guiding performance of these heat exchangers are identified and a database of CFD solutions is built to capture steady and unsteady performance of microstructured heat exchanger cores as a function of the identified parameters and flow conditions.

The thermal management of modern aircraft has become more challenging as aircraft capabilities have increased. The use of thermally resistant composite skins and the desire for low observability, reduced ram inlet size and number, have reduced the ability to transfer heat generated by the aircraft to the environment. As the ability to remove heat from modern aircraft has decreased, the heat loads associated with the aircraft have increased. Early in the aircraft design cycle uncertainty exists in both aircraft requirements and simulation predictions. In order to mitigate the uncertainty, it is advantageous to design thermal management systems that are insensitive to design cycle uncertainty. The risk associated with design uncertainty can be reduced through robust optimization. In the robust optimization of the thermal management system, three noise factors were selected: 1) engine fan air temperature, 2) avionics thermal load, and 3) engine thrust.

Movement toward more-electric architectures in military and commercial airborne systems has led to electrical power systems (EPSs) with complex power flow dynamics and advanced technologies specifically designed to improve power quality in the system. As such, there is a need for tools that can quickly analyze the impact of technology insertion on the system-level dynamic transient and spectral power quality and assess tradeoffs between impact on power quality versus weight and volume. Traditionally, this type of system level analysis is performed through computationally intensive time-domain simulations involving high fidelity models or left until the hardware fabrication and integration stage. In order to provide a more rapid analysis prior to hardware development and integration, stochastic equivalent circuit analysis is developed that can provide power quality assessment directly in the frequency domain.

Analyzing and maintaining power quality in an electrical power system (EPS) is essential to ensure that power generation, distribution, and loads function as expected within their designated operating regimes. Standards such as MIL-STD-704 and associated documents provide the framework for power quality metrics that need to be satisfied under varying operating conditions. However, analyzing these power quality metrics within a fully integrated EPS based solely on measurements of relevant signals is a different challenge that requires a separate framework containing rules for data acquisition, metric calculations, and applicability of metrics in certain operating conditions/modes. Many EPS employed throughout industry and government feature various alternating-current (ac) power systems.

Validation is a critical component of model-based design (MBD). Without it, regardless of the level of model verification, neither the accuracy nor the domain of applicability of the models is known. Thus, it is risky to base design decisions on the predictions of unvalidated models. The Integrated Vehicle Energy Technology (INVENT) program is planning a series of hardware experiments that will be used to validate a large set of unit-, subsystem-, and system-level models. Although validating such a large number of interacting models is a large task, it provides an excellent opportunity to test the limits of MBD.

Advancement in modern aircraft with the development of more dynamic and efficient technologies has led to these technologies increasingly operated near or at their operation limits. More comprehensive analysis methods based on high-fidelity models co-simulated in an integrated environment are needed to support the full utilization of these advanced technologies. Furthermore, the additional information provided by these new analyses needs to be correlated with updates to traditional metrics and specifications. One such case is the thermal limit requirement that sets the upper bound on a thermal system temperature. Traditionally, this bound is defined based on steady-state conditions. However, advanced thermal management systems experience dynamic events where the temperature is not static and may violate steady-state requirements for brief periods of time.

An IPTMS hardware facility has been established in the laboratories of the Aerospace Systems Directorate of the Air Force Research Laboratory (AFRL) at Wright-Paterson Air Force Base (WPAFB). This hardware capability was established to analyze the transient behavior of a high power Electrical Power System (EPS) coupled virtually to a Thermal Management System (TMS) under fast dynamic loading conditions. The system incorporates the use of dynamic electrical load, engine emulation, energy storage, and emulated thermal loads operated to investigate dynamics under step load conditions. Hardware architecture and control options for the IPTMS are discussed. This paper summarizes the IPTMS laboratory demonstration system, its capabilities, and preliminary test results.

Advancements in electrical, mechanical, and structural design onboard modern more electric aircraft have added significant stress to the electrical systems. An electrical system level analysis tool has been created in MATLAB/Simulink to facilitate rapid system analysis and optimization to meet the growing demands of modern aircraft. An integratated model of segment level models of an electrical system including a generator, electrical accumulator unit, electrical distribution unit and electromechanical actuators has been developed. Included in the model are mission level models of an engine and aircraft to provide relevant boundary conditions. It is anticipated that the tracking of the electrical distribution through numerical integration of these various subsystems will lead to more accurate predictions of the bus power quality. In this paper the tool is used to evaluate two architectures using two different load profiles.

