Independent Orbiter Assessment (IOA): Analysis of the rudder/speed brake subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results for the Orbiter Rudder/Speedbrake Actuation Mechanism is documented. The function of the Rudder/Speedbrake (RSB) is to provide directional control and to provide a means of energy control during entry. The system consists of two panels on a vertical hinge mounted on the aft part of the vertical stabilizer. These two panels move together to form a rudder but split apart to make a speedbrake. The Rudder/Speedbrake Actuation Mechanism consists of the following elements: (1) Power Drive Unit (PDU) which is composed of hydraulic valve module and a hydraulic motor-powered gearbox which contains differentials and mixer gears to provide PDU torque output; (2) four geared rotary actuators which apply the PDU generated torque to the rudder/speedbrake panels; and (3) ten torque shafts which join the PDU to the rotary actuators and interconnect the four rotary actuators. Each level of hardware was evaluated and analyzed for possible failures and causes. Criticality was assigned based upon the severity of the effect for each failure mode. Critical RSB failures which result in potential loss of vehicle control were mainly due to loss of hydraulic fluid, fluid contaminators, and mechanical failures in gears and shafts

Independent Orbiter Assessment (IOA): Analysis of the body flap subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items (PCIs). To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results for the Orbiter Body Flap (BF) subsystem hardware are documented. The BF is a large aerosurface located at the trailing edge of the lower aft fuselage of the Orbiter. The proper function of the BF is essential during the dynamic flight phases of ascent and entry. During the ascent phase of flight, the BF trails in a fixed position. For entry, the BF provides elevon load relief, trim control, and acts as a heat shield for the main engines. Specifically, the BF hardware comprises the following components: Power Drive Unit (PDU), rotary actuators, and torque tubes. The IOA analysis process utilized available BF hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 35 failure modes analyzed, 19 were determined to be PCIs

Independent Orbiter Assessment (IOA): Analysis of the ascent thrust vector control actuator subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results for the Ascent Thrust Vector Control (ATVC) Actuator hardware are documented. The function of the Ascent Thrust Vector Control Actuators (ATVC) is to gimbal the main engines to provide for attitude and flight path control during ascent. During first stage flight, the SRB nozzles provide nearly all the steering. After SRB separation, the Orbiter is steered by gimbaling of its main engines. There are six electrohydraulic servoactuators, one pitch and one yaw for each of the three main engines. Each servoactuator is composed of four electrohydraulic servovalve assemblies, one second stage power spool valve assembly, one primary piston assembly and a switching valve. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Critical failures resulting in loss of ATVC were mainly due to loss of hydraulic fluid, fluid contamination and mechanical failures

Independent Orbiter Assessment (IOA): Analysis of the elevon subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results for the Orbiter Elevon system hardware. The elevon actuators are located at the trailing edge of the wing surface. The proper function of the elevons is essential during the dynamic flight phases of ascent and entry. In the ascent phase of flight, the elevons are used for relieving high wing loads. For entry, the elevons are used to pitch and roll the vehicle. Specifically, the elevon system hardware comprises the following components: flow cutoff valve; switching valve; electro-hydraulic (EH) servoactuator; secondary delta pressure transducer; bypass valve; power valve; power valve check valve; primary actuator; primary delta pressure transducer; and primary actuator position transducer. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 25 failure modes analyzed, 18 were determined to be PCIs

Regularization of Inverse Problems:Deep Equilibrium Models versus Bilevel Learning

Variational regularization methods are commonly used to approximate solutions of inverse problems. In recent years, model-based variational regularization methods have often been replaced with data-driven ones such as the fields-of-expert model (Roth and Black, 2009). Training the parameters of such data-driven methods can be formulated as a bilevel optimization problem. In this paper, we compare the framework of bilevel learning for the training of data-driven variational regularization models with the novel framework of deep equilibrium models (Bai, Kolter, and Koltun, 2019) that has recently been introduced in the context of inverse problems (Gilton, Ongie, and Willett, 2021). We show that computing the lower-level optimization problem within the bilevel formulation with a fixed point iteration is a special case of the deep equilibrium framework. We compare both approaches computationally, with a variety of numerical examples for the inverse problems of denoising, inpainting and deconvolution

Regularization of Inverse Problems:Deep Equilibrium Models versus Bilevel Learning

Variational regularization methods are commonly used to approximate solutions of inverse problems. In recent years, model-based variational regularization methods have often been replaced with data-driven ones such as the fields-of-expert model (Roth and Black, 2009). Training the parameters of such data-driven methods can be formulated as a bilevel optimization problem. In this paper, we compare the framework of bilevel learning for the training of data-driven variational regularization models with the novel framework of deep equilibrium models (Bai, Kolter, and Koltun, 2019) that has recently been introduced in the context of inverse problems (Gilton, Ongie, and Willett, 2021). We show that computing the lower-level optimization problem within the bilevel formulation with a fixed point iteration is a special case of the deep equilibrium framework. We compare both approaches computationally, with a variety of numerical examples for the inverse problems of denoising, inpainting and deconvolution