Experimental Hardware-in-the-loop (xHIL) testing utilizing signal and/or power emulation imposes a hard real-time requirement on models of emulated subsystems, directly limiting their fidelity to what can be achieved in real-time on the available computational resources. Most real-time simulators are CPU-based, for which the overhead of an instruction-set architecture imposes a lower limit on the simulation step size, resulting in limited model bandwidth. For power-electronic systems with high-frequency switching, this limit often necessitates using average-value models, significantly reducing fidelity, in order to meet the real-time requirement. An alternative approach emerging recently is to use FPGAs as the computational platform, which, although offering orders-of-magnitudes faster execution due to their parallel architecture, they are more difficult to program and their limited fabric space bounds the size of models that can be simulated.

Next generation aircraft will require more electrical power, more thermal cooling, and better versatility. To attain these improvements, technologies will need to be integrated and optimized at a system-level. The complexity of these integrated systems will require considerable analysis. In order to characterize and understand the implications of highly-integrated aircraft systems, the effects of pulsed-power, highly-transient loads, and the technologies that drive system-stability and behavior, an approach will be taken utilizing integrated modeling and simulation with hardware-in-the-loop (HIL). Such experiments can save time and cost and increase the general understanding of electrical and thermal phenomena as it pertains to aircraft systems before completing an integrated ground demonstration. As a first step toward completing an integrated analysis, a dynamometer “drive stand” was characterized to assess its performance.

In this research, excitation strategies for a salient-pole wound rotor synchronous machine are explored using a magnetic equivalent circuit model that includes core loss. It is shown that the excitation obtained is considerably different than would be obtained using traditional qd-based models. However, through evaluation of the resulting ‘optimal’ excitation, a relatively straightforward field-oriented type control is developed that is consistent with a desire for efficiency yet control simplicity. Validation is achieved through hardware experiment. The usefulness/applicability of the simplified control to variable speed applications is then considered.

The diverse and complex requirements of next-generation energy optimized aircraft (EOA) demand detailed transient and dynamic model-based design (MBD) to ensure the proper operation of numerous interconnected and interacting subsystems across multiple disciplines. In support of the U.S. Air Force's Integrated Vehicle Energy Technology (INVENT) program, several MBD-derived software tools, including models of EOA technologies, have been developed. To validate these models and demonstrate the performance of EOA technologies, a series of Integrated Ground Demonstration (IGD) hardware tests are planned. Several of the numerous EOA software tools and MBD-based processes have been updated and adapted to support this activity.

A reduced order dynamic aircraft model has been created for the purpose of enabling constructive simulation studies involving integrated thermal management subsystems. Such studies are motivated by the increasing impact of on-board power and thermal subsystems to the overall performance and mission effectiveness of modern aircraft. Previous higher-order models that have been used for this purpose have the drawbacks of much higher development time, along with much higher execution times in the simulation studies. The new formulation allows for climbs, accelerations and turns without incurring computationally expensive stability considerations; a dynamic inversion control law provides tracking of user-specified mission data. To assess the trade-off of improved run-time performance against model capability, the reduced order formulation is compared to a traditional six degree-of-freedom model of the same air vehicle.

Future more electric aircraft (MEA) architectures that improve electrical power system's (EPS's) source and load utilization will require advance stability analysis capabilities. Systems are becoming more complex with bidirectional flows from power regeneration, multiple sources per channel and higher peak to average power ratios. Unknown load profiles with large transients complicate common stability analysis techniques. Advancements in analysis are critical for providing useful feedback to the system integrator and designers of multi-source, multi-load power systems. Overall, a framework for evaluating stability with large displacement events has been developed. Within this framework, voltage transient bounds are obtained by identifying the worst case load profile. The results can be used by system designers or integrators to provide specifications or limits to suppliers. Subsystem suppliers can test and evaluate their design prior to integration and hardware development.

With the advent of modern parallel computing systems, larger and more accurate simulation models have been developed to simulate real-world hardware. These models require verification and validation (V&V), the latter using data acquired from representative hardware to ascertain the uncertainty of the model. An understanding of the errors introduced by the measurement system into the validation assessment allows for the model assessor to attribute errors to the measurement system as opposed to the model or experimental setup. Once the model(s) have been through the validation process, decision makers can better understand the risk associated with using these models. This paper describes one possible procedure to quantify the uncertainty of the data acquisition (DAQ) system.